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Data Science Python Course | Python with Data Science Course | Intellipaat

description
🔵 Intellipaat Data Science Course: https://intellipaat.com/data-science-architect-masters-program-training/ In this Data Science Python Course video, you will learn end-to-end about data science with Python along with the hands-on demo and interview preparation. So this python data science tutorial will help you learn various Python, Data Science, Python Numpy, Python Pandas, Python Matplotlib, and Machine Learning Algorithms to get you started in Data Science and excel in the technology. This is a must-watch session for everyone who wishes to learn Data Science and make a career in it. #DataSciencePythonCourse #PythonWithDataScienceCourse #PythonForDataScience #DataScienceWithPython #DataSciencePythonCourse #Intellipaat 00:00:00 - Data Science Python Course 00:06:44 - Introduction to Data Science 00:30:43 - What is Data Science? 00:42:16 - Types of Data Analytics 00:50:47 - Lifecycle of Data Science 01:11:33 - Quiz 01:11:57 - Introduction to Python 01:34:19 - Quiz 02:41:11 - Data Types in Python 04:04:01 - Why Python for Data Science? 04:19:09 - Python Packages for Data Science 04:39:35 - Introduction to Data Extraction 04:46:31 - Why Extract Data? 04:54:54 - Python for Data Extraction 05:18:48 - Importing and Analyzing the Dataset 06:30:38 - Introduction to Matplotlib 06:36:01 - Types of Plots 07:45:19 - Machine Learning around you 10:44:43 - Data Science Interview Questions 🔵 To subscribe to the Intellipaat channel & get regular updates on videos: http://bit.ly/Intellipaat 🔵 Watch complete Data Science tutorials here: https://www.youtube.com/watch?v=LRcIJHHESaY&list=PLVHgQku8Z934OCWXhq5YsfiMGvStaFB1i 🔵 Read the complete Data Science tutorial here: https://intellipaat.com/tutorial/data-science-tutorial/ 🔵. Interested to learn Data Science still more? Please check similar what is Data Science blog here: https://intellipaat.com/blog/what-is-data-science/ 🔵 Read the insightful blog on Python for data science: https://intellipaat.com/blog/python-for-data-science/ 🔵 Know various data science certifications in a detailed blog: https://intellipaat.com/blog/data-science-certification/ If you’ve enjoyed this Data Science Python Course video, Like us and Subscribe to our channel for more similar informative tutorials. Got any questions about python for the data science tutorial? Ask us in the comment section below. ---------------------------- Intellipaat Edge 1. 24*7 Lifetime Access & Support 2. Flexible Class Schedule 3. Job Assistance 4. Mentors with +14 yrs 5. Industry Oriented Courseware 6. Lifetime free Course Upgrade ------------------------------ 🔵 Why should you watch this Python for Data Science tutorial? You can learn Data Science much faster than any other technology and this Data Science tutorial helps you do just that. Data Science is one of the best technological advances that is finding increased applications for machine learning and in a lot of industry domains. We are offering the top Data Science tutorial to gain knowledge in Data Science. 🔵 Who should watch this Python for Data Science tutorial video? If you want to learn what is Data Science to become a Data Scientist then this Intellipaat Data Science tutorial is for you. The Intellipaat Data Science video is your first step to learn Data Science. Since this Data Science tutorial video can be taken by anybody, so if you are a beginner in technology then you can also enroll for Data Science training to take your skills to the next level. ------------------------------ 🔵 For more information: Call Our Course Advisors IND: +91-7022374614 US: 1-800-216-8930 Website: https://intellipaat.com/data-science-architect-masters-program-training/ Facebook: https://www.facebook.com/intellipaatonline LinkedIn: https://www.linkedin.com/company/intellipaat-software-solutions Twitter: https://twitter.com/Intellipaat Telegram : https://t.me/s/Learn_with_Intellipaat Instagram: https://www.instagram.com/intellipaat/

detail
{'title': 'Data Science Python Course | Python with Data Science Course | Intellipaat', 'heatmap': [], 'summary': "Course covers data science, python's role and relevance, programming fundamentals, operators, list operations, dictionaries, data analysis, ai tracking, machine learning, regression models, data visualization, supervised machine learning, linear regression, supermarket analysis, book recommendation, collaborative filtering, and predictive models, offering practical insights and examples for industry applications.", 'chapters': [{'end': 2256.979, 'segs': [{'end': 46.552, 'src': 'embed', 'start': 19.799, 'weight': 1, 'content': [{'end': 27.263, 'text': 'The size of the data is increasing every day, right? And earlier, whatever business decisions we used to take, that also has changed now.', 'start': 19.799, 'duration': 7.464}, {'end': 30.365, 'text': 'From the time when we used to discuss it with the stakeholders.', 'start': 27.844, 'duration': 2.521}, {'end': 36.308, 'text': "they used to come up with a decision based on their experiences, and that's how any business decision was done.", 'start': 30.365, 'duration': 5.943}, {'end': 46.552, 'text': 'But now it has become imperative to basically not only discuss, but to also have facts or data to back what your business decision is going to be.', 'start': 37.048, 'duration': 9.504}], 'summary': 'Data size is increasing, driving shift to data-driven business decisions.', 'duration': 26.753, 'max_score': 19.799, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI19799.jpg'}, {'end': 149.523, 'src': 'embed', 'start': 128.235, 'weight': 0, 'content': [{'end': 141.361, 'text': 'if you look at the job descriptions for data science and if you search for the data science profile on any job portal let it be Indeed or Knockry or anywhere else what you will notice is that most of the job descriptions ask for Python.', 'start': 128.235, 'duration': 13.126}, {'end': 148.163, 'text': "I would say around 60 to 70% of the job descriptions, they ask for Python as a skill set in the data scientists that they're looking for.", 'start': 141.761, 'duration': 6.402}, {'end': 149.523, 'text': 'And yes,', 'start': 148.823, 'duration': 0.7}], 'summary': '60-70% of data science job descriptions require python.', 'duration': 21.288, 'max_score': 128.235, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI128235.jpg'}, {'end': 889.787, 'src': 'embed', 'start': 864.574, 'weight': 2, 'content': [{'end': 869.702, 'text': 'right, applications of data science across different industries.', 'start': 864.574, 'duration': 5.128}, {'end': 873.383, 'text': "okay, and this is not exhaustive, right, it doesn't capture all the use cases.", 'start': 869.702, 'duration': 3.681}, {'end': 877.364, 'text': "it doesn't capture all the industries where you could potentially use data science.", 'start': 873.383, 'duration': 3.981}, {'end': 889.787, 'text': 'but i just want to give you a a glimpse of what are the possible industries where you could apply data science and what is the outcome that you can derive out of using data science.', 'start': 877.364, 'duration': 12.423}], 'summary': 'Data science applications span various industries, not exhaustive, providing valuable outcomes.', 'duration': 25.213, 'max_score': 864.574, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI864574.jpg'}, {'end': 1000.659, 'src': 'embed', 'start': 974.971, 'weight': 3, 'content': [{'end': 981.012, 'text': 'what is it that i need to improve for specific components, for specific segments of customers?', 'start': 974.971, 'duration': 6.041}, {'end': 983.153, 'text': 'that would help me retain my customers better.', 'start': 981.012, 'duration': 2.141}, {'end': 989.389, 'text': "okay, so that's what customer churn analytics right or churn prediction enables you to do right.", 'start': 984.705, 'duration': 4.684}, {'end': 997.897, 'text': 'it enables you to understand in advance who are the customers that are most likely to churn right, and it also enables, with their toolkit,', 'start': 989.389, 'duration': 8.508}, {'end': 1000.659, 'text': 'to know what is the reason why this customer is leaving right.', 'start': 997.897, 'duration': 2.762}], 'summary': 'Churn prediction analytics identifies at-risk customers, improving retention and understanding churn reasons.', 'duration': 25.688, 'max_score': 974.971, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI974971.jpg'}, {'end': 1463.143, 'src': 'embed', 'start': 1433.587, 'weight': 4, 'content': [{'end': 1441.111, 'text': 'Clinical trials, by the way, is one very popular use case of data science in the healthcare industry.', 'start': 1433.587, 'duration': 7.524}, {'end': 1448.675, 'text': "You can use statistics, you can use data science to predict the effectiveness of, let's say, a vaccine.", 'start': 1442.692, 'duration': 5.983}, {'end': 1453.098, 'text': 'Because of the recency of it.', 'start': 1451.297, 'duration': 1.801}, {'end': 1463.143, 'text': "I can say that let's say when India was coming or when any healthcare firm was to generate was basically building a vaccine.", 'start': 1453.098, 'duration': 10.045}], 'summary': 'Data science is used in clinical trials to predict vaccine effectiveness and generate healthcare solutions.', 'duration': 29.556, 'max_score': 1433.587, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI1433587.jpg'}, {'end': 1873.31, 'src': 'embed', 'start': 1845.651, 'weight': 5, 'content': [{'end': 1851.315, 'text': 'so, to start with, in the most easiest manner, if i have to define data science, data science is nothing.', 'start': 1845.651, 'duration': 5.664}, {'end': 1858.659, 'text': "okay, data science is nothing, but it's the science, okay, of applying mathematics to data.", 'start': 1851.315, 'duration': 7.344}, {'end': 1865.148, 'text': "okay, it's the science of applying mathematics to data so that you can make the data talk to you.", 'start': 1858.659, 'duration': 6.489}, {'end': 1873.31, 'text': 'okay, when i say you can make the data talk to you, it means that you enable interpretability of raw data.', 'start': 1865.148, 'duration': 8.162}], 'summary': 'Data science is the application of mathematics to enable interpretability of raw data.', 'duration': 27.659, 'max_score': 1845.651, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI1845651.jpg'}], 'start': 0.229, 'title': 'Data science and its applications', 'summary': 'Discusses the increasing importance of data science, with around 60-70% of job descriptions requiring python skills, the shift from traditional structured data to unstructured data, and the widespread applications of data science across diverse industries. it also explores marketing data science use cases, healthcare applications, and fraud detection, emphasizing the impact on customer retention rates and predicting vaccine effectiveness in clinical trials.', 'chapters': [{'end': 378.682, 'start': 0.229, 'title': 'Data science: hottest job & python skills', 'summary': 'Discusses the increasing importance of data science due to the exponential growth of data, the necessity of using data science for business decisions to stay competitive, and the importance of python skills for aspiring data scientists, with around 60-70% of job descriptions requiring python. the chapter also emphasizes the value of practical experience and projects in data science for career advancement.', 'duration': 378.453, 'highlights': ['The exponential growth of data has changed the way business decisions are made, with an increasing necessity to have data to back business decisions. Exponential increase in data size since 2005.', 'The necessity of using data science for business decisions to stay competitive, as decisions backed by data lead to higher chances of success. Competitive advantage of using data science for decision-making.', 'Around 60-70% of data science job descriptions ask for Python skills, indicating its importance for aspiring data scientists. Percentage of job descriptions requiring Python skills.', 'Emphasizing the value of practical experience and projects in data science for career advancement, with a focus on gaining relevant experience through real-life case studies and projects. Inclusion of real-life case studies and projects in the course.']}, {'end': 899.619, 'start': 379.143, 'title': 'Understanding data science and its applications', 'summary': 'Discusses the definition of data, its various formats, the shift from traditional structured data to unstructured data, and the exponential growth of data over the past two years, highlighting the importance of advanced analytical tools and algorithms in processing and drawing insights from unstructured data, and the widespread applications of data science across diverse industries.', 'duration': 520.476, 'highlights': ['Data generation in the last two years is equivalent to all data stored and generated before 2019, showcasing the exponential growth of data. The amount of data generated in the last two years is equivalent to all the data stored and generated before 2019, highlighting the exponential growth of data.', 'The importance of advanced analytical tools and algorithms in processing unstructured data for drawing meaningful insights. The need for complex and advanced analytical tools and algorithms to process and draw meaningful insights from huge unstructured data is emphasized.', 'The varied and widespread applications of data science across different industries and domains. Data science has innumerable use cases and is applied across various domains and industries, with popular use cases in marketing, manufacturing, insurance, banking, and healthcare.']}, {'end': 1402.276, 'start': 900.679, 'title': 'Marketing data science use cases', 'summary': 'Discusses the importance of customer churn prediction in marketing, highlighting how it enables businesses to identify at-risk customers and take proactive measures to improve retention, potentially leading to a significant impact on customer retention rates. it also explores the applications of machine learning in cross-selling and upselling, as well as the significance of digital marketing and sentiment analysis in leveraging social media platforms for targeted advertising and brand perception analysis.', 'duration': 501.597, 'highlights': ['Customer Churn Prediction Customer churn prediction enables businesses to proactively identify at-risk customers, understand the reasons for potential churn, and take targeted measures to improve retention, potentially leading to a significant impact on customer retention rates.', 'Cross-Selling and Upselling Machine learning can be used to predict the next best product for customers, leading to potential incremental sales through targeted recommendations. Additionally, upselling strategies aim to identify customers likely to upgrade to higher-value services, contributing to revenue generation for the organization.', 'Digital Marketing and Targeted Advertising Digital marketing leverages social media platforms for targeted ad campaigns, driving brand awareness and website traffic by efficiently reaching specific demographics, tapping into the vast audience available on these platforms.', 'Sentiment Analysis for Brand Perception Sentiment analysis utilizes social media reviews to gauge customer perception about a brand, enabling businesses to identify and address negative sentiments, as well as capitalize on positive sentiments to enhance future customer experiences and purchases.']}, {'end': 1752.081, 'start': 1402.276, 'title': 'Data science applications: marketing, healthcare, and fraud detection', 'summary': 'Discusses the diverse applications of data science, including its role in healthcare for predicting vaccine effectiveness through clinical trials and in fraud detection for credit card transactions to mitigate potential losses.', 'duration': 349.805, 'highlights': ['Data science in healthcare: Predicting vaccine effectiveness through clinical trials Clinical trials use data science and statistics to predict vaccine effectiveness, ensuring the sample is representative of the entire population, reducing risks of undisclosed side effects.', 'Fraud detection in credit card transactions: Utilizing machine learning models to identify fraudulent transactions Credit card companies use historical data and machine learning models to predict fraudulent transactions, reducing potential losses and ensuring secure transactions.', 'Marketing and social media automation: Leveraging analytics and data science for various use cases Data science is applied across marketing and social media automation for diverse analytics use cases and machine learning-driven automation.']}, {'end': 2256.979, 'start': 1753.422, 'title': 'Importance of data science', 'summary': "Discusses the importance and definition of data science, its applications across various industries, and the distinction between data scientist, business analyst, and business intelligence officer, emphasizing data science's role in predicting future events.", 'duration': 503.557, 'highlights': ['Data science helps in understanding data and identifying actionable insights, leading to improvements for businesses. Data science enables businesses to make informed decisions and develop strategies by identifying actionable insights, ultimately leading to business improvements.', 'Data science is defined as the application of mathematics to data, enabling the interpretation of raw data and the generation of hidden patterns and insights. Data science is defined as the application of mathematics to data, enabling the interpretation of raw data and the generation of hidden patterns and insights, which is crucial for making data talk and extracting valuable insights.', 'Data science is a blend of various tools, algorithms, and machine learning principles aimed at discovering hidden patterns from raw data. Data science is a blend of various tools, algorithms, and machine learning principles aimed at discovering hidden patterns and generating insights from raw data, ultimately uncovering patterns that are not visible to the naked eye.', 'Data scientists differ from business analysts and business intelligence officers in that they not only explain historical data but also use advanced machine learning algorithms to predict future events. Data scientists differ from business analysts and business intelligence officers by not only explaining historical data but also using advanced machine learning algorithms to predict future events, showcasing the predictive aspect of data science.']}], 'duration': 2256.75, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI229.jpg', 'highlights': ['Around 60-70% of data science job descriptions ask for Python skills, indicating its importance for aspiring data scientists.', 'The exponential growth of data has changed the way business decisions are made, with an increasing necessity to have data to back business decisions.', 'The varied and widespread applications of data science across different industries and domains.', 'Customer churn prediction enables businesses to proactively identify at-risk customers, understand the reasons for potential churn, and take targeted measures to improve retention.', 'Data science in healthcare: Predicting vaccine effectiveness through clinical trials.', 'Data science is defined as the application of mathematics to data, enabling the interpretation of raw data and the generation of hidden patterns and insights.']}, {'end': 4276.379, 'segs': [{'end': 2943.396, 'src': 'embed', 'start': 2915.81, 'weight': 1, 'content': [{'end': 2926.914, 'text': 'okay, so prescriptive analytics is the fourth domain of analytics where, instead of predicting outcomes right, you use concepts of a diagnostic,', 'start': 2915.81, 'duration': 11.104}, {'end': 2933.417, 'text': 'descriptive and productive analytics to prescribe outcomes, to come up with recommendations.', 'start': 2926.914, 'duration': 6.503}, {'end': 2943.396, 'text': 'okay, a very good example of this is recommendation engine, right, wherein you use machine learning not to influence a business, influence an insight,', 'start': 2933.417, 'duration': 9.979}], 'summary': 'Prescriptive analytics is the fourth domain, leveraging diagnostic, descriptive, and predictive analytics to prescribe outcomes and provide recommendations.', 'duration': 27.586, 'max_score': 2915.81, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI2915810.jpg'}, {'end': 3012.574, 'src': 'embed', 'start': 2986.607, 'weight': 0, 'content': [{'end': 2993.17, 'text': "so our data science primarily works on predictive and prescriptive analytics, right, and let's say,", 'start': 2986.607, 'duration': 6.563}, {'end': 2998.392, 'text': '20 to 25 percent of their time also goes in performing descriptive analytics and diagnostic analytics right in the same manner.', 'start': 2993.17, 'duration': 5.222}, {'end': 3003.41, 'text': 'in the same manner, a data analyst would be primarily focused on diagnostic analytics.', 'start': 2999.469, 'duration': 3.941}, {'end': 3010.033, 'text': 'right, this is where a data analyst 80 percent time, 80 percent time of data analysts would be concentrated.', 'start': 3003.41, 'duration': 6.623}, {'end': 3012.574, 'text': 'then the rest would be concentrated across other domains, right.', 'start': 3010.033, 'duration': 2.541}], 'summary': 'Data scientists focus 20-25% on descriptive and diagnostic analytics, while data analysts spend 80% of their time on diagnostic analytics.', 'duration': 25.967, 'max_score': 2986.607, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI2986607.jpg'}, {'end': 3143.105, 'src': 'embed', 'start': 3120.262, 'weight': 2, 'content': [{'end': 3127.964, 'text': 'right, but in most cases you would end up performing each of these stages that are that are being referred here.', 'start': 3120.262, 'duration': 7.702}, {'end': 3133.546, 'text': 'okay, so we start with data acquisition, we go to data pre-processing, we go to model bending,', 'start': 3127.964, 'duration': 5.582}, {'end': 3136.407, 'text': 'we go to pattern evaluation and then we go to knowledge representation.', 'start': 3133.546, 'duration': 2.861}, {'end': 3137.807, 'text': 'these are the five steps.', 'start': 3136.407, 'duration': 1.4}, {'end': 3143.105, 'text': 'right, five stages which are, which are, uh, which are common.', 'start': 3137.807, 'duration': 5.298}], 'summary': '5 stages: data acquisition, pre-processing, model building, pattern evaluation, knowledge representation.', 'duration': 22.843, 'max_score': 3120.262, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI3120262.jpg'}, {'end': 3302.866, 'src': 'embed', 'start': 3279.602, 'weight': 3, 'content': [{'end': 3286.427, 'text': "uh, you're trying to build a model right which could predict in advance whether a customer is likely to churn or not.", 'start': 3279.602, 'duration': 6.825}, {'end': 3296.365, 'text': 'okay, now, even before you start collecting data, it is important for you to do this step right, because only if you know what are the pat,', 'start': 3286.427, 'duration': 9.938}, {'end': 3302.866, 'text': 'what are the parameters that could have an impact on your, on your final output, which is data which is customer churn,', 'start': 3296.365, 'duration': 6.501}], 'summary': 'Build a model to predict customer churn, emphasizing the importance of identifying key parameters for data collection.', 'duration': 23.264, 'max_score': 3279.602, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI3279602.jpg'}, {'end': 3792.321, 'src': 'embed', 'start': 3767.051, 'weight': 6, 'content': [{'end': 3773.153, 'text': "so, basically, what you'll do is there are, there are various machine learning algorithms that you could do, that you could use.", 'start': 3767.051, 'duration': 6.102}, {'end': 3778.598, 'text': 'okay, there are various machine learning techniques that you can use for a particular type of business problem.', 'start': 3773.153, 'duration': 5.445}, {'end': 3785.019, 'text': "right for, let's say, for building a predictive model that could predict customer churn, you can use various techniques.", 'start': 3778.598, 'duration': 6.421}, {'end': 3786.539, 'text': 'right, you can use decision trees.', 'start': 3785.019, 'duration': 1.52}, {'end': 3789.12, 'text': 'you can use uh, random forest.', 'start': 3786.539, 'duration': 2.581}, {'end': 3790.9, 'text': 'you can use gradient boosting.', 'start': 3789.12, 'duration': 1.78}, {'end': 3792.321, 'text': 'you can use logistic regression.', 'start': 3790.9, 'duration': 1.421}], 'summary': 'Various machine learning algorithms can be used to build predictive models for customer churn, including decision trees, random forest, gradient boosting, and logistic regression.', 'duration': 25.27, 'max_score': 3767.051, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI3767051.jpg'}, {'end': 3985.903, 'src': 'embed', 'start': 3960.639, 'weight': 5, 'content': [{'end': 3966.043, 'text': 'what would the business do if they do not know what, what they need to do to to stop those customers from churning right?', 'start': 3960.639, 'duration': 5.404}, {'end': 3978.095, 'text': 'unless they know what is driving that that customer to leave the the organization, it is very difficult for business to come up with incentives,', 'start': 3966.943, 'duration': 11.152}, {'end': 3980.017, 'text': 'come up with interventions right.', 'start': 3978.095, 'duration': 1.922}, {'end': 3985.903, 'text': 'so in this step, what we do is we try to correlate the results of the model.', 'start': 3980.017, 'duration': 5.886}], 'summary': 'Identify customer churn drivers to develop effective interventions and incentives.', 'duration': 25.264, 'max_score': 3960.639, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI3960639.jpg'}, {'end': 4100.559, 'src': 'embed', 'start': 4073.075, 'weight': 7, 'content': [{'end': 4077.416, 'text': 'so, basically, in this step, what you do is you interpret the findings of your model.', 'start': 4073.075, 'duration': 4.341}, {'end': 4085.912, 'text': 'you try to correlate it with your, with your, with your data, with your various variables, to make the recommendations like,', 'start': 4077.416, 'duration': 8.496}, {'end': 4091.074, 'text': 'to come up with recommendations, to come up with outputs which are actionable for the business to implement.', 'start': 4085.912, 'duration': 5.162}, {'end': 4093.636, 'text': 'okay, that comes under pattern evaluation, okay.', 'start': 4091.074, 'duration': 2.562}, {'end': 4100.559, 'text': 'and finally, finally, the last step of your, of your data science engagement, is called knowledge representation.', 'start': 4093.636, 'duration': 6.923}], 'summary': 'Interpret model findings to make actionable recommendations. concludes with knowledge representation.', 'duration': 27.484, 'max_score': 4073.075, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI4073075.jpg'}], 'start': 2257.539, 'title': 'Data science and analytics', 'summary': 'Explores the differences between data analyst and data scientist, types of data analytics, emphasizing 20% on descriptive and diagnostic analytics, and 80% on predictive and prescriptive analytics. it also discusses the data science project lifecycle and emphasizes the importance of scoping business problems and the process of customer churn analysis. finally, it delves into model evaluation and knowledge representation in data science engagements.', 'chapters': [{'end': 2448.404, 'start': 2257.539, 'title': 'Data analyst vs data scientist', 'summary': 'Explains the differences between a data analyst and a data scientist, highlighting that a data scientist uses historical data and independent variables to build algorithms and make predictions for the future, while a data analyst focuses on analyzing previous data to find hindsight and insight for descriptive business trends.', 'duration': 190.865, 'highlights': ['A data scientist uses historical data and independent variables to build algorithms and make predictions for the future, while a data analyst focuses on analyzing previous data to find hindsight and insight for descriptive business trends. N/A', 'Data scientists try to predict the future outcomes with the aim of making informed decisions, whereas data analysts analyze past or current data and use that to predict the future outcomes. N/A', 'Data scientists use machine learning algorithms to make predictions for the future based on historical data and independent variables. N/A', 'Data analysts enable the analysis of previous data to find hindsight and insight to describe business trends. N/A']}, {'end': 2761.123, 'start': 2450.445, 'title': 'Understanding data science and types of data analytics', 'summary': "Explains how data science answers open-ended questions and discusses the four major domains of data analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics, with a focus on understanding the 'why' and 'how' of events through statistical analysis.", 'duration': 310.678, 'highlights': ["Understanding Data Science Data science focuses on answering open-ended questions about events by examining the 'why,' 'what,' and 'how,' and encompasses multiple domains and tools such as visualization, data manipulation, statistical analysis, and machine learning model building.", 'Domains of Data Analytics The four major domains of data analytics are descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics, with a focus on understanding historical events, analyzing root causes of trends, and making predictions based on data analysis.', "Descriptive Analytics Descriptive analytics involves describing historical events, trends, and features through reporting, dashboarding, and focuses on the 'what' of past data, commonly used by business intelligence officers.", "Diagnostic Analytics Diagnostic analytics involves deep diving into specific events or patterns to understand the 'why' and 'how' through statistical and exploratory data analysis, and requires utilizing descriptive analytics to validate or disprove hypotheses."]}, {'end': 2986.607, 'start': 2761.143, 'title': 'Types of analytics and their applications', 'summary': 'Discusses the four domains of analytics, including diagnostic analytics, predictive analytics, and prescriptive analytics, and emphasizes the primary focus of data scientists on predictive analytics, with a 20% emphasis on descriptive and diagnostic analytics, and an 80% focus on predictive and prescriptive analytics.', 'duration': 225.464, 'highlights': ['Data scientists should primarily focus on predictive analytics, with 80% of their time dedicated to it and 20% on descriptive and diagnostic analytics. 80% time for predictive analytics, 20% time for descriptive and diagnostic analytics', "Prescriptive analytics utilizes concepts of diagnostic, descriptive, and predictive analytics to come up with recommendations, such as recommendation engines like Netflix's, which prescribes movie recommendations to users. Prescriptive analytics involves recommending outcomes to end users, e.g., Netflix recommendation engine.", 'Predictive analytics involves using historical data and machine learning techniques to predict future outcomes, and is the primary focus of data scientists. Predictive analytics involves using historical data and machine learning techniques.']}, {'end': 3230.325, 'start': 2986.607, 'title': 'Data science project lifecycle', 'summary': 'Discusses the focus areas and time allocation for data scientists and analysts, and then delves into the five stages of a data science project, emphasizing that 85-90% of projects go through these stages, starting with data acquisition and concluding with knowledge representation.', 'duration': 243.718, 'highlights': ['Data scientists primarily work on predictive and prescriptive analytics, with 20-25% of their time also spent on descriptive and diagnostic analytics. Data scientists primarily focus on predictive and prescriptive analytics, dedicating 20-25% of their time to descriptive and diagnostic analytics.', 'Data analysts focus 80% of their time on diagnostic analytics, with the rest concentrated on other domains and skill development. Data analysts allocate 80% of their time to diagnostic analytics and the remaining time to other domains and skill development.', 'The five stages of a data science project include data acquisition, data pre-processing, model building, pattern evaluation, and knowledge representation, which are common and sequential. The five common and sequential stages of a data science project are data acquisition, data pre-processing, model building, pattern evaluation, and knowledge representation.']}, {'end': 3426.194, 'start': 3230.325, 'title': 'Scoping business problems for data collection', 'summary': 'Emphasizes the importance of scoping business problems before collecting data, highlighting the need to identify parameters impacting the problem, such as cost of services, demographics, and internet service quality.', 'duration': 195.869, 'highlights': ['Identifying Parameters Impacting Business Problems Before collecting data, it is crucial to identify parameters that could impact the final output, such as customer churn, including cost of services, demographics, and internet service quality.', 'Importance of Hypothesis Generation The process involves generating multiple hypotheses to address the business problem, such as identifying variables like cost of services, demographics, and internet service quality as potential factors impacting customer churn.', 'Significance of Scoping in Data Collection Scoping the business problem is essential to determine parameters that could impact the problem, aiding in the identification of factors like cost of services, demographics, and internet service quality before initiating data collection.']}, {'end': 4073.075, 'start': 3426.194, 'title': 'Customer churn analysis process', 'summary': 'Details the process of customer churn analysis, including scoping the problem, data acquisition, data pre-processing, model building, and pattern evaluation, highlighting the importance of understanding the reasons for customer churn to drive business interventions and the use of various machine learning algorithms for model building.', 'duration': 646.881, 'highlights': ['The chapter details the process of customer churn analysis, including scoping the problem, data acquisition, data pre-processing, model building, and pattern evaluation. It covers the key steps involved in analyzing customer churn, providing a comprehensive overview of the entire process.', 'Understanding the reasons for customer churn is emphasized to drive business interventions. Emphasizes the importance of correlating model results with independent variables to understand the reasons for customer churn, enabling targeted interventions to retain customers.', 'The use of various machine learning algorithms for model building is highlighted. Discusses the use of multiple machine learning algorithms such as decision trees, random forest, gradient boosting, logistic regression, and support vector machines for building predictive models to address customer churn.', 'Detailed explanation of data pre-processing and its importance in preparing data for mathematical modeling. Explains the process of data pre-processing, including merging data from various sources, ensuring data quality, and data completeness, highlighting its significance in preparing data for mathematical modeling.', 'The importance of pattern evaluation in interpreting model results and driving business strategies is outlined. Emphasizes the significance of evaluating patterns associated with model predictions to understand the reasons for customer churn and retention, enabling the formulation of targeted business strategies.']}, {'end': 4276.379, 'start': 4073.075, 'title': 'Data science model evaluation', 'summary': 'Discusses the final steps of a data science engagement, including pattern evaluation and knowledge representation, which involves presenting model outputs and recommendations to the leadership, potentially leading to model productionization and integration with the database.', 'duration': 203.304, 'highlights': ['Pattern Evaluation This step involves correlating model findings with data and variables to make actionable recommendations for the business to implement, potentially leading to a decision on model productionization and retention of customers.', "Knowledge Representation Involves presenting model outputs and learnings to an audience, including creating visualizations and graphs to simplify technical terminologies for the leadership's consumption, potentially leading to model productionization and integration with the database."]}], 'duration': 2018.84, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI2257539.jpg', 'highlights': ['Data scientists primarily focus on predictive and prescriptive analytics, dedicating 80% of their time to it and 20% to descriptive and diagnostic analytics.', 'Prescriptive analytics involves recommending outcomes to end users, e.g., Netflix recommendation engine.', 'The five common and sequential stages of a data science project are data acquisition, data pre-processing, model building, pattern evaluation, and knowledge representation.', 'Before collecting data, it is crucial to identify parameters that could impact the final output, such as customer churn, including cost of services, demographics, and internet service quality.', 'The chapter details the process of customer churn analysis, including scoping the problem, data acquisition, data pre-processing, model building, and pattern evaluation.', 'Emphasizes the importance of correlating model results with independent variables to understand the reasons for customer churn, enabling targeted interventions to retain customers.', 'The use of various machine learning algorithms for model building is highlighted.', 'Pattern Evaluation involves correlating model findings with data and variables to make actionable recommendations for the business to implement, potentially leading to a decision on model productionization and retention of customers.']}, {'end': 5672.872, 'segs': [{'end': 4769.297, 'src': 'embed', 'start': 4742.796, 'weight': 0, 'content': [{'end': 4748.221, 'text': 'And what it does is it makes life very, very easy for people who are working on the programming language.', 'start': 4742.796, 'duration': 5.425}, {'end': 4754.987, 'text': 'So libraries and packages are basically reusable pieces of code that have been put together by people.', 'start': 4748.681, 'duration': 6.306}, {'end': 4763.716, 'text': "And because it's an open source world, Everybody believes in sharing and hence that's how a lot of interesting libraries have come in.", 'start': 4756.829, 'duration': 6.887}, {'end': 4769.297, 'text': "I'm going to talk about these libraries in a bit, but it'll make it easier for you to understand.", 'start': 4764.396, 'duration': 4.901}], 'summary': 'Open source libraries make programming easier for developers.', 'duration': 26.501, 'max_score': 4742.796, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI4742796.jpg'}, {'end': 4992.834, 'src': 'embed', 'start': 4968.202, 'weight': 1, 'content': [{'end': 4980.29, 'text': 'But I can comfortably say that almost 95%, 95 to 99% of people who work on data science and deep learning today work with Python.', 'start': 4968.202, 'duration': 12.088}, {'end': 4982.329, 'text': "There's no question about it.", 'start': 4981.128, 'duration': 1.201}, {'end': 4983.429, 'text': "There's no two ways about it.", 'start': 4982.349, 'duration': 1.08}, {'end': 4991.794, 'text': 'Python has become more or less the eventual tool of choice for people who are working in the field of data science, statistics,', 'start': 4983.489, 'duration': 8.305}, {'end': 4992.834, 'text': 'machine learning and so on.', 'start': 4991.794, 'duration': 1.04}], 'summary': '95-99% of data scientists and deep learning professionals work with python, making it the tool of choice for this field.', 'duration': 24.632, 'max_score': 4968.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI4968202.jpg'}, {'end': 5265.251, 'src': 'embed', 'start': 5234.14, 'weight': 3, 'content': [{'end': 5235.981, 'text': 'The basics of Python will remain the same.', 'start': 5234.14, 'duration': 1.841}, {'end': 5242.545, 'text': "So what we're first learning is this, right? We're going to learn the basics of Python and how to basically interact with Python.", 'start': 5236.401, 'duration': 6.144}, {'end': 5249.347, 'text': 'Once we tick this off, then we have the flexibility to progress in whichever direction you want based on your interest.', 'start': 5243.246, 'duration': 6.101}, {'end': 5252.788, 'text': 'What that means is the ones who signed up for the data science course.', 'start': 5250.008, 'duration': 2.78}, {'end': 5259.45, 'text': "So what we're going to do is we're going to first learn these basics and then we're going to learn the data science specific libraries of Python.", 'start': 5252.808, 'duration': 6.642}, {'end': 5265.251, 'text': "Similarly, the ones who signed up for, let's say, any kind of cloud based courses, we're going to learn Python basics.", 'start': 5259.97, 'duration': 5.281}], 'summary': 'Learn python basics for data science and cloud courses.', 'duration': 31.111, 'max_score': 5234.14, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI5234140.jpg'}, {'end': 5593.218, 'src': 'embed', 'start': 5566.898, 'weight': 2, 'content': [{'end': 5573.46, 'text': 'And as a result of that what it does is it eliminates this additional hassle of installing each of these libraries individually.', 'start': 5566.898, 'duration': 6.562}, {'end': 5579.923, 'text': 'That is one of the primary reasons why people do not manually install Python and manually install libraries.', 'start': 5574.561, 'duration': 5.362}, {'end': 5586.393, 'text': "People prefer, let's say, an Anaconda distribution of Python because it gives you a host of libraries along with Python.", 'start': 5580.469, 'duration': 5.924}, {'end': 5593.218, 'text': 'And that is one of the reasons why you were asked to install an Anaconda distribution of Python.', 'start': 5587.774, 'duration': 5.444}], 'summary': 'Installing anaconda distribution of python provides a host of libraries, eliminating the hassle of manual installations.', 'duration': 26.32, 'max_score': 5566.898, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI5566898.jpg'}], 'start': 4276.379, 'title': "Python's role and relevance", 'summary': "Explores python's features, popularity, and relevance in data science, deep learning, and other domains, with 95-99% usage in data science, efficiency, and career opportunities in the software industry, as well as the benefits of anaconda distribution over manual installation.", 'chapters': [{'end': 4783.66, 'start': 4276.379, 'title': 'Python for data science', 'summary': 'Discusses the key features and popularity of python as a programming language, including its role in combining scripting language simplicity with object-oriented programming complexity, its open-source nature, and its rich community support, leading to its widespread adoption in data science and other domains.', 'duration': 507.281, 'highlights': ["Python is popular due to its ability to combine the simplicity of scripting languages with the complexity of object-oriented programming, making it an ubiquitous tool for a variety of applications. Python's popularity is attributed to its unique combination of scripting language simplicity and object-oriented programming complexity, making it a versatile tool for various applications.", "Python's open-source nature has contributed to its popularity and widespread adoption, leading to the development of numerous libraries and packages by the community. The open-source nature of Python has led to the development of a rich ecosystem of libraries and packages by the community, contributing to its widespread adoption.", 'Community support has resulted in the development of various libraries, frameworks, and tools for Python, particularly in the field of data science, contributing to its ease of use and popularity. The community support for Python has led to the development of numerous libraries, frameworks, and tools, particularly in data science, enhancing its ease of use and popularity.']}, {'end': 5137.782, 'start': 4784.464, 'title': "Python's growing popularity and relevance", 'summary': 'Discusses the growing popularity and relevance of python, highlighting its reliability, efficiency, and career opportunities in the software industry, with 95-99% usage in data science and deep learning, and its increasing relevance in cloud and devops.', 'duration': 353.318, 'highlights': ['Python is used by 95-99% of professionals in data science and deep learning, making it the eventual tool of choice in these fields.', "Python's popularity has led to a spike in career opportunities, with a growing demand for Python programmers in the software industry.", 'Python is becoming increasingly relevant in cloud and DevOps, with many cloud frameworks and SDKs being based on Python, allowing for resource deployment, API building, and interaction with cloud resources.', "Python's reliability, efficiency, and ease of use make it a valuable tool for web application development, desktop application building, and various other software industry applications."]}, {'end': 5423.011, 'start': 5138.322, 'title': 'Python basics and applications', 'summary': 'Discusses the importance of learning the basics of python, which serves as the foundation for various applications such as data science, cloud development, and web applications, as well as its widespread use across companies, including its combination with other tools.', 'duration': 284.689, 'highlights': ['Python basics serve as the foundation for various applications such as data science, cloud development, and web applications. Learning the basics of Python is essential as it forms the foundation for diverse applications, including data science, cloud-based development, and web applications.', 'Python is widely used across companies and is often combined with other tools in their technology stack. Python is a preferred tool in many companies and is widely used in combination with other tools in their technology stack, such as at Google and Microsoft.', 'Python is a common tool in the technology stack of many companies, although some still prefer Java for certain tasks. While Python is widely used, some companies still prefer using Java for specific tasks, such as building complex web applications, due to performance reasons.']}, {'end': 5672.872, 'start': 5423.011, 'title': 'Python distribution: anaconda vs manual installation', 'summary': 'Discusses the benefits of using anaconda distribution over manual installation in python, highlighting the ease of accessing pre-installed libraries and eliminating the hassle of individual library installations, ultimately promoting anaconda as a preferred choice for data science activities and python distributions.', 'duration': 249.861, 'highlights': ['Anaconda distribution eliminates the hassle of manually installing libraries in Python by providing pre-installed libraries along with the Python installation, making it a preferred choice for data science activities. Pre-installed libraries, ease of access, preferred choice for data science activities.', 'Anaconda and Miniconda are popular Python distributions known for providing a host of libraries along with the Python installation, thus simplifying the process of accessing and utilizing various packages. Popular Python distributions, simplified package access and utilization.', 'Individual library installations in Python come with the complexity of managing dependencies, whereas Anaconda distribution streamlines this process by offering a comprehensive package manager and environment manager. Complexity of managing dependencies, comprehensive package manager and environment manager in Anaconda.']}], 'duration': 1396.493, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI4276379.jpg', 'highlights': ["Python's open-source nature has led to the development of a rich ecosystem of libraries and packages by the community, contributing to its widespread adoption.", 'Python is used by 95-99% of professionals in data science and deep learning, making it the eventual tool of choice in these fields.', 'Anaconda distribution eliminates the hassle of manually installing libraries in Python by providing pre-installed libraries along with the Python installation, making it a preferred choice for data science activities.', 'Python basics serve as the foundation for various applications such as data science, cloud development, and web applications.']}, {'end': 8037.974, 'segs': [{'end': 5701.575, 'src': 'embed', 'start': 5672.872, 'weight': 0, 'content': [{'end': 5679.116, 'text': 'these packages have all come from pi pi, python package index.', 'start': 5672.872, 'duration': 6.244}, {'end': 5684.52, 'text': 'on a normal day, you would have manually had to install python and manually install pi pi, but because over a period of time,', 'start': 5679.116, 'duration': 5.404}, {'end': 5689.343, 'text': 'people have realized that there are certain combination of packages that people more often use.', 'start': 5684.52, 'duration': 4.823}, {'end': 5694.226, 'text': "hence all of this have been bundled together and they're they're providing this to us for free.", 'start': 5689.343, 'duration': 4.883}, {'end': 5696.514, 'text': 'is an anaconda distribution?', 'start': 5694.833, 'duration': 1.681}, {'end': 5701.575, 'text': 'okay? so that is one of the primary things that you will have to understand now.', 'start': 5696.514, 'duration': 5.061}], 'summary': 'Packages from python package index are bundled in anaconda distribution for ease of use.', 'duration': 28.703, 'max_score': 5672.872, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI5672872.jpg'}, {'end': 5856.41, 'src': 'embed', 'start': 5830.408, 'weight': 1, 'content': [{'end': 5836.473, 'text': 'Jupyter Notebooks is something that I of course do as well for any kind of quick and dirty analysis that I prefer to do.', 'start': 5830.408, 'duration': 6.065}, {'end': 5838.314, 'text': 'I prefer Jupyter Notebooks as well.', 'start': 5836.873, 'duration': 1.441}, {'end': 5843.787, 'text': "The first and the most important thing, of course, I'll first show you how how Jupyter Notebooks looks like.", 'start': 5838.554, 'duration': 5.233}, {'end': 5849.848, 'text': 'So what you can do is you will also have something called as an Anaconda Navigator.', 'start': 5844.147, 'duration': 5.701}, {'end': 5851.909, 'text': 'You can just go here.', 'start': 5850.909, 'duration': 1}, {'end': 5856.41, 'text': "If you open Anaconda Navigator, it'll take a couple of seconds for it to open.", 'start': 5852.849, 'duration': 3.561}], 'summary': 'Prefers using jupyter notebooks for quick analysis and utilizes anaconda navigator for it.', 'duration': 26.002, 'max_score': 5830.408, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI5830408.jpg'}, {'end': 6589.309, 'src': 'embed', 'start': 6563.609, 'weight': 2, 'content': [{'end': 6568.353, 'text': "x is a variable and i've assigned a value 10 to this particular variable.", 'start': 6563.609, 'duration': 4.744}, {'end': 6580.102, 'text': 'and what happens is your python stores this value of x in a particular memory location, so it stores the value of 10 as a memory location, basically.', 'start': 6568.353, 'duration': 11.749}, {'end': 6583.327, 'text': 'So that is what we mean by a Python variable.', 'start': 6580.666, 'duration': 2.661}, {'end': 6585.287, 'text': 'So X becomes a variable in this particular case.', 'start': 6583.387, 'duration': 1.9}, {'end': 6589.309, 'text': 'Simple ways, of course, as X is equal to 10, A is equal to 10 and so on and so forth.', 'start': 6585.567, 'duration': 3.742}], 'summary': 'Python stores value 10 as memory location for variable x.', 'duration': 25.7, 'max_score': 6563.609, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI6563609.jpg'}, {'end': 6943.09, 'src': 'embed', 'start': 6915.341, 'weight': 3, 'content': [{'end': 6919.364, 'text': 'this is a common practice across any programming language.', 'start': 6915.341, 'duration': 4.023}, {'end': 6924.588, 'text': 'as identifiers, rather, do not use keywords as identifiers.', 'start': 6919.364, 'duration': 5.224}, {'end': 6927.29, 'text': "it's a good practice across any programming language.", 'start': 6924.588, 'duration': 2.702}, {'end': 6929.512, 'text': "don't use things like these becomes very confusing.", 'start': 6927.29, 'duration': 2.222}, {'end': 6931.193, 'text': 'so these are certain keywords.', 'start': 6929.512, 'duration': 1.681}, {'end': 6932.374, 'text': 'these are not the only keywords.', 'start': 6931.193, 'duration': 1.181}, {'end': 6934.456, 'text': 'there are ton of other keywords as well.', 'start': 6932.374, 'duration': 2.082}, {'end': 6938.459, 'text': "but we'll know more and more keywords as we go along.", 'start': 6934.456, 'duration': 4.003}, {'end': 6943.09, 'text': 'okay, identifiers there are certain naming.', 'start': 6938.459, 'duration': 4.631}], 'summary': 'Avoid using keywords as identifiers in programming to prevent confusion and adhere to best practices.', 'duration': 27.749, 'max_score': 6915.341, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI6915341.jpg'}, {'end': 7487.298, 'src': 'embed', 'start': 7463.112, 'weight': 4, 'content': [{'end': 7470.238, 'text': "is that you don't have to declare the type of your variable upfront before assigning a value to it.", 'start': 7463.112, 'duration': 7.126}, {'end': 7478.311, 'text': 'you can automatically assign the value and then the programming language will take care of assigning it the right data type as it goes along.', 'start': 7470.238, 'duration': 8.073}, {'end': 7480.512, 'text': 'okay, just a quick info, guys.', 'start': 7478.311, 'duration': 2.201}, {'end': 7483.415, 'text': 'test your knowledge of python by answering this question.', 'start': 7480.512, 'duration': 2.903}, {'end': 7487.298, 'text': 'what do we use to define a block of code in python language?', 'start': 7483.415, 'duration': 3.883}], 'summary': 'In python, variables are dynamically typed, and code blocks are defined using indentation.', 'duration': 24.186, 'max_score': 7463.112, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI7463112.jpg'}, {'end': 7561.657, 'src': 'embed', 'start': 7522.639, 'weight': 7, 'content': [{'end': 7525.501, 'text': 'And if I say type of X, of course, this is a string.', 'start': 7522.639, 'duration': 2.862}, {'end': 7531.406, 'text': "But if let's say I say X is equal to integer of X.", 'start': 7526.522, 'duration': 4.884}, {'end': 7537.907, 'text': 'Now, what am I doing here? This is converting.', 'start': 7533.285, 'duration': 4.622}, {'end': 7543.929, 'text': 'Any value of X.', 'start': 7541.128, 'duration': 2.801}, {'end': 7545.09, 'text': 'Into an integer.', 'start': 7543.929, 'duration': 1.161}, {'end': 7552.653, 'text': 'This is essentially rather than converting, I think we should call it coercing.', 'start': 7548.251, 'duration': 4.402}, {'end': 7554.674, 'text': 'We are forcefully doing this.', 'start': 7553.353, 'duration': 1.321}, {'end': 7561.657, 'text': 'This will comfortably do it because X tried to convert X into an integer.', 'start': 7555.995, 'duration': 5.662}], 'summary': 'Explaining the process of coercing a value into an integer using x.', 'duration': 39.018, 'max_score': 7522.639, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI7522639.jpg'}, {'end': 7790.749, 'src': 'embed', 'start': 7758.216, 'weight': 5, 'content': [{'end': 7763.397, 'text': 'One is a integer literal float, and we spoke about string.', 'start': 7758.216, 'duration': 5.181}, {'end': 7764.938, 'text': 'There are two more.', 'start': 7764.198, 'duration': 0.74}, {'end': 7774.501, 'text': "One is, let's say, your Boolean literals, and the other is Boolean literals, and the other is special literals.", 'start': 7765.918, 'duration': 8.583}, {'end': 7777.043, 'text': 'these are all literals.', 'start': 7775.882, 'duration': 1.161}, {'end': 7780.344, 'text': 'let me call these as literals.', 'start': 7777.043, 'duration': 3.301}, {'end': 7790.749, 'text': "okay. so when i talk about boolean literals, if let's say x is equal to true now if you observe true is a keyword true is essentially a keyword.", 'start': 7780.344, 'duration': 10.405}], 'summary': "Discussion on different types of literals including integers, floats, strings, and boolean, with an emphasis on the keyword 'true'.", 'duration': 32.533, 'max_score': 7758.216, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI7758216.jpg'}, {'end': 7961.976, 'src': 'embed', 'start': 7937.747, 'weight': 8, 'content': [{'end': 7944.57, 'text': 'So as I said, Jupyter notebooks are also quite useful for doing some kind of dashboarding or doing some kind of quick presentations.', 'start': 7937.747, 'duration': 6.823}, {'end': 7946.771, 'text': 'Right So what I can do.', 'start': 7945.27, 'duration': 1.501}, {'end': 7952.015, 'text': 'is I can simply convert this into a Markdown cell by hitting an M.', 'start': 7947.654, 'duration': 4.361}, {'end': 7953.575, 'text': 'If you see this here, I can select the cell.', 'start': 7952.015, 'duration': 1.56}, {'end': 7961.976, 'text': 'I can either call it code where I can write the actual code or I can select Markdown and I can use HTML tags.', 'start': 7955.115, 'duration': 6.861}], 'summary': 'Jupyter notebooks useful for dashboarding and quick presentations. can convert to markdown with html tags.', 'duration': 24.229, 'max_score': 7937.747, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI7937747.jpg'}], 'start': 5672.872, 'title': 'Python programming fundamentals', 'summary': 'Provides an overview of anaconda distribution and python ides, emphasizes setting up the python environment and working with jupyter notebooks, explains python tokens, identifiers, literals, and data types, and introduces markdown cells for creating headings in jupyter notebooks.', 'chapters': [{'end': 6275.256, 'start': 5672.872, 'title': 'Python ides and anaconda distribution overview', 'summary': 'Covers the overview of anaconda distribution and python ides, emphasizing the convenience of bundled packages from pypi, the availability of popular python ides like jupyter notebooks and pycharm, and the functionality of jupyter notebooks for quick ad hoc coding and collaboration.', 'duration': 602.384, 'highlights': ['Anaconda distribution provides bundled packages from PyPI for convenience Over a period of time, certain combinations of packages have been bundled together and provided for free through the Anaconda distribution, eliminating the manual installation of Python and PyPI packages.', 'Functionality of popular Python IDEs like Jupyter notebooks and PyCharm Jupyter notebooks are suitable for quick ad hoc coding and collaboration, while PyCharm and VS Code are preferred in enterprise environments for building production-grade code.', 'Overview of Jupyter notebooks and their features Jupyter notebooks offer a user-friendly UI for code writing, dashboarding, and collaboration, making it particularly useful for quick and dirty analysis. The tool also supports Julia, Python, and R, and can be launched via Anaconda Navigator.']}, {'end': 6686.839, 'start': 6276.696, 'title': 'Python environment and jupyter notebooks', 'summary': 'Discusses setting up the python environment, working with jupyter notebooks, and converting jupyter notebook code into a python script, emphasizing the importance of using an integrated development environment (ide) over an interactive shell, and explaining the concept of python variables and multiple assignment.', 'duration': 410.143, 'highlights': ['The chapter discusses setting up the Python environment, working with Jupyter notebooks, and converting Jupyter notebook code into a Python script The chapter emphasizes the use of Jupyter notebooks as the go-to environment for working with Python, explains the process of accessing Python directly through Anaconda prompt or terminal, and demonstrates converting Jupyter notebook code into a Python file.', 'Emphasizing the importance of using an integrated development environment (IDE) over an interactive shell The speaker highlights the drawbacks of working in an interactive shell, stating that using an IDE like Jupyter notebook makes it easier to track executed code and work with complex scripts.', 'Explaining the concept of Python variables and multiple assignment The chapter explains the concept of Python variables as memory locations to store values, demonstrates simple ways of assigning values to variables, and discusses multiple assignment by assigning multiple variables to a single value or assigning different values to multiple variables.']}, {'end': 7024.234, 'start': 6686.839, 'title': 'Python tokens and identifiers', 'summary': 'Explains python tokens, including keywords, identifiers, literals, and operators, and emphasizes the importance of not using keywords as identifiers, and also provides naming standards for identifiers.', 'duration': 337.395, 'highlights': ['Python tokens are made up of keywords, identifiers, literals, and operators, and it is important not to use keywords as identifiers. The chapter explains that a token in Python is typically made up of keywords, identifiers, literals, and operators. It emphasizes the importance of not using keywords as identifiers.', 'Naming standards for identifiers in Python include not starting with a number, not using special characters except underscore, and being case sensitive. The chapter provides naming standards for identifiers in Python, stating that identifiers cannot start with a number, cannot use special characters except underscore, and are case sensitive.', 'Python is case sensitive, meaning variables with different cases are treated as different variables. It is highlighted that in Python, variables with different cases are treated as different variables, unlike SQL where variables are not case sensitive.']}, {'end': 7487.298, 'start': 7024.234, 'title': 'Python literals and data types', 'summary': 'Covers python literals including integers, floats, complex numbers, and strings, highlighting the dynamic typecasting feature of python, unlike other declarative programming languages, and the lack of explicit type declaration before assigning values to variables.', 'duration': 463.064, 'highlights': ["Python dynamically assigns data types to variables, unlike other declarative programming languages, which require explicit type declaration before assigning values to variables. Python's dynamic typecasting feature automatically assigns the data type of a value to a variable without the need for explicit type declaration, unlike other declarative programming languages.", 'Python literals include integers, floats, complex numbers, and strings, each represented with their respective data types. Python literals encompass integers, floats, complex numbers, and strings, each associated with their specific data types.', "Complex numbers in Python are represented as a combination of real and imaginary parts, with 'j' denoting the imaginary unit. Complex numbers in Python consist of a real part and an imaginary part, denoted by 'j' as the imaginary unit."]}, {'end': 8037.974, 'start': 7487.298, 'title': 'Python literals and markdown cells', 'summary': 'Covers the concept of python literals including integer, string, boolean, and special literals, highlighting the examples of type conversion and string concatenation. it also introduces markdown cells for creating headings in jupyter notebooks.', 'duration': 550.676, 'highlights': ["Python literals include integer, float, string, boolean, and special literals such as 'none'. The chapter discusses the concept of Python literals, covering integer, float, string, boolean, and special literals like 'none'.", "Type conversion example demonstrates converting a value into an integer using the 'integer of X' function. The example illustrates the conversion of a value into an integer using the 'integer of X' function.", 'String concatenation is illustrated through examples of adding string literals and the resulting concatenation. The chapter provides examples demonstrating string concatenation and the resulting concatenated string literals.', 'Introduction of Markdown cells for creating headings in Jupyter notebooks. The chapter introduces the use of Markdown cells for creating headings in Jupyter notebooks, enabling the representation of different levels of headings with their own links.']}], 'duration': 2365.102, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI5672872.jpg', 'highlights': ['Anaconda distribution provides bundled packages from PyPI for convenience, eliminating manual installation of Python and PyPI packages.', 'Jupyter notebooks offer a user-friendly UI for code writing, dashboarding, and collaboration, making it particularly useful for quick and dirty analysis.', 'Python variables are memory locations to store values, and the chapter explains multiple assignment by assigning multiple variables to a single value or assigning different values to multiple variables.', 'Python tokens are made up of keywords, identifiers, literals, and operators, and it is important not to use keywords as identifiers.', 'Python dynamically assigns data types to variables, unlike other declarative programming languages, which require explicit type declaration before assigning values to variables.', 'Python literals include integers, floats, complex numbers, and strings, each represented with their respective data types.', "The chapter discusses the concept of Python literals, covering integer, float, string, boolean, and special literals like 'none'.", "The example illustrates the conversion of a value into an integer using the 'integer of X' function.", 'The chapter introduces the use of Markdown cells for creating headings in Jupyter notebooks, enabling the representation of different levels of headings with their own links.']}, {'end': 9553.718, 'segs': [{'end': 8075.341, 'src': 'embed', 'start': 8038.894, 'weight': 0, 'content': [{'end': 8053.182, 'text': "So, for example, what I'm going to do now, what I'm going to do now is I'm simply going to say operators and under the operator section,", 'start': 8038.894, 'duration': 14.288}, {'end': 8055.803, 'text': "I'm going to start mentioning about all the operators that I know.", 'start': 8053.182, 'duration': 2.621}, {'end': 8057.845, 'text': "The first one is, let's say, your.", 'start': 8055.843, 'duration': 2.002}, {'end': 8075.341, 'text': 'The first one is your equal to the second one is not equal to the third one is greater than fourth one is less than.', 'start': 8064.417, 'duration': 10.924}], 'summary': 'Mentioning various operators: your, equal to, not equal to, greater than, less than.', 'duration': 36.447, 'max_score': 8038.894, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI8038894.jpg'}, {'end': 8379.378, 'src': 'embed', 'start': 8317.393, 'weight': 1, 'content': [{'end': 8324.7, 'text': 'If I put an or condition on the other hand, this would return a true.', 'start': 8317.393, 'duration': 7.307}, {'end': 8330.441, 'text': 'Right If I put an OR condition on the other hand, it returns a true.', 'start': 8327.482, 'duration': 2.959}, {'end': 8334.582, 'text': 'So these are a few arithmetic and the assignment operator.', 'start': 8330.921, 'duration': 3.661}, {'end': 8337.201, 'text': "So let's quickly look at a few arithmetic operators that we know of.", 'start': 8334.602, 'duration': 2.599}, {'end': 8342.263, 'text': 'Plus, minus, multiplied by, divided by, percentile, and star, star.', 'start': 8337.402, 'duration': 4.861}, {'end': 8344.204, 'text': 'These are pretty standard stuff.', 'start': 8342.824, 'duration': 1.38}, {'end': 8347.844, 'text': 'Arithmetic operators for doing addition, subtraction, multiplication, division.', 'start': 8344.724, 'duration': 3.12}, {'end': 8352.126, 'text': 'The modulo over here is for computing the remainder.', 'start': 8348.424, 'duration': 3.702}, {'end': 8358.91, 'text': 'So for example, you could do 10 modulo 2.', 'start': 8352.146, 'duration': 6.764}, {'end': 8361.531, 'text': '10 modulo 3 would return anyone?', 'start': 8358.91, 'duration': 2.621}, {'end': 8363.392, 'text': '10 modulo 3 would return 1.', 'start': 8361.531, 'duration': 1.861}, {'end': 8369.174, 'text': 'yeah, so 10 modulo 3 would return 1 and on the other hand, you could do a 3.', 'start': 8363.392, 'duration': 5.782}, {'end': 8372.215, 'text': 'this is the value of X.', 'start': 8369.174, 'duration': 3.041}, {'end': 8379.378, 'text': 'so X star star 2 would return.', 'start': 8372.215, 'duration': 7.163}], 'summary': 'Introduction to arithmetic operators including modulo and exponentiation with examples.', 'duration': 61.985, 'max_score': 8317.393, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI8317393.jpg'}, {'end': 8795.771, 'src': 'embed', 'start': 8767.963, 'weight': 3, 'content': [{'end': 8771.365, 'text': 'i am assuming this is super basics in python.', 'start': 8767.963, 'duration': 3.402}, {'end': 8775.966, 'text': "so we're going to talk about data types in python.", 'start': 8771.365, 'duration': 4.601}, {'end': 8780.448, 'text': 'this is one way of categorizing data types in python.', 'start': 8775.966, 'duration': 4.482}, {'end': 8788.131, 'text': 'if you would want to call these also have data structures, i would sort of say so.', 'start': 8780.448, 'duration': 7.683}, {'end': 8794.49, 'text': 'on the left hand side, if you see, uh, they are immutable data types, and on the right side you see mutable data types.', 'start': 8788.131, 'duration': 6.359}, {'end': 8795.771, 'text': 'what we mean by immutable?', 'start': 8794.49, 'duration': 1.281}], 'summary': 'Intro to python data types, discussing immutability and mutability.', 'duration': 27.808, 'max_score': 8767.963, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI8767963.jpg'}, {'end': 8990.716, 'src': 'embed', 'start': 8962.073, 'weight': 4, 'content': [{'end': 8964.175, 'text': 'to the actual value in itself.', 'start': 8962.073, 'duration': 2.102}, {'end': 8968.999, 'text': 'Remember, these are immutable.', 'start': 8967.157, 'duration': 1.842}, {'end': 8974.163, 'text': 'When I say immutable, what do I mean? These values cannot be changed.', 'start': 8970.159, 'duration': 4.004}, {'end': 8975.944, 'text': "I've assigned X as hello, hi.", 'start': 8974.303, 'duration': 1.641}, {'end': 8986.653, 'text': "But the only way I've assigned X as hello, hi, the only way I can change this particular value is by overwriting this particular data type.", 'start': 8976.905, 'duration': 9.748}, {'end': 8990.716, 'text': 'I can overwrite this value, this particular variable with a new value.', 'start': 8986.693, 'duration': 4.023}], 'summary': 'Immutable values cannot be changed, must be overwritten.', 'duration': 28.643, 'max_score': 8962.073, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI8962073.jpg'}, {'end': 9251.887, 'src': 'embed', 'start': 9216.329, 'weight': 6, 'content': [{'end': 9217.05, 'text': 'This is minus one.', 'start': 9216.329, 'duration': 0.721}, {'end': 9217.91, 'text': 'I is minus one.', 'start': 9217.17, 'duration': 0.74}, {'end': 9218.971, 'text': 'H is minus two and so on.', 'start': 9217.95, 'duration': 1.021}, {'end': 9220.532, 'text': 'So this is reverse indexing, so to say.', 'start': 9218.991, 'duration': 1.541}, {'end': 9229.754, 'text': 'On the other hand, I can also index this from 0 and colon 2.', 'start': 9221.289, 'duration': 8.465}, {'end': 9240.821, 'text': 'When I say 0 colon 2, what is essentially going to happen? So remember, this is the 0, 1, 2, 3, 4, 5, and 6.', 'start': 9229.754, 'duration': 11.067}, {'end': 9242.022, 'text': 'This is basically the index.', 'start': 9240.821, 'duration': 1.201}, {'end': 9243.823, 'text': 'This is your index.', 'start': 9242.942, 'duration': 0.881}, {'end': 9251.887, 'text': 'On the other hand, what is basically happening here is when you say this is X right?', 'start': 9244.684, 'duration': 7.203}], 'summary': 'Discusses reverse indexing and indexing from 0 to 2 in a sequence.', 'duration': 35.558, 'max_score': 9216.329, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI9216329.jpg'}, {'end': 9339.519, 'src': 'embed', 'start': 9306.48, 'weight': 5, 'content': [{'end': 9307.441, 'text': 'Always remember this.', 'start': 9306.48, 'duration': 0.961}, {'end': 9312.503, 'text': "Whenever you're doing any kind of indexing in Python, it is always left inclusive and right exclusive.", 'start': 9307.521, 'duration': 4.982}, {'end': 9316.044, 'text': "That's more or less a rule of thumb.", 'start': 9314.123, 'duration': 1.921}, {'end': 9321.687, 'text': 'Left inclusive and right exclusive.', 'start': 9317.685, 'duration': 4.002}, {'end': 9326.249, 'text': 'This is like a rule of thumb.', 'start': 9324.948, 'duration': 1.301}, {'end': 9327.669, 'text': 'Remember that always.', 'start': 9326.429, 'duration': 1.24}, {'end': 9335.178, 'text': 'On the other hand, if I say X of uh, three colon.', 'start': 9330.19, 'duration': 4.988}, {'end': 9336.438, 'text': 'just a quick info, guys.', 'start': 9335.178, 'duration': 1.26}, {'end': 9339.519, 'text': 'test your knowledge of python by answering this question.', 'start': 9336.438, 'duration': 3.081}], 'summary': 'In python indexing, remember it is left inclusive and right exclusive.', 'duration': 33.039, 'max_score': 9306.48, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI9306480.jpg'}, {'end': 9486.5, 'src': 'embed', 'start': 9452.286, 'weight': 7, 'content': [{'end': 9453.566, 'text': 'You could simply strip spaces.', 'start': 9452.286, 'duration': 1.28}, {'end': 9457.186, 'text': 'But remember, you will have to assign it back again.', 'start': 9454.646, 'duration': 2.54}, {'end': 9459.207, 'text': "That's the only way it would.", 'start': 9458.147, 'duration': 1.06}, {'end': 9460.307, 'text': "It's more or less like trim.", 'start': 9459.447, 'duration': 0.86}, {'end': 9463.908, 'text': 'Okay So the strip function is basically like the trim function in your SQL.', 'start': 9460.387, 'duration': 3.521}, {'end': 9465.628, 'text': "And it's very similar to that.", 'start': 9464.128, 'duration': 1.5}, {'end': 9468.348, 'text': 'On the other hand, you also have replace.', 'start': 9466.188, 'duration': 2.16}, {'end': 9477.71, 'text': "So if you look at the replace now, here is something that I want all of you all to know is, whenever you use a method like this, like, let's say,", 'start': 9469.448, 'duration': 8.262}, {'end': 9486.5, 'text': "firstly, x dot, and when you hit a tab, you get all the functions you use capitalize, for example and it'll kick.", 'start': 9477.71, 'duration': 8.79}], 'summary': 'Using strip and replace functions in python for data manipulation.', 'duration': 34.214, 'max_score': 9452.286, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI9452286.jpg'}], 'start': 8038.894, 'title': 'Python operators and data types', 'summary': 'Introduces common operators in markdown and covers python assignment, logical, arithmetic, and bitwise operators, and conditional statements. it also discusses python data types categorization and string indexing and manipulation.', 'chapters': [{'end': 8124.852, 'start': 8038.894, 'title': 'Introduction to operators in markdown', 'summary': 'Introduces common operators like equal to, not equal to, greater than, and less than in markdown, highlighting their usefulness and relevance to the topic.', 'duration': 85.958, 'highlights': ['The chapter introduces popular operators like equal to, not equal to, greater than, and less than, emphasizing their usefulness and relevance.', "The speaker mentions the common operators, emphasizing that they are already known and won't be reiterated, adding value to the discussion."]}, {'end': 8429.533, 'start': 8124.852, 'title': 'Python operators and conditions', 'summary': 'Covers python assignment operators, equality checks, logical operators, arithmetic operators, and conditional statements, including examples and outcomes.', 'duration': 304.681, 'highlights': ['The single equal to is a simple assignment operator, while double equal to is an equality check, returning a boolean type value. Explains the difference between single and double equal to operators and their outcomes.', 'Logical operators like AND, OR, and NOT are explained, with examples showcasing their outcomes. Provides an overview of logical operators and their use in conditional statements.', 'Arithmetic operators for addition, subtraction, multiplication, division, modulo, and exponentiation are discussed, with examples demonstrating their usage and results. Covers various arithmetic operators and their application, including specific examples.']}, {'end': 8767.963, 'start': 8429.553, 'title': 'Python assignment operators & bitwise operators', 'summary': 'Discusses python assignment operators, such as plus equal to and bitwise operators like left shift and right shift, providing examples and explanations for their usage. it also touches on the is and is not operators and their practical applications in python.', 'duration': 338.41, 'highlights': ['Python assignment operators like plus equal to and minus equal to are discussed, providing a concise and clear explanation of their usage, which can lead to more readable and efficient code. The chapter explains the usage of Python assignment operators like plus equal to and minus equal to, offering a concise and clear explanation that leads to more readable and efficient code.', 'Examples and explanations are given for bitwise operators in Python, including left shift and right shift, with a demonstration of their practical usage, such as X left shift of one returning a value of five. The transcript provides examples and explanations for bitwise operators in Python, demonstrating practical usage such as X left shift of one returning a value of five.', 'The chapter briefly introduces the is and is not operators and their practical applications, showcasing their usefulness in substring searches and mentioning their relevance when learning about lists in Python. It briefly introduces the is and is not operators, showcasing their practical applications in substring searches and mentioning their relevance when learning about lists in Python.']}, {'end': 9103.235, 'start': 8767.963, 'title': 'Python data types basics', 'summary': 'Covers the categorization of data types in python into immutable and mutable types, with examples and explanations of their characteristics and operations, emphasizing the concept of immutability and the need to assign back the updated value for immutable types.', 'duration': 335.272, 'highlights': ['The chapter explains the categorization of data types in Python into immutable and mutable types, with examples and explanations of their characteristics and operations.', 'The concept of immutability is emphasized, highlighting that once a certain value is assigned to a variable in an immutable data type, the variable can only be overwritten, but the value inside it cannot be changed.', 'The need to assign back the updated value for immutable types is stressed, demonstrating the importance of re-assigning the updated value to the original variable to persist the output.', 'The distinction between immutable data types (e.g., strings and numbers) and mutable data types (e.g., lists, dictionaries, sets, and tuples) is discussed, along with the consideration of lists, dictionaries, sets, and tuples as data structures due to their ability to hold more than one value.', "The chapter delves into working with strings in Python, including examples and explanations of string operations, such as concatenation, methods like 'upper', and the need to re-assign the updated value to persist the changes."]}, {'end': 9553.718, 'start': 9103.335, 'title': 'Python string indexing and manipulation', 'summary': 'Explains python string indexing and manipulation, highlighting the left-inclusive and right-exclusive rule, reverse indexing, and string manipulation functions like strip and replace.', 'duration': 450.383, 'highlights': ['The left-inclusive and right-exclusive rule in Python string indexing is explained, emphasizing the inclusive nature of the left index and the exclusive nature of the right index, illustrated with examples.', 'The concept of reverse indexing in Python strings is described, indicating how negative indices can be used to access characters from the end of the string, with examples demonstrating this technique.', 'The usage of string manipulation functions like strip and replace in Python is demonstrated, emphasizing their utility in removing leading/trailing spaces and replacing substrings within a string, drawing parallels with SQL functions.', "The process of accessing specific characters within a string using square brackets and Python's zero-based indexing system is explained, highlighting the need to adjust to zero-based counting in Python as opposed to one-based counting in human conventions."]}], 'duration': 1514.824, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI8038894.jpg', 'highlights': ['The chapter introduces popular operators like equal to, not equal to, greater than, and less than, emphasizing their usefulness and relevance.', 'Logical operators like AND, OR, and NOT are explained, with examples showcasing their outcomes. Provides an overview of logical operators and their use in conditional statements.', 'Arithmetic operators for addition, subtraction, multiplication, division, modulo, and exponentiation are discussed, with examples demonstrating their usage and results. Covers various arithmetic operators and their application, including specific examples.', 'The chapter explains the categorization of data types in Python into immutable and mutable types, with examples and explanations of their characteristics and operations.', 'The concept of immutability is emphasized, highlighting that once a certain value is assigned to a variable in an immutable data type, the variable can only be overwritten, but the value inside it cannot be changed.', 'The left-inclusive and right-exclusive rule in Python string indexing is explained, emphasizing the inclusive nature of the left index and the exclusive nature of the right index, illustrated with examples.', 'The concept of reverse indexing in Python strings is described, indicating how negative indices can be used to access characters from the end of the string, with examples demonstrating this technique.', 'The usage of string manipulation functions like strip and replace in Python is demonstrated, emphasizing their utility in removing leading/trailing spaces and replacing substrings within a string, drawing parallels with SQL functions.']}, {'end': 11316.592, 'segs': [{'end': 9674.247, 'src': 'embed', 'start': 9626.202, 'weight': 0, 'content': [{'end': 9632.392, 'text': "only the first occurrences are replaced, So it doesn't take a keyword argument.", 'start': 9626.202, 'duration': 6.19}, {'end': 9632.752, 'text': "That's okay.", 'start': 9632.412, 'duration': 0.34}, {'end': 9639.875, 'text': 'So you can just mention how many times do you want it to change? Just the first one or the second one.', 'start': 9633.132, 'duration': 6.743}, {'end': 9642.656, 'text': 'The default is minus one, which means it does it for everything.', 'start': 9640.215, 'duration': 2.441}, {'end': 9648.378, 'text': 'Okay So these are a few string operations.', 'start': 9643.956, 'duration': 4.422}, {'end': 9654.441, 'text': "A couple of other string operations that I just want to show you all is, let's say this is X.", 'start': 9648.418, 'duration': 6.023}, {'end': 9660.523, 'text': "And if I do, what's going to happen? X multiplied by two.", 'start': 9654.441, 'duration': 6.082}, {'end': 9665.18, 'text': 'it multiplies, it basically repeats.', 'start': 9661.718, 'duration': 3.462}, {'end': 9667.302, 'text': 'it essentially repeats the whole thing for you.', 'start': 9665.18, 'duration': 2.122}, {'end': 9669.784, 'text': "so it's basically concatenation.", 'start': 9667.302, 'duration': 2.482}, {'end': 9672.005, 'text': "so that's as far as numbers and strings is concerned.", 'start': 9669.784, 'duration': 2.221}, {'end': 9674.247, 'text': 'there are a lot of other methods also that are available.', 'start': 9672.005, 'duration': 2.242}], 'summary': "Demonstration of string operations and repetition using 'x' and explaining default arguments.", 'duration': 48.045, 'max_score': 9626.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI9626202.jpg'}, {'end': 9815.916, 'src': 'embed', 'start': 9784.786, 'weight': 3, 'content': [{'end': 9788.107, 'text': "So I'd want to talk about lists first and then we can talk about tuples as we go along.", 'start': 9784.786, 'duration': 3.321}, {'end': 9800.631, 'text': 'Okay So what is a list? A list in Python is basically a data structure which can hold any kind of a value inside it.', 'start': 9789.227, 'duration': 11.404}, {'end': 9805.71, 'text': 'When I say any kind of a value inside it, it can hold any data type inside it.', 'start': 9802.108, 'duration': 3.602}, {'end': 9807.952, 'text': 'It can hold a string.', 'start': 9806.771, 'duration': 1.181}, {'end': 9810.493, 'text': 'It can hold a number.', 'start': 9808.732, 'duration': 1.761}, {'end': 9812.875, 'text': 'It can hold another list.', 'start': 9811.134, 'duration': 1.741}, {'end': 9815.916, 'text': 'It can hold a dictionary or a tuple.', 'start': 9813.275, 'duration': 2.641}], 'summary': 'Lists in python are versatile data structures capable of holding various data types including strings, numbers, lists, dictionaries, and tuples.', 'duration': 31.13, 'max_score': 9784.786, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI9784786.jpg'}, {'end': 10020.073, 'src': 'embed', 'start': 9991.035, 'weight': 4, 'content': [{'end': 9995.998, 'text': 'There are certain challenges with lists as well, which other data structures overcome.', 'start': 9991.035, 'duration': 4.963}, {'end': 10000.961, 'text': 'But for now, a list is basically an array of values that it can simply hold.', 'start': 9996.418, 'duration': 4.543}, {'end': 10006.311, 'text': "So currently what I've done is, let's say, I've stored a lot of values, a lot of ages,", 'start': 10001.67, 'duration': 4.641}, {'end': 10011.812, 'text': "and along with it I've also stored some other string values along with it.", 'start': 10006.311, 'duration': 5.501}, {'end': 10020.073, 'text': 'So if you were to quickly check the length of this particular list, I can simply say len,', 'start': 10012.412, 'duration': 7.661}], 'summary': 'Lists are arrays of values with challenges, can hold various data types.', 'duration': 29.038, 'max_score': 9991.035, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI9991035.jpg'}, {'end': 10241.217, 'src': 'embed', 'start': 10208.111, 'weight': 5, 'content': [{'end': 10209.812, 'text': "let's say append 100.", 'start': 10208.111, 'duration': 1.701}, {'end': 10212.833, 'text': 'so now what happens?', 'start': 10209.812, 'duration': 3.021}, {'end': 10215.774, 'text': "remember, let's.", 'start': 10212.833, 'duration': 2.941}, {'end': 10220.456, 'text': 'before i execute this, i just want to show you one thing.', 'start': 10215.774, 'duration': 4.682}, {'end': 10223.497, 'text': 'lists are mutable.', 'start': 10220.456, 'duration': 3.041}, {'end': 10227.459, 'text': 'so when i simply say append of 100, look what happens.', 'start': 10223.497, 'duration': 3.962}, {'end': 10228.999, 'text': 'nothing gets returned.', 'start': 10227.459, 'duration': 1.54}, {'end': 10235.051, 'text': 'but now, if you look at x, x has now changed automatically, without even assigning back value of.', 'start': 10228.999, 'duration': 6.052}, {'end': 10241.217, 'text': 'x has changed because i have added an additional value over here on another day.', 'start': 10235.051, 'duration': 6.166}], 'summary': 'Appending 100 to a list in python results in automatic change of the list, without returning anything.', 'duration': 33.106, 'max_score': 10208.111, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI10208111.jpg'}, {'end': 10379.939, 'src': 'embed', 'start': 10350.869, 'weight': 7, 'content': [{'end': 10353.791, 'text': "so the ones you're not familiar with it, don't worry about it.", 'start': 10350.869, 'duration': 2.922}, {'end': 10363.138, 'text': "so basically, there's a method called as pop, and when you do a pop, what happens is from your x, the last value gets kicked out.", 'start': 10353.791, 'duration': 9.347}, {'end': 10367.641, 'text': 'right, the last value gets kicked out.', 'start': 10363.138, 'duration': 4.503}, {'end': 10372.024, 'text': 'so this particular value gets kicked out.', 'start': 10367.641, 'duration': 4.383}, {'end': 10374.706, 'text': 'and now you have x, which is just this much.', 'start': 10372.024, 'duration': 2.682}, {'end': 10377.739, 'text': 'So this value has been kicked out.', 'start': 10376.038, 'duration': 1.701}, {'end': 10379.939, 'text': 'When you say kicked out, it returns that value for you.', 'start': 10377.999, 'duration': 1.94}], 'summary': 'Explaining the pop method: removes last value from x and returns it.', 'duration': 29.07, 'max_score': 10350.869, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI10350869.jpg'}, {'end': 10612.895, 'src': 'embed', 'start': 10590.011, 'weight': 6, 'content': [{'end': 10598.414, 'text': 'these are the values in x and the moment you say x dot append of 100, the value 100 always goes and sits at the end.', 'start': 10590.011, 'duration': 8.403}, {'end': 10600.095, 'text': 'that is what x is currently.', 'start': 10598.414, 'duration': 1.681}, {'end': 10600.995, 'text': 'this is where we are.', 'start': 10600.095, 'duration': 0.9}, {'end': 10608.214, 'text': "And if you want to, let's say, insert a particular value in a specific position, let's say you want to insert the value 1000 in the first index.", 'start': 10601.753, 'duration': 6.461}, {'end': 10610.255, 'text': 'that means between 188 and 23,.', 'start': 10608.214, 'duration': 2.041}, {'end': 10612.895, 'text': 'the 188 zero and 23 is one.', 'start': 10610.255, 'duration': 2.64}], 'summary': 'Adding 100 to list x appends it; inserting 1000 between 188 and 23 at index 1.', 'duration': 22.884, 'max_score': 10590.011, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI10590011.jpg'}, {'end': 10712.89, 'src': 'embed', 'start': 10671.894, 'weight': 8, 'content': [{'end': 10679.258, 'text': 'The key point that you always have to understand with a list is that lists are mutable, which means that whenever you call these methods,', 'start': 10671.894, 'duration': 7.364}, {'end': 10682.28, 'text': "you don't have to assign this back to that particular list in itself.", 'start': 10679.258, 'duration': 3.022}, {'end': 10685.142, 'text': "So that's one of the most important things that you have to understand as well.", 'start': 10682.4, 'duration': 2.742}, {'end': 10695.722, 'text': 'So this is where we, and if you want to remove a particular value from the list, you could simply call the remove function dot.', 'start': 10688.444, 'duration': 7.278}, {'end': 10699.804, 'text': 'remove of 10 will only remove the first occurrence of that particular value.', 'start': 10695.722, 'duration': 4.082}, {'end': 10707.187, 'text': 'so, for example, if you have multiple occurrences of the value 10, it will only remove the first occurrence of that particular value.', 'start': 10699.804, 'duration': 7.383}, {'end': 10712.89, 'text': 'so remove will do it only once, so to say, okay.', 'start': 10707.187, 'duration': 5.703}], 'summary': 'Lists are mutable, and remove function removes first occurrence of a value.', 'duration': 40.996, 'max_score': 10671.894, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI10671894.jpg'}, {'end': 10803.681, 'src': 'embed', 'start': 10774.45, 'weight': 10, 'content': [{'end': 10777.292, 'text': "right, i'd want to understand how many occurrences are there for a particular value.", 'start': 10774.45, 'duration': 2.842}, {'end': 10783.197, 'text': 'so what i can simply do is i can simply do x dot count and I can say 10, and it would say four.', 'start': 10777.292, 'duration': 5.905}, {'end': 10790.922, 'text': 'So basically telling me that X is X occurs four times in this complete list as of now.', 'start': 10783.937, 'duration': 6.985}, {'end': 10796.645, 'text': 'Okay Now the other point about index.', 'start': 10792.122, 'duration': 4.523}, {'end': 10803.681, 'text': "So for example, let's say I'd want to identify at what location is the value 23.", 'start': 10796.705, 'duration': 6.976}], 'summary': "Using x.count(10) yields 4 occurrences. indexing locates 23's position.", 'duration': 29.231, 'max_score': 10774.45, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI10774450.jpg'}, {'end': 11168.037, 'src': 'embed', 'start': 11138.477, 'weight': 12, 'content': [{'end': 11140.037, 'text': 'Min of list one and max of list one.', 'start': 11138.477, 'duration': 1.56}, {'end': 11141.378, 'text': 'And this would have comfortably worked.', 'start': 11140.057, 'duration': 1.321}, {'end': 11147.239, 'text': 'So remember, the most important thing, if you want to use the min and max functions, you are free to do that.', 'start': 11142.158, 'duration': 5.081}, {'end': 11157.88, 'text': 'However, you will have to keep in your mind that this will only work if, and only if, the numbers are.', 'start': 11147.859, 'duration': 10.021}, {'end': 11161.661, 'text': 'all your values in your list belong to the same data type.', 'start': 11157.88, 'duration': 3.781}, {'end': 11168.037, 'text': 'The same can also be done for example, in this scenario.', 'start': 11162.213, 'duration': 5.824}], 'summary': 'Use min and max functions with same data type for list values.', 'duration': 29.56, 'max_score': 11138.477, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI11138477.jpg'}, {'end': 11280.443, 'src': 'embed', 'start': 11230.58, 'weight': 11, 'content': [{'end': 11251.027, 'text': "now, if, for example, let's look at these two, uh say these two expressions list one, dot, append of list two and list one, dot, extend of list two.", 'start': 11230.58, 'duration': 20.447}, {'end': 11252.167, 'text': 'what is the difference between these two?', 'start': 11251.027, 'duration': 1.14}, {'end': 11280.443, 'text': "So, if you look at the first result, currently I'm using an append and here in the next example, I'm using an extender.", 'start': 11272.381, 'duration': 8.062}], 'summary': 'Explains the difference between list append and extend methods.', 'duration': 49.863, 'max_score': 11230.58, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI11230580.jpg'}], 'start': 9554.178, 'title': 'Python string and list operations', 'summary': 'Covers string operations in python with examples of replacing occurrences and concatenating strings, introduces lists and tuples, explaining their differences and practical use cases, and explains python list methods and working with lists, including mutability, usage of methods, concatenation, and considerations for using min and max functions.', 'chapters': [{'end': 9674.247, 'start': 9554.178, 'title': 'String operations and replacements', 'summary': "Covers string operations in python, including replacement of occurrences, with examples demonstrating how to replace all occurrences and only the first occurrence, as well as the concatenation of strings using the 'multiply' operator.", 'duration': 120.069, 'highlights': ["The 'replace' method in Python allows replacing occurrences of a specified value with another value, where the default behavior replaces all occurrences, but it can be modified to replace only the first occurrence, indicated by a count parameter.", "The 'replace' method can be utilized to replace specific occurrences of a character within a string, and the count parameter allows control over the number of replacements, with the default value of -1 indicating replacement of all occurrences.", "Using the 'multiply' operator with strings in Python results in the repetition or concatenation of the string, providing a simple method for duplicating or extending string values."]}, {'end': 10184.669, 'start': 9674.247, 'title': 'Introduction to lists and tuples', 'summary': 'Introduces the concept of lists and tuples in python, highlighting their ability to store multiple values and their differences in mutability, alongside discussing the benefits and practical use cases of lists. it also covers the indexing and slicing of lists.', 'duration': 510.422, 'highlights': ['Lists and tuples are data structures in Python that can hold multiple values, with lists being mutable and tuples being immutable. Lists and tuples are introduced as data structures in Python, with lists being mutable and tuples being immutable.', 'Lists can hold various data types such as strings, numbers, lists, dictionaries, and more, making them a versatile data structure. Lists can hold various data types such as strings, numbers, lists, dictionaries, and more, making them a versatile data structure.', 'The practical use of lists is demonstrated by the example of storing the ages of students in a class, highlighting the flexibility and convenience of using lists for storing multiple values. The example of storing the ages of students in a class demonstrates the practical use and convenience of using lists for storing multiple values.', 'The process of indexing and slicing in lists is explained, showcasing the ability to access specific elements and ranges of elements within a list. The process of indexing and slicing in lists is explained, showcasing the ability to access specific elements and ranges of elements within a list.']}, {'end': 10671.274, 'start': 10184.669, 'title': 'Python lists methods', 'summary': 'Explains python list methods, including append, insert, pop, and remove, with examples and explanations of how they manipulate the list, such as adding, inserting, and removing values.', 'duration': 486.605, 'highlights': ['The method append adds a value at the end of the list, and it automatically modifies the list without requiring reassignment, demonstrated by adding 100 to the list x. Append 100 to the list x.', 'The insert method allows inserting a value at a specified position in the list, demonstrated by inserting 1000 at the first index between 188 and 23. Insert 1000 at the first index between 188 and 23.', 'The pop method removes the last value from the list and returns the removed value, exemplified by removing 100 from the list x. Remove the last value from the list x.', 'The remove method eliminates a specific value from the list, shown by removing the value 10 from the list. Remove the value 10 from the list.']}, {'end': 11316.592, 'start': 10671.894, 'title': 'Working with python lists', 'summary': 'Covers the mutability of lists, usage of remove, count, index methods, concatenation of lists, and considerations for using min and max functions with examples and explanations.', 'duration': 644.698, 'highlights': ["Lists are mutable, which means that whenever you call these methods, you don't have to assign this back to that particular list in itself. Emphasizes the mutability of lists and the implication of not having to reassign the result of list methods back to the original list.", 'The count method can be used to determine the number of occurrences of a specific value in a list. Explains the usage of the count method to find the number of occurrences of a specific value in a list.', 'Usage of extend method to concatenate two lists and the need to reassign the result to another variable. Describes the process of concatenating lists using the extend method and the requirement to assign the result to a new variable.', 'Using min and max functions with lists and the necessity for all values to belong to the same data type. Details the usage of min and max functions with lists and the condition that all values must belong to the same data type for the functions to work.', 'Comparison between append and extend methods for nested lists. Compares the append and extend methods for nested lists to highlight their differences.']}], 'duration': 1762.414, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI9554178.jpg', 'highlights': ["The 'replace' method in Python allows replacing occurrences of a specified value with another value, where the default behavior replaces all occurrences, but it can be modified to replace only the first occurrence, indicated by a count parameter.", "The 'replace' method can be utilized to replace specific occurrences of a character within a string, and the count parameter allows control over the number of replacements, with the default value of -1 indicating replacement of all occurrences.", "Using the 'multiply' operator with strings in Python results in the repetition or concatenation of the string, providing a simple method for duplicating or extending string values.", 'Lists and tuples are data structures in Python that can hold multiple values, with lists being mutable and tuples being immutable. Lists can hold various data types such as strings, numbers, lists, dictionaries, and more, making them a versatile data structure.', 'The practical use of lists is demonstrated by the example of storing the ages of students in a class, highlighting the flexibility and convenience of using lists for storing multiple values.', 'The method append adds a value at the end of the list, and it automatically modifies the list without requiring reassignment, demonstrated by adding 100 to the list x.', 'The insert method allows inserting a value at a specified position in the list, demonstrated by inserting 1000 at the first index between 188 and 23.', 'The pop method removes the last value from the list and returns the removed value, exemplified by removing 100 from the list x.', 'The remove method eliminates a specific value from the list, shown by removing the value 10 from the list.', "Lists are mutable, which means that whenever you call these methods, you don't have to assign this back to that particular list in itself.", 'The count method can be used to determine the number of occurrences of a specific value in a list.', 'Usage of extend method to concatenate two lists and the need to reassign the result to another variable.', 'Using min and max functions with lists and the necessity for all values to belong to the same data type.', 'Comparison between append and extend methods for nested lists.']}, {'end': 12940.621, 'segs': [{'end': 11482.876, 'src': 'embed', 'start': 11451.073, 'weight': 2, 'content': [{'end': 11462.51, 'text': "I am this is essentially a nested indexing meaning I am first extracting a value and from that value I'm extracting another value.", 'start': 11451.073, 'duration': 11.437}, {'end': 11473.133, 'text': 'Okay Now when we do list one of four, what do we get? We get the complete list.', 'start': 11467.011, 'duration': 6.122}, {'end': 11480.035, 'text': 'Okay Now when I do four of two, what am I going to get? So list one is now this complete list.', 'start': 11473.854, 'duration': 6.181}, {'end': 11482.876, 'text': 'Now from this list, I want the second element.', 'start': 11480.736, 'duration': 2.14}], 'summary': 'Nested indexing to extract elements from a list.', 'duration': 31.803, 'max_score': 11451.073, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI11451073.jpg'}, {'end': 12012.709, 'src': 'embed', 'start': 11983.187, 'weight': 4, 'content': [{'end': 11984.348, 'text': 'Then it goes from here to here.', 'start': 11983.187, 'duration': 1.161}, {'end': 11987.569, 'text': 'Then it goes from here to 42, from 42 to 100.', 'start': 11984.568, 'duration': 3.001}, {'end': 11989.209, 'text': 'Then from 100 to this complete list.', 'start': 11987.569, 'duration': 1.64}, {'end': 11991.55, 'text': 'And this from complete list to hello.', 'start': 11989.99, 'duration': 1.56}, {'end': 11993.851, 'text': 'And then those eventual values.', 'start': 11992.05, 'duration': 1.801}, {'end': 12001.094, 'text': "So whenever you're writing this simple for loop, what you're doing is you are iterating through every value in this particular list.", 'start': 11994.471, 'duration': 6.623}, {'end': 12005.175, 'text': 'So when you say for I in list one, print I.', 'start': 12003.014, 'duration': 2.161}, {'end': 12012.709, 'text': 'every time it is in that particular position, it is simply printing that particular value.', 'start': 12006.224, 'duration': 6.485}], 'summary': 'Iterating through a list of values in a for loop, printing each value.', 'duration': 29.522, 'max_score': 11983.187, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI11983187.jpg'}, {'end': 12275.384, 'src': 'embed', 'start': 12247.03, 'weight': 3, 'content': [{'end': 12254.214, 'text': 'The only point of me trying to show you this is to tell you all that lists by themselves cannot survive.', 'start': 12247.03, 'duration': 7.184}, {'end': 12261.917, 'text': 'So if you want to write any kind of complex logic, the lists by themselves are not sufficient to perform all the activities.', 'start': 12254.673, 'duration': 7.244}, {'end': 12268.581, 'text': "You will have to combine the lists with loops, with looping statements like what we're currently seeing here.", 'start': 12262.857, 'duration': 5.724}, {'end': 12275.384, 'text': 'Only then we will be able to achieve what we want to achieve, okay? So combine your lists with looping statements.', 'start': 12269.081, 'duration': 6.303}], 'summary': "Lists alone can't handle complex logic; combine with loops for effectiveness.", 'duration': 28.354, 'max_score': 12247.03, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI12247030.jpg'}, {'end': 12324.645, 'src': 'embed', 'start': 12293.099, 'weight': 0, 'content': [{'end': 12302.963, 'text': 'tuples are basically a specific scenario of lists and, as you see here, tuples are essentially a specific scenario of list.', 'start': 12293.099, 'duration': 9.864}, {'end': 12308.245, 'text': 'the only difference between lists and tuples is the fact that lists are immutable.', 'start': 12302.963, 'duration': 5.282}, {'end': 12310.627, 'text': 'tuples are immutable.', 'start': 12308.245, 'duration': 2.382}, {'end': 12324.645, 'text': 'that means whatever functions we had in lists of where you could change the values inside a particular list, cannot be done in the case of a tuple.', 'start': 12310.627, 'duration': 14.018}], 'summary': 'Tuples are immutable lists, cannot be changed.', 'duration': 31.546, 'max_score': 12293.099, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI12293099.jpg'}], 'start': 11317.512, 'title': 'List manipulation and data structures in python', 'summary': 'Covers list manipulation methods such as append and extend, nested indexing, list operations, combining lists with loops and tuples, and the differences between tuples and lists, as well as an introduction to dictionaries in python, providing comprehensive insights into data manipulation techniques and structures.', 'chapters': [{'end': 11373.914, 'start': 11317.512, 'title': 'List manipulation in python', 'summary': 'Explains the difference between the append and extend methods for list manipulation in python, where extend yields a flattened list and append returns a nested list, with an example of the output.', 'duration': 56.402, 'highlights': ['The extend method in Python always yields a flattened list, while the append method always returns a nested list, with the last element being a list in itself.', 'The output from extend is always going to be a flattened output versus the output from append, which will always return a nested list.', 'When using append, the last element, regardless of its value, will be added as a last element, resulting in a nested list.']}, {'end': 11862.816, 'start': 11374.815, 'title': 'Nested indexing and list operations', 'summary': "Explains nested indexing in a list, counting elements using the 'count' method, and removing all occurrences of a value using 'del' in python, with examples and explanation.", 'duration': 488.001, 'highlights': ["The 'count' method is used to identify the number of elements of a given value in a list, such as counting the occurrences of 'hello' in the list, resulting in an output of one. The 'count' method is demonstrated, identifying the number of occurrences of 'hello' in the list and explaining that it counts values in the top layer of the list, resulting in a count of one for 'hello' in the example list.", "Explanation of using 'del' to remove all occurrences of a specific value from a list, addressing the limitation of 'remove' method and providing an alternative for dealing with multiple occurrences of a value in a list. The 'del' method is explained, highlighting its capability to remove all occurrences of a specific value from a list, addressing the limitation of the 'remove' method and providing an alternative for dealing with multiple occurrences of a value in a list.", 'Detailed explanation of nested indexing in a list, with step-by-step demonstration of extracting specific elements using index values, providing clarity on nested indexing and its application in Python list manipulation. The detailed explanation of nested indexing in a list is provided, including step-by-step demonstration of extracting specific elements using index values, offering clarity on nested indexing and its application in Python list manipulation.']}, {'end': 12563.489, 'start': 11862.816, 'title': 'Combining lists with loops and tuples', 'summary': 'Explains the use of for loops to iterate through a list and the differences between lists and tuples, emphasizing the importance of combining lists with loops and conditional statements. it also highlights the limitations of tuples compared to lists.', 'duration': 700.673, 'highlights': ['The chapter explains the use of for loops to iterate through a list. Demonstrates the use of for loops to iterate through a list and print values, and uses an example to illustrate the concept.', 'Emphasizes the importance of combining lists with loops and conditional statements. Stresses the need to combine lists with loops and conditional statements to achieve complex business logic.', 'Highlights the limitations of tuples compared to lists. Explains the immutability of tuples and the inability to change or remove values, contrasting them with lists.']}, {'end': 12940.621, 'start': 12564.65, 'title': 'Tuples vs lists and introduction to dictionaries', 'summary': 'Explains the difference between tuples and lists, highlighting the immutability of tuples and the potential data security benefits. it also introduces dictionaries as key-value pairs, emphasizing their significance in data storage and retrieval.', 'duration': 375.971, 'highlights': ['Tuples prevent data from being overwritten, providing data security Tuples ensure data integrity by preventing values from being overwritten, making them suitable for storing sensitive information. This immutability enhances data security within a program.', 'Creating a new tuple by combining existing tuples with no updates to original tuples Concatenating tuples results in the creation of a new tuple without altering the original tuples, preserving their original values and immutability.', 'Introduction to dictionaries as key-value pairs, emphasizing their significance in data storage and retrieval The chapter introduces dictionaries as key-value pairs and emphasizes their importance in storing and retrieving data, likening them to hash tables and explaining their role in organizing data efficiently.']}], 'duration': 1623.109, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI11317512.jpg', 'highlights': ['The chapter introduces dictionaries as key-value pairs and emphasizes their importance in storing and retrieving data, likening them to hash tables and explaining their role in organizing data efficiently.', 'Tuples ensure data integrity by preventing values from being overwritten, making them suitable for storing sensitive information. This immutability enhances data security within a program.', 'The detailed explanation of nested indexing in a list is provided, including step-by-step demonstration of extracting specific elements using index values, offering clarity on nested indexing and its application in Python list manipulation.', 'Stresses the need to combine lists with loops and conditional statements to achieve complex business logic.', 'Demonstrates the use of for loops to iterate through a list and print values, and uses an example to illustrate the concept.']}, {'end': 14606.412, 'segs': [{'end': 13181.172, 'src': 'embed', 'start': 13156.053, 'weight': 9, 'content': [{'end': 13163.263, 'text': 'So that if anybody checks for a particular name, If anybody checks for a specific name,', 'start': 13156.053, 'duration': 7.21}, {'end': 13168.886, 'text': 'there is a small logic written which will directly take me to the location where the respective value has been stored.', 'start': 13163.263, 'duration': 5.623}, {'end': 13173.448, 'text': 'So such a technique is called as hashing.', 'start': 13170.046, 'duration': 3.402}, {'end': 13175.329, 'text': "There's a technique called as hashing.", 'start': 13174.008, 'duration': 1.321}, {'end': 13178.251, 'text': 'A hashing basically is simply like this.', 'start': 13176.41, 'duration': 1.841}, {'end': 13181.172, 'text': "So for example, let's say a very simple example.", 'start': 13178.291, 'duration': 2.881}], 'summary': 'Hashing technique allows direct access to stored values.', 'duration': 25.119, 'max_score': 13156.053, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI13156053.jpg'}, {'end': 13320.723, 'src': 'embed', 'start': 13294.302, 'weight': 7, 'content': [{'end': 13304.669, 'text': "if let's say i would want, if let's say i would want to extract the uh, the marks that are scored by a person c,", 'start': 13294.302, 'duration': 10.367}, {'end': 13306.871, 'text': "what i'm going to do is let's say this is dict one.", 'start': 13304.669, 'duration': 2.202}, {'end': 13314.137, 'text': "all i'm simply going to say is i'm going to say dict one off person c.", 'start': 13307.592, 'duration': 6.545}, {'end': 13320.723, 'text': 'and the moment i say person c, this dictionary object remembers where the value of c is exactly stored in the memory.', 'start': 13314.137, 'duration': 6.586}], 'summary': 'Extract marks scored by person c from dict one', 'duration': 26.421, 'max_score': 13294.302, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI13294302.jpg'}, {'end': 13523.677, 'src': 'embed', 'start': 13495.449, 'weight': 3, 'content': [{'end': 13498.05, 'text': "let's quickly see how to create a dictionary into action.", 'start': 13495.449, 'duration': 2.601}, {'end': 13504.975, 'text': "let's take, for example, a simple example of an employee database right employee database.", 'start': 13498.05, 'duration': 6.925}, {'end': 13511.511, 'text': 'now, In a classic employee database scenario, what would you have??', 'start': 13504.975, 'duration': 6.536}, {'end': 13521.516, 'text': 'I would want to retrieve all the details of a particular employee based on the name of that particular employee.', 'start': 13511.531, 'duration': 9.985}, {'end': 13523.677, 'text': "Or let's say instead of the name, let's say on the employee ID.", 'start': 13521.556, 'duration': 2.121}], 'summary': 'Demonstrating how to create a dictionary for an employee database scenario.', 'duration': 28.228, 'max_score': 13495.449, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI13495449.jpg'}, {'end': 13580.744, 'src': 'embed', 'start': 13545.604, 'weight': 6, 'content': [{'end': 13547.785, 'text': 'Think of it as employee number or one zero one.', 'start': 13545.604, 'duration': 2.181}, {'end': 13552.127, 'text': "And let's say the name is, let's say John.", 'start': 13548.565, 'duration': 3.562}, {'end': 13555.169, 'text': 'One zero two.', 'start': 13554.468, 'duration': 0.701}, {'end': 13558.09, 'text': "And let's say Emma.", 'start': 13556.73, 'duration': 1.36}, {'end': 13560.532, 'text': 'One zero three.', 'start': 13559.831, 'duration': 0.701}, {'end': 13564.394, 'text': "Let's say whatever.", 'start': 13563.113, 'duration': 1.281}, {'end': 13569.918, 'text': "103 is, let's say ben and so on and so forth.", 'start': 13566.075, 'duration': 3.843}, {'end': 13572.699, 'text': "let's say 104 is.", 'start': 13569.918, 'duration': 2.781}, {'end': 13578.022, 'text': "uh, anyway, so this is my dictionary, one that's as simply as that.", 'start': 13572.699, 'duration': 5.323}, {'end': 13580.744, 'text': 'yeah, so dict one.', 'start': 13578.022, 'duration': 2.722}], 'summary': 'The transcript discusses the creation of a dictionary with employee numbers and names.', 'duration': 35.14, 'max_score': 13545.604, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI13545604.jpg'}, {'end': 14508.21, 'src': 'embed', 'start': 14472.762, 'weight': 0, 'content': [{'end': 14474.763, 'text': 'yeah, somebody had a question here.', 'start': 14472.762, 'duration': 2.001}, {'end': 14479.588, 'text': 'uh, in case of dictionaries, pop and remove, both are same.', 'start': 14474.763, 'duration': 4.825}, {'end': 14482.55, 'text': 'yeah, exactly, pop and remove is the same.', 'start': 14479.588, 'duration': 2.962}, {'end': 14484.172, 'text': "you don't have anything called as remove here.", 'start': 14482.55, 'duration': 1.622}, {'end': 14487.154, 'text': 'pop is what remove is all about here.', 'start': 14484.172, 'duration': 2.982}, {'end': 14495.542, 'text': 'yeah, the other point that you also will have to understand, uh, guys, is that you might see when you print the dictionary,', 'start': 14487.154, 'duration': 8.388}, {'end': 14499.166, 'text': 'you might see that the values are in an ordered fashion.', 'start': 14496.385, 'duration': 2.781}, {'end': 14508.21, 'text': 'You might see that this is 101, 102, 103, but technically dictionaries are unordered collection of objects.', 'start': 14499.446, 'duration': 8.764}], 'summary': 'Pop and remove are the same in dictionaries. dictionaries are unordered collections.', 'duration': 35.448, 'max_score': 14472.762, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI14472762.jpg'}], 'start': 12940.621, 'title': 'Optimizing search and dictionaries in python', 'summary': 'Discusses the inefficiency of searching through a list of 10,000 students, emphasizing the need for an optimized approach. it also explains the concept of dictionaries, their efficiency in value retrieval, key rules, and manipulation in python, highlighting their benefits and usage of specific methods.', 'chapters': [{'end': 13053.185, 'start': 12940.621, 'title': 'Optimizing search process with lists', 'summary': "Discusses the inefficiency of searching through a list of 10,000 students to find a person's position and then accessing their marks, highlighting the need for an optimized approach to store and retrieve values from lists.", 'duration': 112.564, 'highlights': ["Searching through a list of 10,000 students for a person's position and then accessing their marks is highlighted as a very tedious and inefficient task, involving 10,000 searches and comparisons.", 'The inefficiency of the searching process in the given scenario is emphasized, with the speaker highlighting the need for a more efficient way of storing and retrieving values from lists.']}, {'end': 13476.231, 'start': 13053.185, 'title': 'Introduction to dictionaries in python', 'summary': 'Explains the concept of dictionaries, a key-value data structure, how it works, its efficiency in value retrieval, and the key rules, emphasizing the quick retrieval, unique keys, and diverse value types.', 'duration': 423.046, 'highlights': ['Dictionaries are efficient for quick value retrieval through unique keys. Dictionaries allow for fast retrieval of values through unique keys, making it a popular data structure.', 'The key-value pairs in dictionaries allow for diverse value types, including numbers, strings, lists, and dictionaries. Dictionaries support a wide range of value types, such as numbers, strings, lists, and even nested dictionaries.', "The concept of hashing in dictionaries enables quick retrieval by mapping keys to their respective values. The technique of hashing in dictionaries facilitates efficient value retrieval by mapping keys to their corresponding values, similar to how a map in one's mind works."]}, {'end': 13919.718, 'start': 13477.037, 'title': 'Dictionaries in python', 'summary': 'Explains the concept of dictionaries in python, demonstrating how to create and manipulate key-value pairs, as well as nested dictionaries and lists, to store and retrieve employee data efficiently.', 'duration': 442.681, 'highlights': ['Dictionaries allow for efficient storage and retrieval of key-value pairs, enabling easy access to employee data based on employee ID or name. Dictionaries provide a convenient way to store and retrieve employee data based on employee ID or name, demonstrating efficient data access.', 'Demonstrated the creation of nested dictionaries to store multiple details for each employee, allowing for organized and structured data storage. Illustrated the creation of nested dictionaries to store multiple details for each employee, enabling organized and structured data storage.', 'Explained the possibility of using lists to store multiple values and demonstrated the convenient extraction of specific information from the lists based on employee ID. Explained the use of lists to store multiple values and showcased the extraction of specific information based on employee ID, facilitating easy data retrieval.']}, {'end': 14606.412, 'start': 13919.718, 'title': 'Working with dictionaries in python', 'summary': 'Explains how to efficiently work with dictionaries in python, highlighting the benefits of key-value pairs, the usage of methods like clear, keys, items, pop, update, and the nature of dictionaries as an unordered collection of objects.', 'duration': 686.694, 'highlights': ['Dictionaries are a popular form of representing data in the IT industry, similar to JSON objects for transferring data between systems. Dictionaries and JSON objects are widely used for representing and transferring data in the IT industry.', 'The chapter explains methods like clear, keys, items, pop, and update for manipulating dictionary objects. The chapter covers methods such as clear, keys, items, pop, and update for manipulating dictionary objects.', 'Dictionaries are mutable objects, allowing for easy modification of key-value pairs using methods like update. Dictionaries are mutable objects, enabling simple modification of key-value pairs using methods like update.', 'The nature of dictionaries as an unordered collection of objects is emphasized, clarifying that they cannot be indexed by position. Dictionaries are emphasized as unordered collections, clarifying that they cannot be indexed by position.']}], 'duration': 1665.791, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI12940621.jpg', 'highlights': ['Dictionaries allow for efficient storage and retrieval of key-value pairs, enabling easy access to employee data based on employee ID or name.', 'The concept of hashing in dictionaries enables quick retrieval by mapping keys to their respective values.', 'Dictionaries are efficient for quick value retrieval through unique keys.', 'Explained the possibility of using lists to store multiple values and demonstrated the convenient extraction of specific information from the lists based on employee ID.', 'The inefficiency of the searching process in the given scenario is emphasized, with the speaker highlighting the need for a more efficient way of storing and retrieving values from lists.', 'Dictionaries and JSON objects are widely used for representing and transferring data in the IT industry.', 'The chapter covers methods such as clear, keys, items, pop, and update for manipulating dictionary objects.', 'Illustrated the creation of nested dictionaries to store multiple details for each employee, enabling organized and structured data storage.', 'Dictionaries are mutable objects, enabling simple modification of key-value pairs using methods like update.', 'The nature of dictionaries as an unordered collection of objects is emphasized, clarifying that they cannot be indexed by position.']}, {'end': 16004.359, 'segs': [{'end': 15049.89, 'src': 'embed', 'start': 15023.785, 'weight': 4, 'content': [{'end': 15032.254, 'text': 'But then with the general understanding that we need more data to train the models better, you need ways where you can efficiently process this data.', 'start': 15023.785, 'duration': 8.469}, {'end': 15035.678, 'text': 'Right With more data, you will be inviting more complexity.', 'start': 15032.274, 'duration': 3.404}, {'end': 15039.041, 'text': 'with more complexity, you will be inviting more problems.', 'start': 15036.098, 'duration': 2.943}, {'end': 15042.804, 'text': "And of course, we don't want that, right? That is exactly what I'm talking about.", 'start': 15039.301, 'duration': 3.503}, {'end': 15049.89, 'text': "When you're working with Python, it gives you very elegant ways on how you can handle data, which of course, we just check out in the upcoming slide.", 'start': 15042.864, 'duration': 7.026}], 'summary': 'Efficient data processing is crucial for training models, as more data invites complexity and problems. python offers elegant solutions for data handling.', 'duration': 26.105, 'max_score': 15023.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI15023785.jpg'}, {'end': 15307.507, 'src': 'embed', 'start': 15277.368, 'weight': 0, 'content': [{'end': 15283.871, 'text': 'so the average data scientist salary really does not care for inflation around it, and this sounds funny and humorous.', 'start': 15277.368, 'duration': 6.503}, {'end': 15290.355, 'text': "but then it's amazing to see that right from 2014, which is now six years before today, uh,", 'start': 15283.871, 'duration': 6.484}, {'end': 15299.06, 'text': 'the data scientist salary has always been above 120 000 american dollars, $120,000, this has been increasing by the way.', 'start': 15290.355, 'duration': 8.705}, {'end': 15307.507, 'text': 'I have friends who are data scientists who make well around $190,000 to $200,000 per year as well.', 'start': 15299.601, 'duration': 7.906}], 'summary': 'Data scientist salaries have consistently been above $120,000, increasing over time, with some earning $190,000 to $200,000 per year.', 'duration': 30.139, 'max_score': 15277.368, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI15277368.jpg'}, {'end': 15582.455, 'src': 'embed', 'start': 15552.583, 'weight': 13, 'content': [{'end': 15555.764, 'text': "what are the packages that are available when you're working with data science?", 'start': 15552.583, 'duration': 3.181}, {'end': 15559.746, 'text': 'We already covered two or three packages in a very just before, right?', 'start': 15555.824, 'duration': 3.922}, {'end': 15562.627, 'text': "Now let's take a look at Pandas, for example.", 'start': 15560.146, 'duration': 2.481}, {'end': 15569.85, 'text': 'Pandas is a beautiful library in Python, which will help you with all of the data structures that you need,', 'start': 15562.687, 'duration': 7.163}, {'end': 15577.013, 'text': 'and you can work with data wrangling concepts, data cleaning, data hunting and, in fact, even data manipulation and data visualization.', 'start': 15569.85, 'duration': 7.163}, {'end': 15582.455, 'text': "So, basically, if I have to order up everything that I've mentioned on the screen, from data cleaning,", 'start': 15577.293, 'duration': 5.162}], 'summary': 'Pandas is a powerful python library for data wrangling, cleaning, manipulation, and visualization, covering various data science tasks.', 'duration': 29.872, 'max_score': 15552.583, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI15552583.jpg'}, {'end': 15712.854, 'src': 'embed', 'start': 15683.506, 'weight': 6, 'content': [{'end': 15688.348, 'text': 'would you like to see an excel sheet filled with thousands of rows and columns of data,', 'start': 15683.506, 'duration': 4.842}, {'end': 15693.089, 'text': 'or would you like to see one or two beautiful looking graphs summarizing all of this information?', 'start': 15688.348, 'duration': 4.741}, {'end': 15695.129, 'text': 'i am sure you guys will say the graph right.', 'start': 15693.089, 'duration': 2.04}, {'end': 15701.591, 'text': 'of course, with this you can plot amazing 2d figures, you can work with it as an object oriented api,', 'start': 15695.129, 'duration': 6.462}, {'end': 15707.833, 'text': 'you can perform very good data visualization and at the end of it, if you have to compare it to any other tool out there,', 'start': 15701.591, 'duration': 6.242}, {'end': 15712.854, 'text': 'it is very similar to the working of matlab as well, in case if you have used matlab.', 'start': 15707.833, 'duration': 5.021}], 'summary': 'Prefer beautiful graphs over thousands of rows; 2d figures, data visualization, similar to matlab.', 'duration': 29.348, 'max_score': 15683.506, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI15683506.jpg'}], 'start': 14607.788, 'title': "Python's impact in data science", 'summary': "Emphasizes python's top position in data science, its impact on ai enhancement by 44%, optimization by 42%, and decision-making by over 35%, with over 2 million job openings in 2020 and essential data science libraries like pandas, numpy, scipy, matplotlib, scikit-learn, tensorflow, and pytorch.", 'chapters': [{'end': 14904.624, 'start': 14607.788, 'title': 'Python for data science', 'summary': 'Emphasizes python as the top programming language for data science, citing its simplicity, readability, and compatibility with big data frameworks, enabling parallel computing and handling gigabytes and petabytes of data.', 'duration': 296.836, 'highlights': ["Python is the number one programming language in today's world, with a huge, powerful, open source community.", 'Python scales across multiple domains, including artificial intelligence, natural language processing, and web development.', "Python's simplicity and readability make it a clear choice for data science, offering clarity and ease of understanding for programmers.", "Python's compatibility with big data frameworks like Apache Spark, Dask, and PyDoop enables parallel computing and efficient handling of gigabytes and petabytes of data.", 'Parallel computing in Python allows tasks to be spread across multiple machines in a network, leveraging the computation power for efficient and quick processing.']}, {'end': 15192.892, 'start': 14905.766, 'title': 'Ai impact & python tools', 'summary': 'Discusses the significant impact of ai in enhancing products by 44%, optimizing operations by 42%, and improving decision-making with over 35% guarantee, while emphasizing the benefits of python in efficient data processing, vast library ecosystem, powerful data science tools, and strong community support.', 'duration': 287.126, 'highlights': ['AI enhances products by 44% and optimizes operations by 42% The chapter highlights the substantial impact of AI, indicating a 44% increase in enhancing products and up to 42% optimization in operations within organizations and firms.', 'AI improves decision-making with over 35% guarantee The discussion emphasizes the influence of AI in decision-making, stating over 35% guarantee in making better decisions when utilizing artificial intelligence.', 'Python provides efficient data processing and vast library ecosystem The significance of Python is underscored, particularly in its efficient data processing capabilities and vast library ecosystem, catering to solving a multitude of problems and supporting millions of users.', "Python offers powerful data science tools and strong community support The chapter outlines Python's strength in offering powerful data science tools like Pandas, NumPy, Seaborn, and Matplotlib, along with strong community support that facilitates quick problem-solving, garnering investments from numerous companies globally."]}, {'end': 15590.161, 'start': 15192.932, 'title': 'Python developers trends & job opportunities', 'summary': 'Discusses the current trends for python developers, highlighting over 2 million job openings in 2020, an annual addition of 500,000 jobs, and average salaries ranging from $90,000 to $140,000 in the united states and 8 lakhs to 24 lakhs per annum in india.', 'duration': 397.229, 'highlights': ['There are over 2 million job openings for Python developers in 2020. The chapter emphasizes the abundance of job opportunities for Python developers in 2020, highlighting the substantial demand in the industry.', 'An annual addition of 500,000 jobs for Python developers. The chapter underscores the consistent growth in job opportunities for Python developers by highlighting the annual increase of 500,000 jobs, indicating a thriving market.', 'The average salaries for Python developers range from over $90,000 to $140,000 in the United States and 8 lakhs to 24 lakhs per annum in India. The chapter provides detailed information on the average salaries for Python developers, illustrating the lucrative earning potential in both the United States and India.']}, {'end': 16004.359, 'start': 15590.161, 'title': 'Python data science libraries', 'summary': 'Highlights the essential python data science libraries such as pandas, numpy, scipy, matplotlib, scikit-learn, tensorflow, and pytorch, emphasizing their significance in simplifying data operations, statistical analysis, visualization, machine learning, and deep learning, ultimately facilitating the achievement of artificial intelligence goals.', 'duration': 414.198, 'highlights': ['Python data science libraries such as Pandas, NumPy, SciPy, Matplotlib, Scikit-learn, TensorFlow, and PyTorch are essential for simplifying data operations, statistical analysis, visualization, machine learning, and deep learning. These libraries play a crucial role in simplifying various data operations, statistical analysis, visualization, machine learning, and deep learning, ultimately facilitating the achievement of artificial intelligence goals.', 'Matplotlib is a beautiful library for creating visually appealing graphs and data visualizations, which are essential for conveying information effectively. Matplotlib is noted for its capability in creating visually appealing graphs and data visualizations, aiding in conveying information effectively, as humans can perceive images 3000 times faster than numerical data.', 'Scikit-learn provides machine learning algorithms for supervised learning, unsupervised learning, reinforcement learning, classification, regression, clustering, model tuning, and selection. Scikit-learn offers a wide range of machine learning algorithms for various learning types, including supervised, unsupervised, and reinforcement learning, covering classification, regression, clustering, model tuning, and selection.', 'TensorFlow and PyTorch are essential for deep learning, enabling analytics on steroids and performing tasks such as sentiment analysis, voice recognition, face recognition, and time series analysis. TensorFlow and PyTorch play a crucial role in deep learning, allowing for advanced analytics and tasks such as sentiment analysis, voice recognition, face recognition, and time series analysis.']}], 'duration': 1396.571, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI14607788.jpg', 'highlights': ['Python is the number one programming language with a powerful open source community.', 'Python scales across domains including AI, NLP, and web development.', "Python's simplicity and readability make it a clear choice for data science.", "Python's compatibility with big data frameworks enables efficient handling of large data.", 'AI enhances products by 44% and optimizes operations by 42%.', 'AI improves decision-making with over 35% guarantee.', 'Python provides efficient data processing and a vast library ecosystem.', 'Python offers powerful data science tools and strong community support.', 'Over 2 million job openings for Python developers in 2020.', 'An annual addition of 500,000 jobs for Python developers.', 'Average salaries for Python developers range from $90,000 to $140,000 in the US and 8 lakhs to 24 lakhs per annum in India.', 'Python data science libraries are essential for simplifying data operations, statistical analysis, visualization, machine learning, and deep learning.', 'Matplotlib is essential for creating visually appealing graphs and data visualizations.', 'Scikit-learn provides machine learning algorithms for various learning types.', 'TensorFlow and PyTorch are essential for deep learning and advanced analytics.']}, {'end': 17682.742, 'segs': [{'end': 16281.865, 'src': 'embed', 'start': 16256.866, 'weight': 0, 'content': [{'end': 16263.529, 'text': 'you can see that the mean sales price of a house which is being sold in all of the data that we have is $180,000..', 'start': 16256.866, 'duration': 6.663}, {'end': 16272.635, 'text': 'So for around $181,000, this forms to be our basic mean saying that this is where all the house prices might have clubbed on.', 'start': 16263.53, 'duration': 9.105}, {'end': 16275.618, 'text': 'And this is where that, you know, they are structured about.', 'start': 16272.975, 'duration': 2.643}, {'end': 16281.865, 'text': "So on an average, if you're thinking about all of these thousands of houses, this is where the house price is being sold at.", 'start': 16275.838, 'duration': 6.027}], 'summary': 'The mean sales price of houses is $180,000, forming the basic average for thousands of houses sold.', 'duration': 24.999, 'max_score': 16256.866, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI16256866.jpg'}, {'end': 16519.63, 'src': 'embed', 'start': 16491.11, 'weight': 2, 'content': [{'end': 16493.17, 'text': '70% of the time, the living area matters a lot.', 'start': 16491.11, 'duration': 2.06}, {'end': 16497.775, 'text': 'The number of cars you can park in your garage has a 64% impact.', 'start': 16493.511, 'duration': 4.264}, {'end': 16503.359, 'text': 'And you know, even the basement footing square footing of your basement adds a lot of value and people say,', 'start': 16498.235, 'duration': 5.124}, {'end': 16507.902, 'text': 'even the year it has been rebuilt was basically adding this much of correlation.', 'start': 16503.359, 'duration': 4.543}, {'end': 16510.344, 'text': "we're thinking about sales price now.", 'start': 16508.383, 'duration': 1.961}, {'end': 16516.228, 'text': "on that note, we need to hunt for linear relationships where we're trying to function it and we're trying to understand everything.", 'start': 16510.344, 'duration': 5.884}, {'end': 16519.63, 'text': 'saying this is the reason that the price is increased right.', 'start': 16516.228, 'duration': 3.402}], 'summary': 'Living area impacts pricing by 70%, garage parking by 64%, and basement square footage positively correlates with value. linear relationships are being sought to understand price increases.', 'duration': 28.52, 'max_score': 16491.11, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI16491110.jpg'}, {'end': 16570.65, 'src': 'embed', 'start': 16546.463, 'weight': 4, 'content': [{'end': 16556.242, 'text': "Well, after creating our list and after understanding that we need to find some sort of linear relationships where we'll be tracking that feature with respect to our sales price,", 'start': 16546.463, 'duration': 9.779}, {'end': 16557.083, 'text': 'then we need to understand.', 'start': 16556.242, 'duration': 0.841}, {'end': 16558.304, 'text': 'there is one important thing.', 'start': 16557.083, 'duration': 1.221}, {'end': 16566.448, 'text': 'There is a lot of cases where there is a lot of zeros, right? So let me give you an example that the garage area is zero in a couple of houses.', 'start': 16558.925, 'duration': 7.523}, {'end': 16570.65, 'text': 'So what this means is that there is no garages in that house at all.', 'start': 16566.867, 'duration': 3.783}], 'summary': 'Identifying linear relationships between features and sales price, noting presence of zeros like zero garage area.', 'duration': 24.187, 'max_score': 16546.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI16546463.jpg'}, {'end': 16765.326, 'src': 'embed', 'start': 16738.365, 'weight': 12, 'content': [{'end': 16741.508, 'text': 'but you will not get a clear understanding of what the result is.', 'start': 16738.365, 'duration': 3.143}, {'end': 16747.273, 'text': 'And now with the power of Python, with the power of simple programming concepts, as shown here.', 'start': 16742.049, 'duration': 5.224}, {'end': 16753.697, 'text': 'at the end of it, you can confidently say these are the values, and this is the list containing all of the features,', 'start': 16747.273, 'duration': 6.424}, {'end': 16757.34, 'text': 'that is basically ensuring that it has strong correlation.', 'start': 16753.697, 'duration': 3.643}, {'end': 16760.182, 'text': 'And this is the reason the house is priced the way that is.', 'start': 16757.4, 'duration': 2.782}, {'end': 16765.326, 'text': 'How cool is this and how powerful this is, right? Well, exactly my point.', 'start': 16760.682, 'duration': 4.644}], 'summary': 'Python enables clear understanding and strong correlation in pricing houses.', 'duration': 26.961, 'max_score': 16738.365, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI16738365.jpg'}, {'end': 16894.565, 'src': 'embed', 'start': 16868.833, 'weight': 9, 'content': [{'end': 16875.358, 'text': "And it's going to be really nice to understand the real time use and help of data extraction techniques as well.", 'start': 16868.833, 'duration': 6.525}, {'end': 16882.421, 'text': "So again, coming back to the definition, basically, what we're talking about is a technique, is a methodology which will help us, you know,", 'start': 16875.658, 'duration': 6.763}, {'end': 16887.162, 'text': "gather the data, and it does it in an automatic way, so that we don't have to manually gather the data.", 'start': 16882.421, 'duration': 4.741}, {'end': 16894.565, 'text': "So this part, where it's automated, is all about what we're going to discuss, because at the end of the day, if it's going to be a manual task,", 'start': 16887.442, 'duration': 7.123}], 'summary': 'Data extraction techniques automate gathering data, eliminating manual effort.', 'duration': 25.732, 'max_score': 16868.833, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI16868833.jpg'}, {'end': 17123.809, 'src': 'embed', 'start': 17096.71, 'weight': 1, 'content': [{'end': 17103.715, 'text': 'But then to give you another example, you can go on to e-commerce sites like Amazon again, Flipkart, there are many other places there.', 'start': 17096.71, 'duration': 7.005}, {'end': 17111.621, 'text': 'You can find out all the prices that each of these guys are offering for the same product and you can find out who is providing it for cheaper.', 'start': 17104.015, 'duration': 7.606}, {'end': 17115.243, 'text': 'Is it Amazon, is it Flipkart or any other case for that example as well.', 'start': 17111.661, 'duration': 3.582}, {'end': 17118.946, 'text': 'So all this information is gathered in the websites.', 'start': 17115.663, 'duration': 3.283}, {'end': 17123.809, 'text': 'And we use a term called as scraping to basically pull all of these information out.', 'start': 17119.406, 'duration': 4.403}], 'summary': 'E-commerce sites like amazon and flipkart gather and compare prices for products, utilizing scraping to extract information.', 'duration': 27.099, 'max_score': 17096.71, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI17096710.jpg'}, {'end': 17620.044, 'src': 'embed', 'start': 17590.483, 'weight': 11, 'content': [{'end': 17594.506, 'text': 'dot text and as soon as you hit enter, it will take you to their file.', 'start': 17590.483, 'duration': 4.023}, {'end': 17595.066, 'text': 'you know where.', 'start': 17594.506, 'duration': 0.56}, {'end': 17598.068, 'text': 'they will tell you what you can allow and what you cannot allow.', 'start': 17595.066, 'duration': 3.002}, {'end': 17600.969, 'text': 'so you can see disallow slash wishlist.', 'start': 17598.068, 'duration': 2.901}, {'end': 17607.453, 'text': 'so all of these pages which start with disallow means that you cannot legally access all of these as well.', 'start': 17600.969, 'duration': 6.484}, {'end': 17608.594, 'text': 'you can see allow as well.', 'start': 17607.453, 'duration': 1.141}, {'end': 17613.678, 'text': 'So basically, allow will tell you hey, you know what you can have legal access to all of these as well.', 'start': 17609.094, 'duration': 4.584}, {'end': 17615.9, 'text': 'So this is exactly what I was talking about.', 'start': 17613.778, 'duration': 2.122}, {'end': 17620.044, 'text': "But then to give you more clarity on what this means, it's not just Amazon.", 'start': 17615.94, 'duration': 4.104}], 'summary': 'Explanation of disallow and allow in robots.txt for website access.', 'duration': 29.561, 'max_score': 17590.483, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI17590483.jpg'}], 'start': 16004.599, 'title': 'House pricing data analysis', 'summary': 'Covers analyzing house pricing data, including data cleaning, identifying key features, mean sales price of $180,000, linear relationships, and the importance of web scraping for research and development.', 'chapters': [{'end': 16256.866, 'start': 16004.599, 'title': 'Data analysis for house pricing', 'summary': 'Discusses the process of analyzing a dataset for house pricing, including the various features and factors that determine the price, the need for data cleaning and pre-processing, and the removal of columns with less than 30% non-null values, with a dataset containing 1460 values for each feature.', 'duration': 252.267, 'highlights': ['The dataset contains 1460 values for each feature, raising the need for data cleaning and pre-processing to ensure meaningful analysis and removing columns with less than 30% non-null values.', 'The various features determining house pricing include lot frontage, total lot area, neighborhood characteristics, house condition and style, year built, remodeling history, heating and air conditioning types, and the presence of a basement and pool.', 'Columns with less than 30% non-null values, such as ID, alley, pool QC, fence, and miscellaneous features, are dropped as they do not add significant value to the analysis.']}, {'end': 16510.344, 'start': 16256.866, 'title': 'Analyzing house sales data', 'summary': 'Discusses the mean sales price of houses at $180,000, the process of filtering out categorical data, and identifying features with strong correlation to sales price, such as overall quality (79%), living area (70%), garage capacity (64%), and basement square footage.', 'duration': 253.478, 'highlights': ['Mean sales price of houses is $180,000 The mean sales price of houses is $180,000, forming the basic mean for all house prices.', 'Filtering out categorical data The process involves removing non-numerical data to work with a clean, elegant numerical dataset.', 'Identifying features with strong correlation to sales price Features such as overall quality (79%), living area (70%), garage capacity (64%), and basement square footage are found to have strong correlations to sales price.']}, {'end': 16983.389, 'start': 16510.344, 'title': 'Data extraction and linear relationships', 'summary': 'Discusses how to extract data using automated techniques and identifies linear relationships in the data, focusing on features that strongly correlate with house prices, while also addressing the presence of outliers and zero values.', 'duration': 473.045, 'highlights': ['Identifying features strongly correlated with house prices The chapter emphasizes identifying features like year remodeled, year built, full bathrooms, garage area, and overall quality of the house, which strongly correlate with the quoted house prices.', 'Understanding the presence of outliers and zero values in data The discussion points out that there are outliers in the data and highlights the impact of zero values, such as zero garage area, on the computation and value of the sales price.', 'Automated data extraction techniques The chapter explains the significance of automated data extraction techniques, highlighting their role in gathering data from websites, social media, and other sources, enabling effective utilization of information.']}, {'end': 17438.135, 'start': 16983.769, 'title': 'Data extraction and scraping for insights', 'summary': 'Introduces the concept of data scraping for extracting valuable information from websites, showcasing examples like sports player valuation, e-commerce price comparison, job listings consolidation, social media analysis, and market value comparison.', 'duration': 454.366, 'highlights': ['Data scraping is used to extract information from websites, such as sports player valuation, e-commerce price comparison, job listings consolidation, social media analysis, and market value comparison. The chapter explores various examples of data scraping applications, including sports player valuation, e-commerce price comparison, job listings consolidation, social media analysis, and market value comparison.', 'Scraping allows for the creation of organized data sets, enabling analysis, assessment, and application in various fields. Data scraping facilitates the creation of structured data sets, enabling analysis, assessment, and application in diverse fields like job listings, social media analysis, and market value comparison.', 'Market value comparison is a key area where data scraping provides significant value, helping users identify price differentials and make informed purchasing decisions. Data scraping is particularly valuable in market value comparison, enabling users to identify price differentials and make informed purchasing decisions, especially for high-value items.', 'Social media analysis through data scraping aids businesses in brand establishment, marketing strategy optimization, and understanding customer sentiment, contributing to overall brand success. Data scraping for social media analysis supports businesses in brand establishment, marketing strategy optimization, and understanding customer sentiment, contributing to overall brand success and customer satisfaction.', 'Data scraping for job listings consolidation simplifies the process of finding and applying for relevant openings, enhancing the job search experience for individuals in specific fields or industries. Data scraping for job listings consolidation simplifies the process of finding and applying for relevant openings, enhancing the job search experience for individuals in specific fields or industries.']}, {'end': 17682.742, 'start': 17438.476, 'title': 'Importance of web scraping for research and development', 'summary': 'Highlights the importance of web scraping for providing valuable data for research and development, emphasizing the legality of data extraction and the use of robots.txt files to determine allowed access to websites.', 'duration': 244.266, 'highlights': ['The significance of web scraping lies in providing scientists and physiologists with valuable data for analysis, offering new perspectives and clarity, which can be further utilized in artificial intelligence and machine learning applications.', 'Understanding the legality of data extraction is crucial, as websites use robots.txt files to specify allowed and disallowed access, ensuring that data is obtained through legal means.', "Websites use robots.txt files to inform crawler robots about the allowed and disallowed access, with 'disallow' indicating restricted access and 'allow' indicating legal access to specific content.", "Accessing websites' robots.txt files can provide clarity on whether data scraping is permissible, as 'disallow' prevents access to specific folders or files, while 'allow' grants legal access to particular content.", 'The chapter discusses the legal aspects of scraping data, stating that it can be done both legally and illegally, emphasizing the importance of understanding and abiding by the laws regarding data extraction.']}], 'duration': 1678.143, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI16004599.jpg', 'highlights': ['Data cleaning and pre-processing are essential due to 1460 values for each feature, removing columns with less than 30% non-null values.', 'Key features determining house pricing include lot frontage, total lot area, neighborhood characteristics, house condition and style, year built, and more.', 'Features with strong correlations to sales price include overall quality (79%), living area (70%), garage capacity (64%), and basement square footage.', 'Identifying features strongly correlated with house prices: year remodeled, year built, full bathrooms, garage area, and overall quality.', 'Automated data extraction techniques play a significant role in gathering data from websites, social media, and other sources.', 'Data scraping is used for extracting information from websites, such as sports player valuation, e-commerce price comparison, job listings consolidation, social media analysis, and market value comparison.', 'Data scraping facilitates the creation of structured data sets, enabling analysis, assessment, and application in diverse fields like job listings, social media analysis, and market value comparison.', 'Data scraping is particularly valuable in market value comparison, enabling users to identify price differentials and make informed purchasing decisions, especially for high-value items.', 'Data scraping for social media analysis supports businesses in brand establishment, marketing strategy optimization, and understanding customer sentiment, contributing to overall brand success and customer satisfaction.', 'Data scraping for job listings consolidation simplifies the process of finding and applying for relevant openings, enhancing the job search experience for individuals in specific fields or industries.', 'Web scraping provides valuable data for analysis, offering new perspectives and clarity, which can be further utilized in artificial intelligence and machine learning applications.', 'Understanding the legality of data extraction is crucial, as websites use robots.txt files to specify allowed and disallowed access, ensuring that data is obtained through legal means.', "Accessing websites' robots.txt files can provide clarity on whether data scraping is permissible, as 'disallow' prevents access to specific folders or files, while 'allow' grants legal access to particular content.", 'The chapter discusses the legal aspects of scraping data, stating that it can be done both legally and illegally, emphasizing the importance of understanding and abiding by the laws regarding data extraction.']}, {'end': 19067.031, 'segs': [{'end': 17798.949, 'src': 'embed', 'start': 17759.483, 'weight': 1, 'content': [{'end': 17761.925, 'text': 'So point number two is something very important.', 'start': 17759.483, 'duration': 2.442}, {'end': 17764.588, 'text': 'Point number two is what makes Python what it is.', 'start': 17762.005, 'duration': 2.583}, {'end': 17768.552, 'text': 'It is the number of libraries that Python supports.', 'start': 17764.768, 'duration': 3.784}, {'end': 17772.034, 'text': 'See, for example, in the world of data extraction.', 'start': 17768.792, 'duration': 3.242}, {'end': 17781, 'text': 'we will perform computations, we will perform data storage methodologies and we will go on to visualize this data at the end as well.', 'start': 17772.034, 'duration': 8.966}, {'end': 17788.124, 'text': 'So for numerical computations, you have libraries such as Panda and NumPy, which will perform any computation on the data that you have extracted.', 'start': 17781.3, 'duration': 6.824}, {'end': 17795.208, 'text': 'And for data visualization, we have tools such as seaborn, matplotlib and much more, which will basically help you,', 'start': 17788.464, 'duration': 6.744}, {'end': 17798.949, 'text': "even after you've achieved the results of data extraction as well.", 'start': 17795.208, 'duration': 3.741}], 'summary': "Python's strength lies in its numerous libraries, including panda, numpy, seaborn, and matplotlib, which enable data extraction, computation, storage, and visualization.", 'duration': 39.466, 'max_score': 17759.483, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI17759483.jpg'}, {'end': 18031.689, 'src': 'embed', 'start': 17999.511, 'weight': 2, 'content': [{'end': 18004.733, 'text': "so when you perform web testing, when you perform activities where you're, you know pulling the data continuously,", 'start': 17999.511, 'duration': 5.222}, {'end': 18011.216, 'text': 'you have to go on to test to see if your data is being pulled, right to analyze your data to, you know,', 'start': 18004.733, 'duration': 6.483}, {'end': 18013.878, 'text': 'automate this process of verification and validation.', 'start': 18011.216, 'duration': 2.662}, {'end': 18017.099, 'text': 'so selenium is used right after beautiful suit for that.', 'start': 18013.878, 'duration': 3.221}, {'end': 18018.36, 'text': "and then there's any tree.", 'start': 18017.099, 'duration': 1.261}, {'end': 18023.862, 'text': 'any tree is basically, you know, helping you create a tree data structure which is used for data extraction.', 'start': 18018.36, 'duration': 5.502}, {'end': 18031.689, 'text': 'So a tree data structure, at the end of the day, will basically make sure that your data is organized and it can be pulled in a structured way.', 'start': 18024.062, 'duration': 7.627}], 'summary': 'Selenium and anytree automate web testing and data extraction for organized and structured data.', 'duration': 32.178, 'max_score': 17999.511, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI17999511.jpg'}, {'end': 18241.044, 'src': 'embed', 'start': 18215.444, 'weight': 4, 'content': [{'end': 18222.248, 'text': "So again, when we're talking about how important is a data set to solve a problem, let's see if we can, you know,", 'start': 18215.444, 'duration': 6.804}, {'end': 18224.77, 'text': 'bring machine learning together and medicine together.', 'start': 18222.248, 'duration': 2.522}, {'end': 18227.872, 'text': 'so we have a comprehensive video on the channel which will, uh,', 'start': 18224.77, 'duration': 3.102}, {'end': 18232.917, 'text': 'which will guide you on how machine learning is basically used to fight against code 19 as well.', 'start': 18227.872, 'duration': 5.045}, {'end': 18235.359, 'text': 'so make sure you check that out after this video.', 'start': 18232.917, 'duration': 2.442}, {'end': 18241.044, 'text': 'so, coming back to how important the data set is, the first thing your data set can help in this particular case.', 'start': 18235.359, 'duration': 5.685}], 'summary': 'Data set is crucial in using machine learning for fighting covid-19.', 'duration': 25.6, 'max_score': 18215.444, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI18215444.jpg'}, {'end': 18298.658, 'src': 'embed', 'start': 18269.461, 'weight': 7, 'content': [{'end': 18271.702, 'text': 'probably this is the most important thing.', 'start': 18269.461, 'duration': 2.241}, {'end': 18279.167, 'text': 'This is the most widely asked thing right now to see if computers can help fight against the virus.', 'start': 18272.263, 'duration': 6.904}, {'end': 18281.488, 'text': 'And of course, computers are doing everything they can.', 'start': 18279.187, 'duration': 2.301}, {'end': 18286.631, 'text': 'There are experts out there who are bringing together the world of IT and medicine just for this cause.', 'start': 18281.528, 'duration': 5.103}, {'end': 18292.855, 'text': 'So if computers can help scientists find a cure for this particular disease, it would help.', 'start': 18286.931, 'duration': 5.924}, {'end': 18298.658, 'text': 'And again, if we take a quick backtrack, of course, medicine in all of its glory will be the one finding the cure.', 'start': 18293.255, 'duration': 5.403}], 'summary': 'Computers are widely asked to help fight the virus, experts are collaborating for this cause, aiding in finding a cure.', 'duration': 29.197, 'max_score': 18269.461, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI18269461.jpg'}, {'end': 18575.138, 'src': 'embed', 'start': 18549.231, 'weight': 5, 'content': [{'end': 18555.073, 'text': 'So to do that, to pull the data, to pull all the HTML data and to analyze and understand it, we require requests.', 'start': 18549.231, 'duration': 5.842}, {'end': 18557.494, 'text': 'and we have a library called as lxml as well.', 'start': 18555.473, 'duration': 2.021}, {'end': 18565.096, 'text': 'so lxml again will help you parse, uh, your xml data and pretty much make it understandable and make it usable for further down the line.', 'start': 18557.494, 'duration': 7.602}, {'end': 18571.497, 'text': "so you can execute, uh, these code statements that are in the comments in case if you're using any other environment as well.", 'start': 18565.096, 'duration': 6.401}, {'end': 18575.138, 'text': 'so coming to google collab, basically, google collab has all of it covered for us.', 'start': 18571.497, 'duration': 3.641}], 'summary': 'Using requests and lxml to pull and analyze html data, while google colab provides comprehensive support.', 'duration': 25.907, 'max_score': 18549.231, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI18549231.jpg'}, {'end': 19054.778, 'src': 'embed', 'start': 19031.765, 'weight': 0, 'content': [{'end': 19038.773, 'text': 'as you can see on the right hand side of your screen, you can choose to pick up how many ever details that you have to show on your screen as well.', 'start': 19031.765, 'duration': 7.008}, {'end': 19040.133, 'text': 'So here as well.', 'start': 19039.133, 'duration': 1}, {'end': 19047.696, 'text': 'you know all the countries, all the total cases, the new cases from yesterday, the total number of deaths in that country, new deaths,', 'start': 19040.133, 'duration': 7.563}, {'end': 19049.136, 'text': 'total recovered and much more.', 'start': 19047.696, 'duration': 1.44}, {'end': 19053.657, 'text': "So this detail, you know, we've picked up from the worldometer side.", 'start': 19049.176, 'duration': 4.481}, {'end': 19054.778, 'text': 'What are the active cases?', 'start': 19053.697, 'duration': 1.081}], 'summary': 'View various covid-19 statistics by country on screen, including total cases, new cases, deaths, and recoveries.', 'duration': 23.013, 'max_score': 19031.765, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI19031765.jpg'}], 'start': 17682.782, 'title': 'Python, libraries, ai tracking, and data cleaning for global data', 'summary': 'Covers python for data extraction, python libraries such as beautiful soup, selenium, and anytree, ai tracking of infectious diseases using blue dot, and data cleaning and analysis for global data, including csv file creation.', 'chapters': [{'end': 17939.314, 'start': 17682.782, 'title': 'Python for data extraction', 'summary': 'Discusses the importance of understanding robots.txt before web scraping, the ease of use of python, the extensive library support for data extraction, and the strong and supportive open-source community surrounding python.', 'duration': 256.532, 'highlights': ["Python's ease of use is attributed to its readability, simple syntax, and minimal need for training, making it a popular choice over other programming languages.", "Python's extensive library support for data extraction includes libraries such as Pandas, NumPy, seaborn, and matplotlib, enabling various computations, data storage methodologies, and data visualization.", 'The small and concise nature of Python code allows for significant results with minimal coding, making it an important aspect for data extraction.', 'The strong and supportive open-source community surrounding Python fosters collaboration, assistance, and development in various concepts including data extraction.']}, {'end': 18322.69, 'start': 17939.314, 'title': 'Python libraries for data extraction', 'summary': 'Discusses the importance of data extraction and the top three most used python libraries, highlighting beautiful soup, selenium, and anytree, and emphasizes the critical role of data sets in solving problems, particularly in the context of covid-19, linking data extraction to the potential impact on medicine and machine learning.', 'duration': 383.376, 'highlights': ['The chapter discusses the importance of data extraction and the top three most used Python libraries, highlighting Beautiful Soup, Selenium, and AnyTree. It emphasizes the significance of these libraries for data extraction.', 'Emphasizes the critical role of data sets in solving problems, particularly in the context of COVID-19, linking data extraction to the potential impact on medicine and machine learning. It stresses the crucial role of data sets in problem-solving, particularly in the context of COVID-19, and their potential impact on medicine and machine learning.', 'The data sets can help in tracing the outbreak, diagnosing patients, disinfecting areas, and finding a cure for diseases like COVID-19. It highlights the various ways in which data sets can contribute to addressing the COVID-19 outbreak, including tracing the outbreak, diagnosing patients, disinfecting areas, and finding a cure.', 'The chapter also mentions the potential impact of machine learning on fighting against COVID-19 and how computers can aid in speeding up the process in the field of medicine. It discusses the potential impact of machine learning in combating COVID-19 and the role of computers in speeding up processes in the field of medicine.']}, {'end': 18719.668, 'start': 18322.99, 'title': 'Ai tracking of infectious diseases', 'summary': 'Showcases how blue dot, an ai platform, detected an unusual rise in pneumonia cases in wuhan, china, nine days before the world health organization identified the new virus, highlighting the effectiveness of ai in early detection of outbreaks and the potential for ai and machine learning to revolutionize medical research and data extraction.', 'duration': 396.678, 'highlights': ['Blue Dot AI platform detected unusual rise in pneumonia cases in Wuhan, China, nine days before World Health Organization identified the new virus Blue Dot platform identified an unusual rise in pneumonia cases in Wuhan, China, leading to the detection of a new virus by World Health Organization, showcasing the early detection capability of AI in tracking infectious diseases.', 'April 2020: 100,000 deaths, 2.4 million cases due to the new virus By April 2020, there were 100,000 deaths and 2.4 million cases attributed to the new virus, emphasizing the significant impact of the outbreak and the need for early detection and intervention.', 'Potential for AI and machine learning to revolutionize medical research and data extraction The speaker highlights the potential of AI and machine learning to accelerate medical research and data extraction, emphasizing the role of technology in addressing global health challenges.', 'Data extraction from various sources for COVID-19 analysis: satellites, news, flight data, health organizations, and hospitals The availability of data from multiple sources, including satellites, news, flight records, and health organizations, highlights the diverse data points used for COVID-19 analysis and outbreak tracking.', 'Utilizing Python for data extraction and web scraping in COVID-19 dataset creation The usage of Python for data extraction and web scraping in creating COVID-19 datasets demonstrates the practical application of programming languages in gathering and analyzing real-time data for health-related research.']}, {'end': 19067.031, 'start': 18719.668, 'title': 'Data cleaning and analysis for global data', 'summary': 'Outlines the process of cleaning and analyzing global data, including steps to find data for all continents, creating a result list, and writing the data into a csv file.', 'duration': 347.363, 'highlights': ['Step 11: Writing results to CSV file The process of writing the actual results to the CSV file, pushing all the data from the result list into the file, ensuring data is saved, and explaining the file location.', "Step 9: Adding data to rows and result list The step involves finding and adding all the required data to a list called 'row', checking for existing data, and appending the rows to the result list.", 'Step 7: Creation of result list Creating a temporary result list to hold all the data before writing it into a CSV file, adding all the headings to the list, and preparing for the final output.', 'Step 4: Cleaning the data and getting headings The process focuses on cleaning the data and extracting headings for the columns, ensuring data is ready for further use.', 'Step 10: Preparing for storage and writing data to CSV file The step involves creating and opening a file in write mode, writing the obtained results into the CSV file, and ensuring the file is closed to save the data.']}], 'duration': 1384.249, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI17682782.jpg', 'highlights': ["Python's extensive library support for data extraction includes libraries such as Pandas, NumPy, seaborn, and matplotlib, enabling various computations, data storage methodologies, and data visualization.", 'The small and concise nature of Python code allows for significant results with minimal coding, making it an important aspect for data extraction.', 'The chapter discusses the importance of data extraction and the top three most used Python libraries, highlighting Beautiful Soup, Selenium, and AnyTree.', 'The data sets can help in tracing the outbreak, diagnosing patients, disinfecting areas, and finding a cure for diseases like COVID-19.', 'Blue Dot AI platform detected unusual rise in pneumonia cases in Wuhan, China, nine days before World Health Organization identified the new virus.', 'April 2020: 100,000 deaths, 2.4 million cases due to the new virus.', 'Utilizing Python for data extraction and web scraping in COVID-19 dataset creation.', 'Step 11: Writing results to CSV file.', 'Step 9: Adding data to rows and result list.', 'Step 7: Creation of result list.']}, {'end': 20250.378, 'segs': [{'end': 19116.47, 'src': 'embed', 'start': 19087.325, 'weight': 9, 'content': [{'end': 19091.289, 'text': "Once you have structured data and once you've created structured files like this,", 'start': 19087.325, 'duration': 3.964}, {'end': 19095.413, 'text': 'you can understand that you know you can leverage this and make full use out of it down the ladder.', 'start': 19091.289, 'duration': 4.124}, {'end': 19098.216, 'text': "Down the ladder, it's basically performing analysis.", 'start': 19095.774, 'duration': 2.442}, {'end': 19102.16, 'text': 'It can be, you know, working with deep learning, working with machine learning, whatever it is.', 'start': 19098.336, 'duration': 3.824}, {'end': 19108.925, 'text': 'Step one is very much essential in all of these fields of processes, and step one is creation of data,', 'start': 19102.88, 'duration': 6.045}, {'end': 19116.47, 'text': 'And you know we have created a structured pipeline to access our data as well, and this again, in my opinion,', 'start': 19108.925, 'duration': 7.545}], 'summary': 'Structured data enables data leverage for analysis and machine learning.', 'duration': 29.145, 'max_score': 19087.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI19087325.jpg'}, {'end': 19139.558, 'src': 'embed', 'start': 19116.47, 'weight': 3, 'content': [{'end': 19129.66, 'text': "is one of the most important use cases or Uses of data extraction and you're working with Python as well How to import a data set using pandas and how to analyze various attributes of the data,", 'start': 19116.47, 'duration': 13.19}, {'end': 19131.822, 'text': 'how we can import a data set using pandas.', 'start': 19129.66, 'duration': 2.162}, {'end': 19137.878, 'text': 'and So, for importing the data set, the very first thing that you will be doing is importing the pandas library.', 'start': 19131.822, 'duration': 6.056}, {'end': 19139.558, 'text': "So I'm importing pandas as pd.", 'start': 19137.958, 'duration': 1.6}], 'summary': 'Learn how to import and analyze data using python and pandas.', 'duration': 23.088, 'max_score': 19116.47, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI19116470.jpg'}, {'end': 19562.003, 'src': 'embed', 'start': 19534.743, 'weight': 4, 'content': [{'end': 19541.567, 'text': 'for example, we had serial number previously right, but the serial number was not adding any value to our analysis part.', 'start': 19534.743, 'duration': 6.824}, {'end': 19547.191, 'text': 'so for the further processing of the data we can avoid or we can drop the column of serial number.', 'start': 19541.567, 'duration': 5.624}, {'end': 19549.453, 'text': 'okay, so how we can drop any column?', 'start': 19547.191, 'duration': 2.262}, {'end': 19557.039, 'text': 'so for dropping any column, what we have to do is data frame dot drop and inside that, uh, we have to mention the name of our column,', 'start': 19549.453, 'duration': 7.586}, {'end': 19562.003, 'text': 'just like this cars equal, cars dot drop and inside this columns equal.', 'start': 19557.039, 'duration': 4.964}], 'summary': 'Serial number adds no value, drop column using data frame dot drop method.', 'duration': 27.26, 'max_score': 19534.743, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI19534743.jpg'}, {'end': 19603.302, 'src': 'embed', 'start': 19577.987, 'weight': 5, 'content': [{'end': 19583.991, 'text': 'Well, this correlation matrix will help you to find how a variable is correlated to other variable.', 'start': 19577.987, 'duration': 6.004}, {'end': 19590.276, 'text': 'So if the value of correlation is high, that means the two variables are highly correlated to each other.', 'start': 19584.392, 'duration': 5.884}, {'end': 19595.96, 'text': 'That is changing one variable by a factor of X changes other variable by a factor of Y.', 'start': 19590.336, 'duration': 5.624}, {'end': 19600.342, 'text': 'okay. so here, as a output, we got a correlation matrix right.', 'start': 19596.841, 'duration': 3.501}, {'end': 19603.302, 'text': 'so from here you can see that cylinder to cylinder.', 'start': 19600.342, 'duration': 2.96}], 'summary': 'Correlation matrix identifies strong relationships between variables, aiding in analysis.', 'duration': 25.315, 'max_score': 19577.987, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI19577987.jpg'}, {'end': 20250.378, 'src': 'embed', 'start': 20173.669, 'weight': 0, 'content': [{'end': 20175.971, 'text': 'So for that, we added this condition over here.', 'start': 20173.669, 'duration': 2.302}, {'end': 20176.351, 'text': "That's it.", 'start': 20176.011, 'duration': 0.34}, {'end': 20182.877, 'text': 'That is cars of cylinder is greater than 6 and cars of HP that is horsepower is greater than 300.', 'start': 20176.775, 'duration': 6.102}, {'end': 20185.377, 'text': 'So we are using and operator over here.', 'start': 20182.877, 'duration': 2.5}, {'end': 20190.099, 'text': 'Okay So this will give me a true result only if both the parameters are true.', 'start': 20185.938, 'duration': 4.161}, {'end': 20194.62, 'text': 'And again same we are filtering the data frame and displaying the filter data.', 'start': 20190.559, 'duration': 4.061}, {'end': 20202.563, 'text': 'So, as you can see, here, we have only one model, Maserati Bora, which is having a horsepower of 35,, which is greater than 300,', 'start': 20195.16, 'duration': 7.403}, {'end': 20206.625, 'text': 'and number of cylinders as 8,, which is again greater than 6..', 'start': 20202.563, 'duration': 4.062}, {'end': 20208.446, 'text': 'So both our conditions are true.', 'start': 20206.625, 'duration': 1.821}, {'end': 20210.607, 'text': "So there's our filter data over here.", 'start': 20209.006, 'duration': 1.601}, {'end': 20217.61, 'text': 'Yeah, so this is our first task, data manipulation.', 'start': 20214.328, 'duration': 3.282}, {'end': 20222.672, 'text': "And to do this, let's go ahead and actually import all of our libraries.", 'start': 20218.37, 'duration': 4.302}, {'end': 20227.183, 'text': "So I'll just type in import Pandas as PD.", 'start': 20224.181, 'duration': 3.002}, {'end': 20230.085, 'text': "And then I'll also load the NumPy library.", 'start': 20227.863, 'duration': 2.222}, {'end': 20232.486, 'text': "So I'll type in import NumPy as NP.", 'start': 20230.145, 'duration': 2.341}, {'end': 20235.328, 'text': "I'd also need the matplotlib library.", 'start': 20233.347, 'duration': 1.981}, {'end': 20243.614, 'text': "So I'll type in from matplotlib import by plot as PLT.", 'start': 20235.528, 'duration': 8.086}, {'end': 20246.696, 'text': 'So these are all of my required libraries.', 'start': 20244.634, 'duration': 2.062}, {'end': 20249.137, 'text': "I'll just wait till these libraries are loaded.", 'start': 20247.096, 'duration': 2.041}, {'end': 20250.378, 'text': 'Right, this is done.', 'start': 20249.637, 'duration': 0.741}], 'summary': 'Filter data based on cars with cylinders > 6 and horsepower > 300, resulting in one model, maserati bora.', 'duration': 76.709, 'max_score': 20173.669, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI20173669.jpg'}], 'start': 19067.031, 'title': 'Importance of structured data and data analysis in python', 'summary': 'Emphasizes the significance of structured data in deep learning and machine learning, and covers data analysis with pandas in python including various statistical insights. it also provides a comprehensive guide to data cleaning and manipulation in python, including techniques such as renaming columns, handling null values, and dataset indexing.', 'chapters': [{'end': 19116.47, 'start': 19067.031, 'title': 'Importance of creating structured data', 'summary': 'Emphasizes the importance of creating structured data for performing analysis in fields like deep learning and machine learning, which is essential in processes and can be applied beyond covid-19.', 'duration': 49.439, 'highlights': ['Creating structured data is crucial for performing analysis in fields like deep learning and machine learning, and it is essential in various processes beyond COVID-19.', 'Structured data sets facilitate analysis and can be leveraged for various purposes down the line, including deep learning and machine learning.', 'Step one in the process is the creation of structured data, which is a crucial aspect in accessing and utilizing data for analysis.']}, {'end': 19399.82, 'start': 19116.47, 'title': 'Data analysis with pandas in python', 'summary': 'Covers the process of importing a data set using pandas in python, analyzing the attributes of the data set including type, head, tail, shape, info, mean, median, standard deviation, maximum, minimum, count, and describe, providing insights and quantitative data to understand the data structure and statistics.', 'duration': 283.35, 'highlights': ['Viewing Data Structure and Summary Statistics The process of analyzing the data set includes examining the type, head, tail, shape, info, mean, median, standard deviation, maximum, minimum, count, and describe functions, providing comprehensive insights into the structure and statistics of the data set.', 'Data Frame Description The data frame describe function provides a descriptive statistics summary, including count, mean, standard deviation, minimum, maximum, 25th percentile, 50th percentile, and 75th percentile values for all columns, aiding in understanding the distribution and characteristics of the data set.', "Data Frame Shape and Info The data frame shape function reveals the total number of rows and columns in the data set, while the info function displays the data type of each column and the number of non-null values, offering a comprehensive overview of the data set's dimensions and completeness."]}, {'end': 20250.378, 'start': 19399.82, 'title': 'Data cleaning and manipulating dataset in python', 'summary': 'Provides a comprehensive guide to data cleaning and manipulation in python, covering techniques such as renaming columns, handling null values, dropping unwanted columns, finding correlation matrix, changing data types, and manipulating dataset indexing by position, setting values, applying functions, sorting data, and filtering data based on specified conditions.', 'duration': 850.558, 'highlights': ['The chapter provides a comprehensive guide to data cleaning and manipulation in Python. The chapter covers techniques such as renaming columns, handling null values, dropping unwanted columns, finding correlation matrix, changing data types, and manipulating dataset indexing by position, setting values, applying functions, sorting data, and filtering data based on specified conditions.', 'The process of data cleaning involves detecting, correcting, or removing corrupt or inaccurate records from a dataset table or any database. Data cleaning or data cleansing is a process of detecting, correcting, or removing corrupt or inaccurate records from a dataset table or any database.', 'Renaming columns, handling null values, and dropping unwanted columns are essential steps in cleaning the data. Techniques such as renaming columns, handling null values, and dropping unwanted columns are essential steps in cleaning the data.', 'Replacing missing data with the mean value can aid in better data analysis. Replacing the missing data with the mean value will help in analyzing the data in a better way.', 'Finding correlation matrix helps in understanding the relationship between variables, aiding in decision-making for problem-solving. The correlation matrix helps in understanding the relationship between variables and aids in decision-making for problem-solving.']}], 'duration': 1183.347, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI19067031.jpg', 'highlights': ['Structured data is crucial for analysis in deep learning and machine learning, and it is essential in various processes beyond COVID-19.', 'Creating structured data is a crucial aspect in accessing and utilizing data for analysis.', 'Structured data sets facilitate analysis and can be leveraged for various purposes including deep learning and machine learning.', 'The process of analyzing the data set includes examining the type, head, tail, shape, info, mean, median, standard deviation, maximum, minimum, count, and describe functions, providing comprehensive insights into the structure and statistics of the data set.', 'The data frame describe function provides a descriptive statistics summary, aiding in understanding the distribution and characteristics of the data set.', "The data frame shape function reveals the total number of rows and columns in the data set, offering a comprehensive overview of the data set's dimensions and completeness.", 'The chapter provides a comprehensive guide to data cleaning and manipulation in Python, covering techniques such as renaming columns, handling null values, dropping unwanted columns, finding correlation matrix, changing data types, and manipulating dataset indexing.', 'Data cleaning involves detecting, correcting, or removing corrupt or inaccurate records from a dataset table or any database.', 'Renaming columns, handling null values, and dropping unwanted columns are essential steps in cleaning the data.', 'Replacing missing data with the mean value can aid in better data analysis.', 'Finding correlation matrix helps in understanding the relationship between variables, aiding in decision-making for problem-solving.']}, {'end': 21775.466, 'segs': [{'end': 20382.758, 'src': 'embed', 'start': 20353.408, 'weight': 5, 'content': [{'end': 20356.029, 'text': 'After this, we have the type of payment method.', 'start': 20353.408, 'duration': 2.621}, {'end': 20361.531, 'text': 'So the type of payment method could be these electronic check, mail check, bank transfer, and so on.', 'start': 20356.149, 'duration': 5.382}, {'end': 20366.893, 'text': 'And then these are the monthly charges and total charges incurred by the customer.', 'start': 20362.371, 'duration': 4.522}, {'end': 20372.134, 'text': "All right, so now let's start off with our data manipulation tasks.", 'start': 20367.873, 'duration': 4.261}, {'end': 20374.435, 'text': 'So this is our very simple task.', 'start': 20372.374, 'duration': 2.061}, {'end': 20378.517, 'text': 'We just have to extract some individual columns from the entire data frame.', 'start': 20374.475, 'duration': 4.042}, {'end': 20382.758, 'text': "We'll have to extract the fifth column and store it in customer five.", 'start': 20378.917, 'duration': 3.841}], 'summary': 'Data includes payment methods, monthly charges, and manipulation tasks.', 'duration': 29.35, 'max_score': 20353.408, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI20353408.jpg'}, {'end': 20428.582, 'src': 'embed', 'start': 20401.008, 'weight': 2, 'content': [{'end': 20405.632, 'text': 'So zero, one, two, three, and four, right? So this would be my fifth column over here.', 'start': 20401.008, 'duration': 4.624}, {'end': 20408.274, 'text': 'And I will store this in.', 'start': 20406.613, 'duration': 1.661}, {'end': 20412.638, 'text': "Let's say C underscore five.", 'start': 20410.396, 'duration': 2.242}, {'end': 20420.955, 'text': "and I'll have a glance at the head of this, C underscore phi dot head.", 'start': 20413.849, 'duration': 7.106}, {'end': 20428.582, 'text': 'All right, so we have successfully extracted the fifth column from this entire data frame, and this is the head of it.', 'start': 20420.995, 'duration': 7.587}], 'summary': 'Extracted fifth column from data frame, c_5, with 5 elements.', 'duration': 27.574, 'max_score': 20401.008, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI20401008.jpg'}, {'end': 20508.231, 'src': 'embed', 'start': 20481.943, 'weight': 4, 'content': [{'end': 20487.824, 'text': 'The first condition is the gender of the customer needs to be male second condition is senior citizen.', 'start': 20481.943, 'duration': 5.881}, {'end': 20494.765, 'text': 'The value of senior citizen needs to be equal to one and the third condition is the payment method needs to be equal to electronic check.', 'start': 20487.884, 'duration': 6.881}, {'end': 20497.565, 'text': "So I'll given all of these three conditions over here.", 'start': 20495.445, 'duration': 2.12}, {'end': 20501.586, 'text': "I'll start off by giving the first condition steamer churn.", 'start': 20498.065, 'duration': 3.521}, {'end': 20508.231, 'text': "I'll type in gender and the gender needs to be equal to me right?", 'start': 20502.489, 'duration': 5.742}], 'summary': 'Target male senior citizens using electronic check for payment.', 'duration': 26.288, 'max_score': 20481.943, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI20481943.jpg'}, {'end': 21180.993, 'src': 'embed', 'start': 21152.318, 'weight': 3, 'content': [{'end': 21156.46, 'text': "And if I want all of the values, I'll just remove the method keys from there, right?", 'start': 21152.318, 'duration': 4.142}, {'end': 21163.683, 'text': 'So this over here would give me all of the values present with respect to this internet service column, right?', 'start': 21156.9, 'duration': 6.783}, {'end': 21170.886, 'text': 'So fiber optic, or in other words, the number of customers whose internet services fiber optic is 3096,', 'start': 21164.341, 'duration': 6.545}, {'end': 21180.993, 'text': "number of customers whose internet services ESL is 2421 and number of customers who don't avail the internet service are 1526, right now again.", 'start': 21170.886, 'duration': 10.107}], 'summary': 'The number of customers with fiber optic internet service is 3096, esl is 2421, and those without internet service are 1526.', 'duration': 28.675, 'max_score': 21152.318, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI21152318.jpg'}, {'end': 21731.472, 'src': 'embed', 'start': 21708.05, 'weight': 0, 'content': [{'end': 21714.676, 'text': 'Similarly, if the contract of the customer is one year, then the median tenure of the customer would be around 45 months.', 'start': 21708.05, 'duration': 6.626}, {'end': 21722.002, 'text': 'And then if the contract of the customer is month to month, then the median tenure of the customer would be around 15 odd months.', 'start': 21716.135, 'duration': 5.867}, {'end': 21728.068, 'text': 'Right, so these were all of the examples of visualization.', 'start': 21723.884, 'duration': 4.184}, {'end': 21731.472, 'text': "Now it's finally time to head on to machine learning.", 'start': 21728.609, 'duration': 2.863}], 'summary': 'Median customer tenure: 45 months for 1-year contract, 15 months for month-to-month.', 'duration': 23.422, 'max_score': 21708.05, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI21708050.jpg'}], 'start': 20251.241, 'title': 'Loading and manipulating customer churn data', 'summary': 'Covers loading customer churn data, extracting specific columns, performing data manipulation tasks, creating bar plots and histograms, and utilizing various data visualization techniques to provide insights into the dataset.', 'chapters': [{'end': 20298.748, 'start': 20251.241, 'title': 'Loading customer churn data', 'summary': "Covers the process of loading the customer churn data frame, storing it into an object named 'customer churn', and previewing the dataset, which contains the unique customer id column.", 'duration': 47.507, 'highlights': ["The data frame is loaded using pd.read_csv with the file name 'customer churn dot csv'.", "The loaded file is stored in a new object named 'customer churn'.", "The dataset is previewed using 'customer churn.head' to inspect the unique customer ID column."]}, {'end': 21035.529, 'start': 20298.748, 'title': 'Data manipulation and extraction', 'summary': "Covers basic data manipulation tasks such as extracting specific columns, performing data extraction based on conditions, random sampling, and obtaining the count of different levels from the 'churn' column in the customer churn dataset.", 'duration': 736.781, 'highlights': ["Extracting specific columns from the data frame The speaker demonstrates the extraction of the fifth and fifteenth columns from the dataset, storing them in 'customer 5' and 'customer 15' respectively.", "Data extraction based on conditions The process involves extracting male senior citizens using electronic check payment method, customers with tenure greater than 70 months or monthly charges exceeding $100, and customers with a two-year contract, payment method as mail check, and churn value as 'yes'.", "Random sampling The lecturer illustrates the process of randomly sampling 333 records from the dataset using the 'sample' method, emphasizing the variability in the sampled records upon repeated executions.", "Obtaining count of different levels from categorical column The method 'value_counts' is utilized to obtain the count of customers who will and will not be churning out, along with counts for different contract types in the dataset."]}, {'end': 21434.414, 'start': 21038.852, 'title': 'Creating bar plot and histogram', 'summary': 'Demonstrates creating a bar plot to display the distribution of internet service with counts for fiber optic, dsl, and non-availment, and then building a histogram to visualize the tenure distribution of customers, showcasing peaks at short and long tenures with a majority in the range of 20-60 months.', 'duration': 395.562, 'highlights': ['Creating a bar plot to display the distribution of internet service with counts for fiber optic, DSL, and non-availment The bar plot visually presents the distribution of internet service with counts, showing 3096 customers with fiber optic, 2421 with DSL, and 1526 not availing the service.', 'Building a histogram to visualize the tenure distribution of customers The histogram illustrates the tenure distribution, indicating a peak at short tenures (around 800 customers with less than a month) and another peak at long tenures (more than 70 months), with the majority of customers having tenures in the range of 20-60 months.']}, {'end': 21775.466, 'start': 21436.575, 'title': 'Data visualization techniques', 'summary': 'Covers creating bar plots, histograms, scatter plots, and box plots to visualize the distribution and relationships between different columns in the data set, providing insights such as the median tenure based on different contract types.', 'duration': 338.891, 'highlights': ['The chapter covers creating a scatter plot between monthly charges and tenure, with tenure on the x-axis and monthly charges on the y-axis.', 'It explains the difference between bar plots and histograms, indicating that bar plots are used for categorical columns, while histograms are used for continuous numerical columns.', 'The chapter demonstrates creating a box plot between the tenure column and the contract column, revealing insights such as the median tenure for different contract types, with the highest median tenure for a two-year contract and the lowest for a month-to-month contract.']}], 'duration': 1524.225, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI20251241.jpg', 'highlights': ['Creating a bar plot to display the distribution of internet service with counts for fiber optic, DSL, and non-availment', 'Building a histogram to visualize the tenure distribution of customers', 'The chapter demonstrates creating a box plot between the tenure column and the contract column, revealing insights such as the median tenure for different contract types, with the highest median tenure for a two-year contract and the lowest for a month-to-month contract', 'The chapter covers creating a scatter plot between monthly charges and tenure, with tenure on the x-axis and monthly charges on the y-axis', "Data extraction based on conditions The process involves extracting male senior citizens using electronic check payment method, customers with tenure greater than 70 months or monthly charges exceeding $100, and customers with a two-year contract, payment method as mail check, and churn value as 'yes'", "Random sampling The lecturer illustrates the process of randomly sampling 333 records from the dataset using the 'sample' method, emphasizing the variability in the sampled records upon repeated executions", "Obtaining count of different levels from categorical column The method 'value_counts' is utilized to obtain the count of customers who will and will not be churning out, along with counts for different contract types in the dataset", "The data frame is loaded using pd.read_csv with the file name 'customer churn dot csv'", "The loaded file is stored in a new object named 'customer churn'", "The dataset is previewed using 'customer churn.head' to inspect the unique customer ID column"]}, {'end': 23372.074, 'segs': [{'end': 21805.398, 'src': 'embed', 'start': 21775.466, 'weight': 2, 'content': [{'end': 21784.541, 'text': "we'll start off with our first machine learning algorithm, which would be linear regression over here and planar regression, as you already know.", 'start': 21775.466, 'duration': 9.075}, {'end': 21789.766, 'text': 'so over here, though, a dependent variable would be a numerical column,', 'start': 21784.541, 'duration': 5.225}, {'end': 21795.43, 'text': "and you're basically trying to understand how does one variable change with respect to another variable?", 'start': 21789.766, 'duration': 5.664}, {'end': 21805.398, 'text': "and over here we'd have to build a simple linear model where our dependent variable is monthly charges and the independent variable is equal to tenure.", 'start': 21795.43, 'duration': 9.968}], 'summary': 'Introduction to linear regression for understanding variable relationships.', 'duration': 29.932, 'max_score': 21775.466, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI21775466.jpg'}, {'end': 22550.927, 'src': 'embed', 'start': 22520.754, 'weight': 0, 'content': [{'end': 22529.458, 'text': "So let's see, I'll put in y print and let's say I'll have a glance to the first five values, right? So these are the monthly charges predicted.", 'start': 22520.754, 'duration': 8.704}, {'end': 22534.6, 'text': 'Let me also show you why.', 'start': 22529.478, 'duration': 5.122}, {'end': 22540.563, 'text': 'this five, right?', 'start': 22534.6, 'duration': 5.963}, {'end': 22546.826, 'text': 'So these are the actual values and these are the predicted values over here, right?', 'start': 22541.383, 'duration': 5.443}, {'end': 22550.927, 'text': 'so this is not exactly dependent on the churn of the customer.', 'start': 22547.384, 'duration': 3.543}], 'summary': 'Monthly charges predicted for first five values indicated actual and predicted values, not dependent on customer churn.', 'duration': 30.173, 'max_score': 22520.754, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI22520754.jpg'}, {'end': 22749.376, 'src': 'embed', 'start': 22715.032, 'weight': 4, 'content': [{'end': 22722.094, 'text': "So I'll set it to be 35, so this time 35% of the records would be present in the test set and the rest,", 'start': 22715.032, 'duration': 7.062}, {'end': 22724.454, 'text': '65% of the records would be present in the train set.', 'start': 22722.094, 'duration': 2.36}, {'end': 22729.116, 'text': 'And I am storing all of those into xtrain, xtest, ytrain, and ytest.', 'start': 22725.015, 'duration': 4.101}, {'end': 22737.198, 'text': "Right after this I'd have to import the logistic regression model.", 'start': 22730.416, 'duration': 6.782}, {'end': 22749.376, 'text': "so from sklearn dot linear model i'd have to import the logistic regression model and i'd have to create an instance of this.", 'start': 22737.198, 'duration': 12.178}], 'summary': 'Splitting 35% for test set, 65% for train set, and importing logistic regression model.', 'duration': 34.344, 'max_score': 22715.032, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI22715032.jpg'}, {'end': 22926.913, 'src': 'embed', 'start': 22891.953, 'weight': 5, 'content': [{'end': 22894.575, 'text': 'We have built the model on top of the training set.', 'start': 22891.953, 'duration': 2.622}, {'end': 22896.696, 'text': "Now let's go ahead and..", 'start': 22895.215, 'duration': 1.481}, {'end': 22899.896, 'text': 'predict the values on top of the test set.', 'start': 22897.915, 'duration': 1.981}, {'end': 22903.258, 'text': "So it'll be log model dot.", 'start': 22899.916, 'duration': 3.342}, {'end': 22912.884, 'text': 'So you we teach the value of X and we are not changing the value of X.', 'start': 22907.741, 'duration': 5.143}, {'end': 22923.11, 'text': 'We have multiple X values over here, right? So this becomes MX 1 plus, you know, M 2 X 2 plus M 3 X 3 plus M 4 X 4 and so on.', 'start': 22912.884, 'duration': 10.226}, {'end': 22926.913, 'text': 'Have multiple independent variables.', 'start': 22925.072, 'duration': 1.841}], 'summary': 'Built model on training set, predicting on test set using multiple x values.', 'duration': 34.96, 'max_score': 22891.953, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI22891953.jpg'}, {'end': 23309.965, 'src': 'embed', 'start': 23265.784, 'weight': 3, 'content': [{'end': 23271.109, 'text': "Right, and I'll go ahead and fit the model on top of the training set.", 'start': 23265.784, 'duration': 5.325}, {'end': 23273.891, 'text': 'So myTree.fit.', 'start': 23272.049, 'duration': 1.842}, {'end': 23279.616, 'text': 'This takes in two parameters, which are xtrain and ytrain.', 'start': 23275.492, 'duration': 4.124}, {'end': 23284.421, 'text': "I fit the model, now it's time to predict the values.", 'start': 23281.678, 'duration': 2.743}, {'end': 23293.288, 'text': "MyTree.predict and I'll be predicting on top of the X test.", 'start': 23286.162, 'duration': 7.126}, {'end': 23306.2, 'text': "Now I'll import the metrics, right? So let me calculate the confusion metrics and also the accuracy score.", 'start': 23294.429, 'duration': 11.771}, {'end': 23309.965, 'text': 'confusion metrics.', 'start': 23308.584, 'duration': 1.381}], 'summary': 'Model trained on training set, metrics calculated: confusion matrix, accuracy score.', 'duration': 44.181, 'max_score': 23265.784, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI23265784.jpg'}], 'start': 21775.466, 'title': 'Building and evaluating regression models', 'summary': 'Covers the process of building linear regression model, training and evaluating the model with a focus on root mean square error, and also includes the building of machine learning models achieving 77.50% accuracy in logistic regression and 74% in decision tree.', 'chapters': [{'end': 22307.508, 'start': 21775.466, 'title': 'Linear regression model building', 'summary': 'Covers building a linear regression model to understand the relationship between monthly charges and tenure, including splitting the dataset into 70/30, importing necessary libraries, and explaining the purpose of training and testing sets.', 'duration': 532.042, 'highlights': ['The chapter covers building a linear regression model to understand the relationship between monthly charges and tenure, including splitting the dataset into 70/30. Key point: Focuses on the primary objective of building a linear regression model and the dataset splitting ratio.', 'Importing necessary libraries such as linear regression model and train test split from sklearn. Key point: Emphasizes the essential libraries required for building the linear regression model.', 'Explaining the purpose of training and testing sets to ensure the model learns the underlying patterns and to test its learning using a sample space. Key point: Provides a clear understanding of the purpose and importance of training and testing sets in model building.']}, {'end': 22595.929, 'start': 22307.508, 'title': 'Model training and evaluation', 'summary': 'Discusses the importance of dividing data into training and testing sets to prevent overfitting, creating and evaluating linear regression models, and the significance of root mean square error in model evaluation, with an example showcasing the prediction of monthly charges based on customer tenure.', 'duration': 288.421, 'highlights': ['The division of training and testing set is done to prevent overfitting, ensuring the model performs well on new data, with an explanation of the consequences of overfitting.', 'The creation and fitting of a linear regression model on the training set is described, followed by the prediction of values on the test set and the calculation of root mean square error, with a specific example yielding a root mean squared error value of 29.39.', 'The concept of comparing root mean square error values from different models is explained, showcasing how a lower root mean square error indicates a better model performance, with hypothetical comparisons of root mean square error values for different models such as 39 and 19.', 'An example demonstrating the prediction of monthly charges based on customer tenure is provided, illustrating the process of understanding the relationship between customer tenure and monthly charges, and emphasizing the building of a logistic regression model with tenure and monthly charges as independent variables.']}, {'end': 23372.074, 'start': 22597.75, 'title': 'Machine learning model building', 'summary': 'Covers the process of building machine learning models, including logistic regression, multiple linear regression, and decision tree, achieving an accuracy of 77.50% in logistic regression and 74% in decision tree.', 'duration': 774.324, 'highlights': ['The logistic regression model achieved an accuracy of 77.50%, with 935 true positives and 157 true negatives out of the total 935+157+106+211 instances.', 'The decision tree model achieved an accuracy of 74%, with 965 true positives and 87 true negatives out of the total 965+87+281+76 instances.', 'The process involves dividing the data set into training and testing sets, fitting the model on the training set, predicting values on the test set, and evaluating the model using metrics such as confusion matrix and accuracy score.', 'The logistic regression model was built with monthly charges as the independent variable and churn as the dependent variable, while the decision tree model was built with tenure as the independent variable.', 'The logistic regression model was also fitted with two independent variables, monthly charges and tenure, achieving an accuracy of 77.50% with a test size ratio of 80-20.']}], 'duration': 1596.608, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI21775466.jpg', 'highlights': ['The logistic regression model achieved an accuracy of 77.50%, with 935 true positives and 157 true negatives out of the total 935+157+106+211 instances.', 'The decision tree model achieved an accuracy of 74%, with 965 true positives and 87 true negatives out of the total 965+87+281+76 instances.', 'The logistic regression model was also fitted with two independent variables, monthly charges and tenure, achieving an accuracy of 77.50% with a test size ratio of 80-20.', 'The chapter covers building a linear regression model to understand the relationship between monthly charges and tenure, including splitting the dataset into 70/30.', 'The division of training and testing set is done to prevent overfitting, ensuring the model performs well on new data, with an explanation of the consequences of overfitting.', 'The creation and fitting of a linear regression model on the training set is described, followed by the prediction of values on the test set and the calculation of root mean square error, with a specific example yielding a root mean squared error value of 29.39.']}, {'end': 24906.706, 'segs': [{'end': 23517.016, 'src': 'embed', 'start': 23473.633, 'weight': 1, 'content': [{'end': 23477.654, 'text': "So that's why you must have seen we have used matplotlib.pyplot.", 'start': 23473.633, 'duration': 4.021}, {'end': 23482.536, 'text': 'Right So matplotlib is the basic class and pyplot is a function of it.', 'start': 23477.694, 'duration': 4.842}, {'end': 23485.077, 'text': "So that's why class name dot object name.", 'start': 23482.576, 'duration': 2.501}, {'end': 23486.937, 'text': 'I mean, sorry, object name dot function name.', 'start': 23485.097, 'duration': 1.84}, {'end': 23488.018, 'text': "That's how we use it.", 'start': 23487.057, 'duration': 0.961}, {'end': 23489.018, 'text': "Right That's the OOP basic.", 'start': 23488.038, 'duration': 0.98}, {'end': 23490.408, 'text': "So that's how we do it.", 'start': 23489.448, 'duration': 0.96}, {'end': 23493.149, 'text': 'Okay Next is very wide variety of graphs.', 'start': 23490.728, 'duration': 2.421}, {'end': 23494.93, 'text': 'We will see that next.', 'start': 23493.189, 'duration': 1.741}, {'end': 23503.172, 'text': 'Like what all types of graphs we have in Matplotlib and how that can be used, and what are the usage of those graphs,', 'start': 23495.09, 'duration': 8.082}, {'end': 23504.612, 'text': 'and where do we use it like that?', 'start': 23503.172, 'duration': 1.44}, {'end': 23507.893, 'text': 'We have simple functions used for visualization.', 'start': 23505.152, 'duration': 2.741}, {'end': 23512.354, 'text': 'So if you go ahead and see here, forget about the first example, see this basic.', 'start': 23507.933, 'duration': 4.421}, {'end': 23517.016, 'text': 'What we have done, we have taken a np.a range.', 'start': 23512.874, 'duration': 4.142}], 'summary': 'Matplotlib.pyplot used for visualization. next: various graphs, usage, and functions.', 'duration': 43.383, 'max_score': 23473.633, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI23473633.jpg'}, {'end': 23802.84, 'src': 'embed', 'start': 23779.082, 'weight': 4, 'content': [{'end': 23787.568, 'text': 'okay, first of all, why and what plot need to be used, where it need to be used and how you can write those.', 'start': 23779.082, 'duration': 8.486}, {'end': 23793.497, 'text': "okay customization, Even if you learn it slowly by playing with it, it's perfectly fine.", 'start': 23787.568, 'duration': 5.929}, {'end': 23794.577, 'text': "I don't know.", 'start': 23793.737, 'duration': 0.84}, {'end': 23795.597, 'text': "That's not a matter.", 'start': 23794.617, 'duration': 0.98}, {'end': 23802.84, 'text': 'Okay The customization thing, coloring thing, changing the access labels, adding legends, changing the colors.', 'start': 23796.058, 'duration': 6.782}], 'summary': 'Customize plots by adding access labels, legends, and changing colors.', 'duration': 23.758, 'max_score': 23779.082, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI23779082.jpg'}, {'end': 24503.731, 'src': 'embed', 'start': 24475.551, 'weight': 10, 'content': [{'end': 24479.234, 'text': 'so this marker object is to show you this points.', 'start': 24475.551, 'duration': 3.683}, {'end': 24485.999, 'text': 'okay, show you this data points how you want to see those, but o gives you the best visualization among all.', 'start': 24479.234, 'duration': 6.765}, {'end': 24487.801, 'text': "okay, so that's how you can use it.", 'start': 24485.999, 'duration': 1.802}, {'end': 24493.423, 'text': 'Okay, next, is this line style is for styling this lines, this intermediate lines.', 'start': 24489.48, 'duration': 3.943}, {'end': 24496.685, 'text': 'If you provide colon then it will be like this dashed line.', 'start': 24493.503, 'duration': 3.182}, {'end': 24500.668, 'text': 'If you provide a dash it dashes will join right.', 'start': 24497.146, 'duration': 3.522}, {'end': 24503.731, 'text': 'So dashes will join and it will you will see a straight line.', 'start': 24501.049, 'duration': 2.682}], 'summary': 'Using marker object for data points visualization. line style for intermediate lines.', 'duration': 28.18, 'max_score': 24475.551, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI24475551.jpg'}, {'end': 24685.962, 'src': 'embed', 'start': 24654.785, 'weight': 0, 'content': [{'end': 24655.686, 'text': 'Okay Okay.', 'start': 24654.785, 'duration': 0.901}, {'end': 24658.047, 'text': 'Next is alpha is for reducing the brightness.', 'start': 24655.706, 'duration': 2.341}, {'end': 24660.748, 'text': "So let's say you will understand on your own only.", 'start': 24658.067, 'duration': 2.681}, {'end': 24661.648, 'text': "I don't need to explain.", 'start': 24660.788, 'duration': 0.86}, {'end': 24664.71, 'text': 'If I run it to 1, see this is much vivid.', 'start': 24661.668, 'duration': 3.042}, {'end': 24668.692, 'text': 'But if I do it 0.1, you will see this is way too dim.', 'start': 24665.09, 'duration': 3.602}, {'end': 24669.912, 'text': 'It will be way too dim.', 'start': 24668.892, 'duration': 1.02}, {'end': 24671.633, 'text': 'Right So that is what is alpha.', 'start': 24669.992, 'duration': 1.641}, {'end': 24674.375, 'text': 'Alpha is for reducing the brightness.', 'start': 24671.893, 'duration': 2.482}, {'end': 24676.596, 'text': 'So alpha is the transparency factor.', 'start': 24674.395, 'duration': 2.201}, {'end': 24685.962, 'text': 'So if you reduce the alpha, Then what it will do, it will make the line or plot whatever you are doing transparent to the plane.', 'start': 24676.916, 'duration': 9.046}], 'summary': 'Alpha controls brightness & transparency in plots. 1 makes it vivid, 0.1 makes it dim.', 'duration': 31.177, 'max_score': 24654.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI24654785.jpg'}, {'end': 24906.706, 'src': 'embed', 'start': 24868.987, 'weight': 8, 'content': [{'end': 24870.568, 'text': 'The my line thing is already added.', 'start': 24868.987, 'duration': 1.581}, {'end': 24877.412, 'text': 'And yellow sequels, this means the graph will on its own calculate the best position for the legend and it will put it.', 'start': 24870.988, 'duration': 6.424}, {'end': 24879.674, 'text': 'So it will always put in the best position.', 'start': 24877.452, 'duration': 2.222}, {'end': 24880.575, 'text': 'It can be down.', 'start': 24879.734, 'duration': 0.841}, {'end': 24881.315, 'text': 'It can be up.', 'start': 24880.635, 'duration': 0.68}, {'end': 24882.136, 'text': 'It can be anywhere.', 'start': 24881.455, 'duration': 0.681}, {'end': 24884.317, 'text': 'Okay PLT dot grid true.', 'start': 24882.396, 'duration': 1.921}, {'end': 24885.918, 'text': 'That is to show this grid lines.', 'start': 24884.437, 'duration': 1.481}, {'end': 24888.2, 'text': "If you don't want it, just don't mention this function.", 'start': 24885.978, 'duration': 2.222}, {'end': 24889.701, 'text': "It won't show you this grid.", 'start': 24888.66, 'duration': 1.041}, {'end': 24891.362, 'text': "Okay So that's up to you.", 'start': 24890.101, 'duration': 1.261}, {'end': 24893.223, 'text': 'If you like the grid, you can keep it.', 'start': 24891.662, 'duration': 1.561}, {'end': 24894.624, 'text': "If you don't, you cannot.", 'start': 24893.283, 'duration': 1.341}, {'end': 24901.765, 'text': 'So there is no really no no real thing in that and showing or not showing the grids.', 'start': 24895.043, 'duration': 6.722}, {'end': 24906.706, 'text': 'Okay, somewhere if you fill it, you can provide you can you can have those displayed.', 'start': 24902.285, 'duration': 4.421}], 'summary': 'Graph automatically calculates best legend position and grid lines can be customized.', 'duration': 37.719, 'max_score': 24868.987, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI24868987.jpg'}], 'start': 23372.634, 'title': 'Data visualization and machine learning', 'summary': 'Covers an end-to-end project on customer churn dataset, including data manipulation, visualization, and machine learning algorithms. it also discusses python keywords, matplotlib visualization, different types of plots, histograms in image processing, and plotting line graphs using matplotlib, emphasizing practical demonstrations and examples.', 'chapters': [{'end': 23413.799, 'start': 23372.634, 'title': 'Customer churn data project', 'summary': 'Covers the process of dividing features and target into training and testing split, building a model, making predictions, and calculating accuracy in an end-to-end project on customer churn dataset, including data manipulation, visualization, and machine learning algorithms.', 'duration': 41.165, 'highlights': ['The most important part in the entire data science lifecycle is the data processing part, involving data manipulation, visualization, and implementing machine learning algorithms.', 'The process involves dividing features and target into training and testing split, building a model, making predictions, and calculating accuracy.', 'The chapter also includes an end-to-end project on customer churn dataset, encompassing data manipulation, visualization, and machine learning algorithms.']}, {'end': 23695.762, 'start': 23414.339, 'title': 'Python keywords and matplotlib visualization', 'summary': 'Discusses python keywords and matplotlib visualization, highlighting the oop-based api and various graph creation capabilities, emphasizing the ease of data visualization using matplotlib functions and the importance of the pyplot module.', 'duration': 281.423, 'highlights': ['Matplotlib has a wide variety of graphs and is based on an OOP setting, making it easy to visualize data. Matplotlib offers a wide variety of graphs and is structured based on OOP, allowing for easy data visualization.', 'Usage of Matplotlib functions for data visualization is illustrated through an example of plotting a line plot. The transcript illustrates the usage of Matplotlib functions for data visualization through an example of plotting a line plot, showcasing the ease of visualizing functions in Matplotlib.', 'The importance of the PyPlot module for data visualization in Matplotlib is emphasized, highlighting its role behind the scenes. The PyPlot module is highlighted as the main module responsible for data visualization in Matplotlib, emphasizing its role behind the scenes.']}, {'end': 24178.351, 'start': 23695.762, 'title': 'Matplotlib basics and graph types', 'summary': 'Covers the basics of using matplotlib inline command, different types of plots in matplotlib, and the practical use of matplotlib in creating and sending data visualizations to upper management.', 'duration': 482.589, 'highlights': ['Practical use of Matplotlib in creating and sending data visualizations to upper management The speaker shares an example of creating a script using Python to take data from a rdbms, plot it using Matplotlib, and send it to upper management for reporting purposes.', 'Different types of plots in Matplotlib and their practical uses The chapter explains line plots, bar plots, and scatter plots in Matplotlib, highlighting their uses for plotting 2D equations, representing data distributions, and determining clustering in data.', 'Basics of using Matplotlib inline command and customization options in Matplotlib The speaker emphasizes the use of Matplotlib inline command for displaying graphs in the same window, and mentions customization options like changing background and foreground colors, bar color customization, and accessing bars individually for modifications.']}, {'end': 24422.01, 'start': 24178.351, 'title': 'Histograms & image processing', 'summary': 'Explains the usage of histograms in image processing, emphasizing the frequency of data points and their relevance in cpu processing, while also highlighting the differences between histogram and bar plots. it also introduces the concepts of image plot, box plots, and violin plots as important visualization tools.', 'duration': 243.659, 'highlights': ['Histograms are primarily used in image processing to display the frequency of data points and are particularly useful for evaluating the spread and frequency of pixel values, especially in CPU processing scenarios.', 'The chapter emphasizes the distinction between histogram and bar plots, highlighting that while bar plots only display X and Y points, histograms provide frequency data, making them crucial for analyzing CPU usage and frequency of high CPU usage.', 'Introduction of important visualization tools such as image plot, box plots, and violin plots is provided, indicating their significance in data visualization and analysis.']}, {'end': 24906.706, 'start': 24422.07, 'title': 'Plotting line graphs with matplotlib', 'summary': 'Explains how to plot line graphs using matplotlib, covering the customization of line style, color, transparency, markers, titles, legends, and grid lines, with emphasis on providing practical demonstrations and examples.', 'duration': 484.636, 'highlights': ['The chapter emphasizes the customization of line style, color, transparency, markers, titles, legends, and grid lines when plotting line graphs using Matplotlib. The transcript covers a detailed explanation of customizing line style, color, transparency, markers, titles, legends, and grid lines for line graphs using Matplotlib.', 'The speaker demonstrates the use of various line styles such as dot, dash, and continuous lines, along with the ability to provide any color using full names, short names, RGB values, or hex values. The speaker demonstrates the use of various line styles (dot, dash, continuous lines) and the ability to provide any color using full names, short names, RGB values, or hex values for customization.', 'The chapter explains the function of alpha in reducing the brightness and transparency of lines, with practical demonstrations using different alpha values to adjust the visual appearance of the lines. The chapter explains the function of alpha in reducing the brightness and transparency of lines, with practical demonstrations using different alpha values to adjust the visual appearance of the lines.', "The use of markers to show data points is detailed, with examples of various markers such as cross and plus, and the speaker emphasizes the significance of using 'o' for the best visualization. The use of markers to show data points is detailed, with examples of various markers such as cross and plus, and the speaker emphasizes the significance of using 'o' for the best visualization.", 'The chapter provides insights into the placement and customization of titles, including the ability to change the position to top or bottom, and the usage of X and Y labels for the graph. The chapter provides insights into the placement and customization of titles, including the ability to change the position to top or bottom, and the usage of X and Y labels for the graph.']}], 'duration': 1534.072, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI23372634.jpg', 'highlights': ['The most important part in the entire data science lifecycle is the data processing part, involving data manipulation, visualization, and implementing machine learning algorithms.', 'The process involves dividing features and target into training and testing split, building a model, making predictions, and calculating accuracy.', 'The chapter also includes an end-to-end project on customer churn dataset, encompassing data manipulation, visualization, and machine learning algorithms.', 'Matplotlib has a wide variety of graphs and is based on an OOP setting, making it easy to visualize data.', 'Usage of Matplotlib functions for data visualization is illustrated through an example of plotting a line plot.', 'The importance of the PyPlot module for data visualization in Matplotlib is emphasized, highlighting its role behind the scenes.', 'Practical use of Matplotlib in creating and sending data visualizations to upper management.', 'Different types of plots in Matplotlib and their practical uses.', 'Basics of using Matplotlib inline command and customization options in Matplotlib.', 'Histograms are primarily used in image processing to display the frequency of data points and are particularly useful for evaluating the spread and frequency of pixel values, especially in CPU processing scenarios.', 'Introduction of important visualization tools such as image plot, box plots, and violin plots is provided, indicating their significance in data visualization and analysis.', 'The chapter emphasizes the customization of line style, color, transparency, markers, titles, legends, and grid lines when plotting line graphs using Matplotlib.', 'The speaker demonstrates the use of various line styles such as dot, dash, and continuous lines, along with the ability to provide any color using full names, short names, RGB values, or hex values for customization.', 'The chapter explains the function of alpha in reducing the brightness and transparency of lines, with practical demonstrations using different alpha values to adjust the visual appearance of the lines.', "The use of markers to show data points is detailed, with examples of various markers such as cross and plus, and the speaker emphasizes the significance of using 'o' for the best visualization.", 'The chapter provides insights into the placement and customization of titles, including the ability to change the position to top or bottom, and the usage of X and Y labels for the graph.']}, {'end': 26980.251, 'segs': [{'end': 25837.033, 'src': 'embed', 'start': 25807.682, 'weight': 8, 'content': [{'end': 25812.344, 'text': 'Histogram denotes the frequency of data points between a range.', 'start': 25807.682, 'duration': 4.662}, {'end': 25814.565, 'text': 'So from 0 to 20, how many? 22.', 'start': 25812.684, 'duration': 1.881}, {'end': 25818.847, 'text': 'So what happens if we add another bin, right? The graph shape will change radically.', 'start': 25814.565, 'duration': 4.282}, {'end': 25823.288, 'text': "Why that will change? Because within 0 and 10, we don't have any point.", 'start': 25819.087, 'duration': 4.201}, {'end': 25824.749, 'text': 'So it has kept it blank.', 'start': 25823.689, 'duration': 1.06}, {'end': 25826.69, 'text': "So we haven't put the marker over there.", 'start': 25824.889, 'duration': 1.801}, {'end': 25830.171, 'text': "Now, let's say we are plotting a marker in 30.", 'start': 25827.35, 'duration': 2.821}, {'end': 25832.011, 'text': 'Okay, we are changing this to 30.', 'start': 25830.171, 'duration': 1.84}, {'end': 25837.033, 'text': 'So see it from 20 to 30, the bin has changed, right? Because we have created two different markers.', 'start': 25832.011, 'duration': 5.022}], 'summary': 'Histogram shows frequency of data points in bins. adding bins changes graph shape. no data between 0 and 10 results in blank bin. adding marker at 30 creates new bin.', 'duration': 29.351, 'max_score': 25807.682, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI25807682.jpg'}, {'end': 26168.628, 'src': 'embed', 'start': 26137.068, 'weight': 0, 'content': [{'end': 26138.849, 'text': 'okay, that we have in here.', 'start': 26137.068, 'duration': 1.781}, {'end': 26144.194, 'text': 'so if you see carefully for the first, this total, this is the box plot for that.', 'start': 26138.849, 'duration': 5.345}, {'end': 26145.515, 'text': 'what was the minimum value?', 'start': 26144.194, 'duration': 1.321}, {'end': 26149.518, 'text': 'one, correct, so here is it is one what was the first quartile?', 'start': 26145.515, 'duration': 4.003}, {'end': 26151.539, 'text': '10. what was the mean 20?', 'start': 26149.518, 'duration': 2.021}, {'end': 26152.66, 'text': 'what was the third quartile?', 'start': 26151.539, 'duration': 1.121}, {'end': 26155.122, 'text': '30, and this sums it up.', 'start': 26152.66, 'duration': 2.462}, {'end': 26157.123, 'text': 'okay, this 30 sums it up.', 'start': 26155.122, 'duration': 2.001}, {'end': 26158.465, 'text': 'so this is the box plot.', 'start': 26157.123, 'duration': 1.342}, {'end': 26159.625, 'text': 'this much you focus on.', 'start': 26158.465, 'duration': 1.16}, {'end': 26168.628, 'text': "and the max point, that is that, what that what i was referring to as q4, that is not there, that doesn't present, that is not present anyways.", 'start': 26159.625, 'duration': 9.003}], 'summary': 'Box plot analysis shows minimum 1, first quartile 10, mean 20, and third quartile 30.', 'duration': 31.56, 'max_score': 26137.068, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI26137068.jpg'}, {'end': 26787.187, 'src': 'embed', 'start': 26756.56, 'weight': 2, 'content': [{'end': 26763.643, 'text': 'so as we have more ones, so the peak is flattened near one, as we have less, less number of zero.', 'start': 26756.56, 'duration': 7.083}, {'end': 26765.403, 'text': 'so it is flattened near zero.', 'start': 26763.643, 'duration': 1.76}, {'end': 26775.904, 'text': 'but see, between zero and one, the curve is getting shortened and when it goes, so uh, So 0.6 will be their mean.', 'start': 26765.403, 'duration': 10.501}, {'end': 26779.485, 'text': 'Okay Because that is what we have taken as 0 and 1.', 'start': 26776.004, 'duration': 3.481}, {'end': 26782.625, 'text': 'Okay So 0.6 will be their mean.', 'start': 26779.485, 'duration': 3.14}, {'end': 26787.187, 'text': 'And if you see from 0 to 0.6 that the curve is a.', 'start': 26782.645, 'duration': 4.542}], 'summary': 'Data distribution peaks near one with fewer zeros, mean at 0.6.', 'duration': 30.627, 'max_score': 26756.56, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI26756560.jpg'}], 'start': 24906.726, 'title': 'Creating subplots and data visualization with matplotlib', 'summary': 'Discusses creating subplots in matplotlib, using plt.subplot for data visualization, customizing various plot types, and understanding probability density function, with emphasis on parameters, plot arrangement, and statistical plots. the content provides insights through detailed explanations, code samples, and practical applications.', 'chapters': [{'end': 25029.669, 'start': 24906.726, 'title': 'Subplots in matplotlib', 'summary': 'Discusses the concept of creating subplots in matplotlib through the subplot function, allowing the display of multiple plots within the same graph, showcasing the use of parameters and the importance of subplot function.', 'duration': 122.943, 'highlights': ['The chapter explains the concept of creating subplots in Matplotlib through the subplot function, showcasing the display of multiple plots within the same graph, emphasizing the use of parameters and the importance of subplot function.', 'The subplot function is used to create subplots in Matplotlib, allowing the display of multiple plots within the same graph, with an emphasis on the importance of parameters and its functionality.', 'The subplot function in Matplotlib is utilized to create subplots, enabling the display of multiple plots within the same graph, highlighting the significance of parameters for customization.']}, {'end': 25213.712, 'start': 25029.669, 'title': 'Using plt.subplot in data visualization', 'summary': 'Explains how to use plt.subplot to create subplots in data visualization, emphasizing the importance of setting the number of rows as one to avoid resizing issues, and the standard usage of plt.subplot for arranging plots in a rectangular way.', 'duration': 184.043, 'highlights': ['The importance of setting the number of rows as one to avoid resizing issues It is important to set the number of rows as one when using plt.subplot to avoid resizing issues, as creating multiple rows results in the first plot becoming smaller and the extra portion of the second plot getting placed in the second row.', 'Standard usage of plt.subplot for arranging plots in a rectangular way The standard usage of plt.subplot involves passing the number of rows and number of columns, and it is recommended to have the number of rows as one for subplots containing a single graph, while creating a separate subplot is suggested for accommodating multiple graphs.', 'Explanation of the figure number parameter in plt.subplot The figure number parameter denotes the figure number attached to the plot, and it is associated with the pi plot dot figure function, allowing for customization using the figure object.']}, {'end': 25543.544, 'start': 25217.344, 'title': 'Creating subplots and customizing plot types', 'summary': 'Covers creating subplots for line plots, bar plots, and scatter plots, explaining how to customize the plots and providing examples of using bar and scatter plots with detailed explanations and code samples.', 'duration': 326.2, 'highlights': ['The chapter covers creating subplots for line plots, bar plots, and scatter plots, explaining how to customize the plots and providing examples of using bar and scatter plots with detailed explanations and code samples.', 'Bar plot is basically used to show counts, as demonstrated by creating a dataset with counts for apple, mangoes, lemons, and oranges, and plotting it using PLT dot bar.', 'The chapter also explains horizontal bar graphs, demonstrating their creation using the same dataset and the barh function, and mentions the customization options such as changing the width, height, and colors of the bars.', 'The scatter plot section explains that it is different from x and y equations, uses three datasets a, b, and x, and demonstrates the creation and customization of scatter plots using PLT dot scatter with detailed explanations and code samples.']}, {'end': 25855.178, 'start': 25543.544, 'title': 'Data visualization with matplotlib', 'summary': 'Explains the usage of various parameters like size, color, marker, alpha, and histogram with matplotlib library, demonstrating how to customize and save figures, understand histogram bins, and interpret the frequency of data points within specific ranges.', 'duration': 311.634, 'highlights': ['The chapter covers the usage of parameters like size, color, marker, alpha, and histogram with Matplotlib library for visualization. It explains the usage of key parameters for visualization using Matplotlib.', 'Demonstrates how to customize and save figures using the plt.savefig command to produce image outputs for further use. It explains the process of customizing and saving figures for external use.', 'Provides a detailed understanding of histogram bins and their significance in representing the frequency of data points within specific ranges. It provides a detailed explanation of histogram bins and their role in depicting data point frequencies within defined intervals.']}, {'end': 26609.881, 'start': 25855.218, 'title': 'Explaining histograms and statistical plots', 'summary': 'Covers the explanation of histograms, customizing histograms, and statistical plots including box plots and violin plots, providing insights into data distribution and quartile information, with a focus on probability density function in the violin plot.', 'duration': 754.663, 'highlights': ['Histograms and plot types Explained the usage of different plot types including bar, line, scatter, and histogram for visualizing data distribution and counts.', 'Customizing histograms Detailed customization options for histograms, including bin numbers, colors, and axis labels, enhancing visualization of data distribution.', 'Box plot and quartile information Detailed explanation of quartile information and calculation methods, providing insights into the distribution of data points and quartile range in a box plot.', 'Interquartile range and data frequency Explanation of interquartile range (IQR) and its significance in reflecting the frequency and distribution of data points in a box plot.', 'Probability density function in violin plot Detailed explanation of the probability density function and its role in representing the likelihood of data points within a given range, enhancing understanding of data distribution in a violin plot.']}, {'end': 26980.251, 'start': 26609.881, 'title': 'Understanding probability density function', 'summary': 'Explains the concept of probability density function and its application in understanding skewed data sets and model validation, demonstrating how to analyze and interpret the distribution and weight of features in a machine learning data set.', 'duration': 370.37, 'highlights': ["Probability density function helps in understanding the distribution and weight of features in a machine learning data set. It explains how to validate a model and analyze the features' distribution and weight, providing insights for model improvement.", 'Skewed data sets require alternative approaches and cannot be fitted with straight line equations. The chapter emphasizes the need to change thinking and consider alternative options for skewed data sets, such as using binomial or sigmoid functions.', 'Quiver and stream plots are used for vector analysis and are essential for physicists and electrical engineers. These plots are crucial for analyzing vectors and have applications in physics and electrical engineering for understanding current flow and vector analysis in circuits.', 'Area plots visualize the area covered by a curve, providing a visual representation of the covered area. Area plots visually represent the area covered by a curve, allowing for easy visualization of the covered area within the plot.']}], 'duration': 2073.525, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI24906726.jpg', 'highlights': ['The subplot function in Matplotlib is utilized to create subplots, enabling the display of multiple plots within the same graph, highlighting the significance of parameters for customization.', 'Standard usage of plt.subplot involves passing the number of rows and number of columns, and it is recommended to have the number of rows as one for subplots containing a single graph, while creating a separate subplot is suggested for accommodating multiple graphs.', 'The chapter covers creating subplots for line plots, bar plots, and scatter plots, explaining how to customize the plots and providing examples of using bar and scatter plots with detailed explanations and code samples.', 'The chapter covers the usage of parameters like size, color, marker, alpha, and histogram with Matplotlib library for visualization. It explains the usage of key parameters for visualization using Matplotlib.', 'Histograms and plot types Explained the usage of different plot types including bar, line, scatter, and histogram for visualizing data distribution and counts.', 'Probability density function in violin plot Detailed explanation of the probability density function and its role in representing the likelihood of data points within a given range, enhancing understanding of data distribution in a violin plot.', "Probability density function helps in understanding the distribution and weight of features in a machine learning data set. It explains how to validate a model and analyze the features' distribution and weight, providing insights for model improvement.", 'Skewed data sets require alternative approaches and cannot be fitted with straight line equations. The chapter emphasizes the need to change thinking and consider alternative options for skewed data sets, such as using binomial or sigmoid functions.', 'Quiver and stream plots are used for vector analysis and are essential for physicists and electrical engineers. These plots are crucial for analyzing vectors and have applications in physics and electrical engineering for understanding current flow and vector analysis in circuits.', 'Area plots visualize the area covered by a curve, providing a visual representation of the covered area.']}, {'end': 28668.543, 'segs': [{'end': 27443.105, 'src': 'embed', 'start': 27411.949, 'weight': 1, 'content': [{'end': 27414.491, 'text': 'So that is the number will be whole intact.', 'start': 27411.949, 'duration': 2.542}, {'end': 27416.893, 'text': 'Then after the decimal, we want only one point.', 'start': 27414.671, 'duration': 2.222}, {'end': 27418.454, 'text': "So that's why 1.1.", 'start': 27416.913, 'duration': 1.541}, {'end': 27422.898, 'text': 'Shadow true means that you see there is a shadow effect inside.', 'start': 27418.454, 'duration': 4.444}, {'end': 27428.622, 'text': 'If you give it as false, I will show you, then you will understand the difference between these two.', 'start': 27423.158, 'duration': 5.464}, {'end': 27431.82, 'text': 'shadow, true and false.', 'start': 27430.259, 'duration': 1.561}, {'end': 27443.105, 'text': "so if it's a, if it's a 3D, I mean if so, here the shadow is not really visible, but it should be.", 'start': 27431.82, 'duration': 11.285}], 'summary': 'The number is 1.1 with a shadow effect shown as true or false.', 'duration': 31.156, 'max_score': 27411.949, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI27411949.jpg'}, {'end': 27506.007, 'src': 'embed', 'start': 27459.353, 'weight': 0, 'content': [{'end': 27462.992, 'text': 'okay, so it will have a equal, equal kind of a shape.', 'start': 27459.353, 'duration': 3.639}, {'end': 27465.494, 'text': 'so it will have a equal kind of a array.', 'start': 27462.992, 'duration': 2.502}, {'end': 27468.837, 'text': 'actually i mean, sorry, equal kind of a shape of the circle.', 'start': 27465.494, 'duration': 3.343}, {'end': 27471.139, 'text': "so that's why start angle equals 90..", 'start': 27468.837, 'duration': 2.302}, {'end': 27476.924, 'text': 'explode. as you can see, if you mention 0.1 as an explode, then it will be those.', 'start': 27471.139, 'duration': 5.785}, {'end': 27480.648, 'text': 'those slices will be cut out from the array and it will be outside the array.', 'start': 27476.924, 'duration': 3.724}, {'end': 27483.291, 'text': "so that's how explode function works and explode.", 'start': 27480.648, 'duration': 2.643}, {'end': 27484.892, 'text': 'you need to give one for each.', 'start': 27483.291, 'duration': 1.601}, {'end': 27490.238, 'text': 'so where i have mentioned 0.1, those slices, see they are cut out from the pie chart.', 'start': 27484.892, 'duration': 5.346}, {'end': 27493.522, 'text': 'right. just a quick info, guys.', 'start': 27490.238, 'duration': 3.284}, {'end': 27502.2, 'text': 'intellipaat provides data science architect master program in partnership with IBM and mentored by industry experts.', 'start': 27493.522, 'duration': 8.678}, {'end': 27506.007, 'text': 'The course link of which is given in the description below.', 'start': 27503.142, 'duration': 2.865}], 'summary': 'Explode function cuts 0.1 slices from array to outside, starts at angle 90.', 'duration': 46.654, 'max_score': 27459.353, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI27459353.jpg'}, {'end': 27730.484, 'src': 'embed', 'start': 27705.361, 'weight': 2, 'content': [{'end': 27712.944, 'text': 'uh, just think we want to show to the viewer this much of value, this much of portion.', 'start': 27705.361, 'duration': 7.583}, {'end': 27714.084, 'text': 'so what can we do?', 'start': 27712.944, 'duration': 1.14}, {'end': 27716.558, 'text': 'we can fill it up with some value.', 'start': 27714.084, 'duration': 2.474}, {'end': 27718.239, 'text': 'we can fill it up with some color.', 'start': 27716.558, 'duration': 1.681}, {'end': 27721.32, 'text': "let's say we are filling up, filling this up with orange.", 'start': 27718.239, 'duration': 3.081}, {'end': 27723.161, 'text': 'okay, what will happen?', 'start': 27721.32, 'duration': 1.841}, {'end': 27725.182, 'text': 'this shade will left upon right.', 'start': 27723.161, 'duration': 2.021}, {'end': 27727.623, 'text': 'this outer side circle will be left upon.', 'start': 27725.182, 'duration': 2.441}, {'end': 27728.543, 'text': "so that's what.", 'start': 27727.623, 'duration': 0.92}, {'end': 27730.484, 'text': "that's how you can create a donut chart.", 'start': 27728.543, 'duration': 1.941}], 'summary': 'Demonstrating how to create a donut chart with value and color.', 'duration': 25.123, 'max_score': 27705.361, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI27705361.jpg'}], 'start': 26981.431, 'title': 'Various data visualization techniques and machine learning fundamentals', 'summary': "Covers the use of quiver and stream plots, along with area plots, in data science. it also includes python programming concepts for image plotting, details on creating pie charts, donut charts, and area charts using matplotlib in python. additionally, it introduces machine learning, emphasizing python's potential for generating visually appealing charts and the significance of data cleaning, which consumes 70 percent of a data scientist's time. the chapter also explains supervised machine learning concepts, terms, and provides an example of a supervised machine learning problem.", 'chapters': [{'end': 27029.613, 'start': 26981.431, 'title': 'Understanding quiver, stream, and area plots', 'summary': 'Demonstrates the use of quiver and stream plots, highlighting their differences and limited usage in data science, with the area plot also discussed as a less frequently used tool.', 'duration': 48.182, 'highlights': ['The stream plot differs from the quiver plot in that it does not display vector direction, instead representing only positive or negative values for a vector in a 2D plane.', 'Data scientists rarely use quiver and stream plots, with the area plot also having limited usage in the field.', 'Area plots show the area covered under a curve, serving as a less frequently utilized tool in data science.']}, {'end': 27383.1, 'start': 27029.613, 'title': 'Python programming and image plotting', 'summary': 'Covers python programming concepts and image plotting using the pillow library, including details on opening images, converting them to numpy arrays, and displaying them using the ax1.imshow function as well as stream plots, pie charts, and quiver plots for vector calculations and trend analysis.', 'duration': 353.487, 'highlights': ['The chapter covers Python programming concepts and image plotting using the Pillow library, including details on opening images, converting them to numpy arrays, and displaying them using the ax1.imshow function. Covers Python programming concepts and image plotting, including opening images, converting to numpy arrays, and displaying using ax1.imshow.', 'Stream plots, pie charts, and quiver plots are also discussed for vector calculations and trend analysis. Discusses stream plots, pie charts, and quiver plots for vector calculations and trend analysis.']}, {'end': 27840.838, 'start': 27383.64, 'title': 'Matplotlib charts explanation', 'summary': 'Covers the explanation of creating pie charts, donut charts, and area charts using matplotlib in python, highlighting the parameters and effects such as shadow, explode, and transparency.', 'duration': 457.198, 'highlights': ["Explaining the function of explode in pie charts and its effect on the slices, with a demonstration using the value 0.1 resulting in the slices being cut out from the chart and placed outside. The function of explode in pie charts is demonstrated with the value 0.1, showcasing how it cuts out slices from the pie chart and positions them outside, providing a visual demonstration of the function's effect.", 'Demonstrating the impact of shadow parameter in pie charts, explaining its visibility based on color choices and its bulging effect with lighter colors, with a recommendation to leave it unused. The impact of the shadow parameter in pie charts is explained, highlighting its visibility based on color choices and its bulging effect with lighter colors, along with a recommendation to leave it unused due to its minimal usage.', 'Describing the process of creating a donut chart using two pie charts combined, and explaining the visual difference between a pie chart and a donut chart. The process of creating a donut chart using two pie charts combined is described, along with an explanation of the visual difference between a pie chart and a donut chart, providing insights into their usage and visual representation.']}, {'end': 28152.745, 'start': 27840.838, 'title': 'Understanding machine learning', 'summary': "Introduces the concept of machine learning, discussing its applications, types, and importance in data analysis, emphasizing the potential of python for generating visually appealing charts and the significance of data cleaning, highlighting that it consumes 70 percent of a data scientist's time.", 'duration': 311.907, 'highlights': ["The chapter emphasizes the potential of Python for generating visually appealing charts and highlights the significance of data cleaning, which consumes 70 percent of a data scientist's time. Emphasizes the potential of Python for generating visually appealing charts, highlights the significance of data cleaning, which consumes 70 percent of a data scientist's time", 'The chapter discusses the applications of machine learning, including product recommendations, video recommendations, Amazon, Alexa, Microsoft, and Siri, emphasizing their relevance in the context of data analysis. Discusses the applications of machine learning, including product recommendations, video recommendations, Amazon, Alexa, Microsoft, and Siri, emphasizing their relevance in the context of data analysis', 'The chapter introduces the concept of machine learning, discussing its applications, types, and importance in data analysis, and highlights the potential of Python for generating visually appealing charts. Introduces the concept of machine learning, discusses its applications, types, and importance in data analysis, highlights the potential of Python for generating visually appealing charts']}, {'end': 28668.543, 'start': 28152.745, 'title': 'Supervised machine learning basics', 'summary': 'Introduces the concept of supervised machine learning, explaining the key terms like target variable, dependent variable, response variable, and independent variables, and provides an example of a supervised machine learning problem with the hourly rate prediction based on years of experience and corporate training. it also outlines the process of creating a model in supervised machine learning and presents use cases for both supervised and unsupervised machine learning.', 'duration': 515.798, 'highlights': ['The chapter introduces the concept of supervised machine learning It provides an example of a supervised machine learning problem with the hourly rate prediction based on years of experience and corporate training.', 'Explanation of key terms like target variable, dependent variable, response variable, and independent variables The chapter explains the terms and their significance in the context of supervised machine learning.', 'Process of creating a model in supervised machine learning It outlines the process of passing features and target to a machine learning algorithm to create a model, as well as making predictions for new data.', 'Use cases for both supervised and unsupervised machine learning It presents use cases for supervised machine learning, such as the HAM-SPAM classification, and briefly touches upon unsupervised machine learning with an example of k-means clustering.']}], 'duration': 1687.112, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI26981431.jpg', 'highlights': ["The chapter emphasizes the potential of Python for generating visually appealing charts and highlights the significance of data cleaning, which consumes 70 percent of a data scientist's time", 'The chapter introduces the concept of machine learning, discussing its applications, types, and importance in data analysis, highlights the potential of Python for generating visually appealing charts', 'The chapter introduces the concept of supervised machine learning It provides an example of a supervised machine learning problem with the hourly rate prediction based on years of experience and corporate training', 'Explaining the function of explode in pie charts and its effect on the slices, with a demonstration using the value 0.1 resulting in the slices being cut out from the chart and placed outside', 'Describing the process of creating a donut chart using two pie charts combined, and explaining the visual difference between a pie chart and a donut chart']}, {'end': 30717.191, 'segs': [{'end': 28808.85, 'src': 'embed', 'start': 28779.876, 'weight': 0, 'content': [{'end': 28782.298, 'text': 'okay, now, what do i mean by continuous?', 'start': 28779.876, 'duration': 2.422}, {'end': 28783.659, 'text': 'or the categorical data?', 'start': 28782.298, 'duration': 1.361}, {'end': 28786.581, 'text': 'so the continuous is something as like any value.', 'start': 28783.659, 'duration': 2.922}, {'end': 28791.664, 'text': 'suppose, if i say that here you have the target variable is price right.', 'start': 28786.581, 'duration': 5.083}, {'end': 28793.705, 'text': 'so see, price can be any value.', 'start': 28791.664, 'duration': 2.041}, {'end': 28804.147, 'text': 'either it can be 23.5, okay, either it can be 24.2, or either it can be 25, or any value can be there in the price.', 'start': 28793.705, 'duration': 10.442}, {'end': 28808.85, 'text': 'or if i say that salary, right, if i say weight of the people, height of the people.', 'start': 28804.147, 'duration': 4.703}], 'summary': 'Continuous data can take any value, for example 23.5, 24.2, or 25, and applies to variables such as price, salary, weight, and height.', 'duration': 28.974, 'max_score': 28779.876, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI28779876.jpg'}, {'end': 29374.271, 'src': 'embed', 'start': 29339.076, 'weight': 1, 'content': [{'end': 29343.9, 'text': 'or. you can relate this equation with your equation of line as well.', 'start': 29339.076, 'duration': 4.824}, {'end': 29345.581, 'text': 'i hope you all know the equation of line.', 'start': 29343.9, 'duration': 1.681}, {'end': 29347.002, 'text': 'what is the equation of line?', 'start': 29345.581, 'duration': 1.421}, {'end': 29354.468, 'text': 'y is equals to m, x plus c.', 'start': 29347.002, 'duration': 7.466}, {'end': 29356.73, 'text': 'okay, so what is your c?', 'start': 29354.468, 'duration': 2.262}, {'end': 29359.492, 'text': 'c is usually my intercept.', 'start': 29356.73, 'duration': 2.762}, {'end': 29368.429, 'text': 'right, and what is your M?', 'start': 29359.492, 'duration': 8.937}, {'end': 29371.89, 'text': 'M will be your slope of the line.', 'start': 29368.429, 'duration': 3.461}, {'end': 29374.271, 'text': 'okay, what will be the slope of line?', 'start': 29371.89, 'duration': 2.381}], 'summary': 'Discussion about the equation of a line: y = mx + c, where c is the intercept and m is the slope.', 'duration': 35.195, 'max_score': 29339.076, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI29339076.jpg'}], 'start': 28668.543, 'title': 'Supervised machine learning and linear regression', 'summary': 'Covers the key steps in supervised machine learning, distinguishing between regression and classification problems, and delving into linear regression in mathematics with practical examples and calculations, emphasizing the importance of target variable and providing insights into predicted values.', 'chapters': [{'end': 28822.778, 'start': 28668.543, 'title': 'Understanding supervised machine learning', 'summary': 'Discusses the key steps in supervised machine learning, emphasizing the importance of checking for the presence and type of the target variable when beginning a project.', 'duration': 154.235, 'highlights': ['The first step in a machine learning problem is to check for the presence of the target variable, either present or absent.', 'If the target variable is present, the next step is to determine its type, which can be continuous or categorical data.', 'Continuous data includes values like price, salary, weight, and height, while categorical data consists of specific categories or groups.']}, {'end': 29082.345, 'start': 28822.778, 'title': 'Regression vs classification in data analysis', 'summary': 'Explains the difference between regression and classification problems, examples of each, and the utility of regression analysis for cause and effect measures, such as temperature on humidity, and its impact on various scenarios.', 'duration': 259.567, 'highlights': ['Regression analysis is a statistical tool for cause and effect measures, such as the effect of temperature on humidity, and its impact on various scenarios. Regression analysis is a statistical tool that is very effective in cause and effect manner, such as the effect of temperature on humidity, the interrelation of height and weight, and the effect of hydrocarbon level on water purity during the distillation process.', 'Distinguishing between continuous and categorical target variables is crucial in determining whether the problem is a regression or classification problem. The distinction between continuous and categorical target variables determines whether the problem is a regression or classification problem, with continuous variables leading to regression problems and categorical variables leading to classification problems.', 'Provided examples of classification problems, such as predicting gender or determining whether a person will go on a date, with the outcomes being either male/female or going on a date/not going on a date. Examples of classification problems include predicting gender (male/female) and determining whether a person will go on a date or not, illustrating the binary outcomes associated with such problems.']}, {'end': 29460.956, 'start': 29082.345, 'title': 'Linear regression in mathematics', 'summary': 'Discusses linear regression in mathematics, using an example of predicting the number of cars sold based on the number of ads, explaining the concepts of independent and target variables, and providing insights into the linear regression equation y = beta_0 + beta_1 * x and its graphical representation.', 'duration': 378.611, 'highlights': ['Explaining the relationship between the number of cars sold and the number of ads in a data set, with specific examples and their corresponding values. The example demonstrates the relationship between the number of ads and the number of cars sold, such as when there are two ads, one car is sold, and when there are 10 ads, 13 cars are sold.', 'Defining the independent variable as the number of ads and the target variable as the number of cars sold in the context of predicting car sales based on ad numbers. The independent variable is the number of ads, while the target variable is the number of cars sold, forming the basis for prediction in the model.', 'Introducing the concept of linear regression equation y = beta_0 + beta_1 * x, relating it to the equation of a line y = mx + c, and explaining the meaning of beta_0 and beta_1 as the intercept and slope/coefficient of x. The linear regression equation y = beta_0 + beta_1 * x is explained by relating it to the equation of a line, where beta_0 represents the intercept, and beta_1 represents the slope or coefficient of x.', "Providing a graphical representation of the linear regression line and its components, including the intercept beta_0 and the regression line's relationship with the x and y axes. The graphical representation illustrates the placement of the regression line, indicating the intercept beta_0 and its relationship with the x and y axes for visualizing the linear regression model."]}, {'end': 30300.301, 'start': 29461.056, 'title': 'Linear regression calculation', 'summary': 'Explains the calculation of linear regression, including finding the regression line equation with a slope of 5 and an intercept of 9.2, based on a small dataset of number of tv ads and number of cars sold.', 'duration': 839.245, 'highlights': ['The formula for calculating the slope (m) in linear regression is demonstrated using the given dataset. The formula for calculating the slope (m) involves finding the mean of x and y, then using the summation of x minus x bar and y minus y bar to calculate m, resulting in a slope of 5 for the given dataset.', 'The process of finding the intercept (c) in the linear regression equation is explained, with a specific example showcasing the calculation. The explanation demonstrates that the intercept (c) in the linear regression equation is 9.2 by utilizing the mean of x and y in the formula, resulting in the equation y = 5x + 9.2 for the given dataset.']}, {'end': 30717.191, 'start': 30300.301, 'title': 'Regression line & predicted values', 'summary': 'Discusses the concept of y hat (predicted y) by calculating the predicted values using the regression equation and plotting the regression line with actual and predicted data points.', 'duration': 416.89, 'highlights': ['The concept of y hat (predicted y) is explained by calculating the predicted values using the regression equation. Explanation of y hat, calculation of predicted values, use of regression equation.', 'The process of plotting the regression line with actual and predicted data points is described. Description of plotting regression line, inclusion of actual and predicted data points.', 'Calculation of predicted y values for different x values: 29.2 for x=4, 24.2 for x=3, 19.2 for x=2, and 14.2 for x=1. Specific predicted y values calculated for different x values.']}], 'duration': 2048.648, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI28668543.jpg', 'highlights': ['Distinguishing between continuous and categorical target variables determines whether the problem is a regression or classification problem.', 'Provided examples of classification problems, such as predicting gender or determining whether a person will go on a date.', 'The linear regression equation y = beta_0 + beta_1 * x is explained by relating it to the equation of a line.', 'The formula for calculating the slope (m) in linear regression is demonstrated using the given dataset.', 'The concept of y hat (predicted y) is explained by calculating the predicted values using the regression equation.']}, {'end': 32321.328, 'segs': [{'end': 32169.179, 'src': 'embed', 'start': 32116.128, 'weight': 0, 'content': [{'end': 32125.391, 'text': 'so therefore, uh, if we look at this data set so, we find out that this median underscore income column is scaled.', 'start': 32116.128, 'duration': 9.263}, {'end': 32127.572, 'text': 'okay, now, what do i mean by scale?', 'start': 32125.391, 'duration': 2.181}, {'end': 32129.173, 'text': "that i'll tell you a little bit later.", 'start': 32127.572, 'duration': 1.601}, {'end': 32134.902, 'text': 'scaled means that we can scale our largest values to a specific range.', 'start': 32129.173, 'duration': 5.729}, {'end': 32139.284, 'text': 'and for scaling there are many feature scaling parameters available.', 'start': 32134.902, 'duration': 4.382}, {'end': 32146.888, 'text': "but as of now, let's say that that this, your median house median income, is the scaled column in your data set.", 'start': 32139.284, 'duration': 7.604}, {'end': 32151.17, 'text': 'okay, and if i talk about the info, housing.info.', 'start': 32146.888, 'duration': 4.282}, {'end': 32155.472, 'text': 'so you will see that how many total values are there in your data set?', 'start': 32151.17, 'duration': 4.302}, {'end': 32164.178, 'text': 'there are total 20 640 entries and how many columns are there?', 'start': 32155.472, 'duration': 8.706}, {'end': 32166.898, 'text': 'there are total 10 columns.', 'start': 32164.178, 'duration': 2.72}, {'end': 32168.639, 'text': 'first you have the data set with you.', 'start': 32166.898, 'duration': 1.741}, {'end': 32169.179, 'text': 'you will check.', 'start': 32168.639, 'duration': 0.54}], 'summary': 'The data set contains 20,640 entries and 10 columns, with the median income column being scaled.', 'duration': 53.051, 'max_score': 32116.128, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI32116128.jpg'}], 'start': 30717.191, 'title': 'Linear regression and assumptions in data analysis', 'summary': 'Covers the importance of minimizing error in linear regression, understanding goodness of fit, and addressing assumptions such as linear distribution, normality, multicollinearity, autocorrelation, and homoscedasticity, with practical examples and visualization techniques.', 'chapters': [{'end': 30853.998, 'start': 30717.191, 'title': 'Linear regression and error minimization', 'summary': 'Explains the importance of minimizing the distance between the regression line and predicted data points in linear regression, with a focus on reducing the error term, y-y^, to achieve the best fit line and minimize the cost function.', 'duration': 136.807, 'highlights': ['The main focus in linear regression is to minimize the distance between the regression line and the predicted data points, aiming for the least possible error, as it is considered as your errors (24.2, 19.2, 17).', 'The error term in linear regression is calculated as the difference between actual and predicted values (y-y^), and the goal is to always reduce this error to achieve the best fit line (error should be less, of course) and minimize the cost function (y-y^) (equation plus error, reduce the error) .', 'The linear regression model aims to find the best fit line with the least distance between the regression line and the predicted data points, in order to minimize the error term and the cost function (distance between the regression line and the predicted data points, least distance in between the linear regression line and the predicted data points).']}, {'end': 31403.126, 'start': 30853.998, 'title': 'Linear regression and goodness of fit', 'summary': 'Explains linear regression, including the process to find the best fit line, minimizing errors using the cost function, and understanding the goodness of fit through terms like sum of squares due to regression, sum of squares of error, and sum of squares total.', 'duration': 549.128, 'highlights': ['The process to find the best fit line involves ensuring one of the coordinates of a line passes through the average or mean of points, followed by minimizing errors using the cost function. The best fit line is determined by ensuring it passes through the average or mean of points and then minimizing errors using the cost function. This process is fundamental to understanding linear regression.', 'The cost function of linear regression, also known as the least square method, involves minimizing the error, and it is represented by the formula y - y cap whole square. The cost function of linear regression, known as the least square method, is represented by the formula y - y cap whole square, and it involves minimizing the error to achieve the best fit line.', 'Understanding the goodness of fit is achieved through terms like sum of squares due to regression, sum of squares of error, and sum of squares total, which provide insights into the accuracy and errors of the regression model. The terms like sum of squares due to regression, sum of squares of error, and sum of squares total provide insights into the accuracy and errors of the regression model, contributing to understanding the goodness of fit.']}, {'end': 31692.794, 'start': 31404.448, 'title': 'Linear regression assumptions', 'summary': 'Discusses the five assumptions of linear regression, including linear distribution and the presence of normality, emphasizing the need for linearly distributed data and the requirement for normal distribution in datasets with more than 30 observations.', 'duration': 288.346, 'highlights': ['Linear Distribution: Data should be linearly distributed, with the relationship between independent and target variables being linear, verified by plotting a scatter plot. The first assumption of linear regression is that the data should be linearly distributed, with a linear relationship between the independent and target variables. This can be verified by plotting a scatter plot to check the linear distribution.', 'Presence of Normality: Data should follow a normal distribution, automatically occurring when the dataset has more than 30 observations, confirmed by drawing a histogram. The second assumption is the presence of normality, where the dataset should follow a normal distribution. This automatically occurs when the dataset has more than 30 observations, and can be confirmed by drawing a histogram.']}, {'end': 32321.328, 'start': 31692.794, 'title': 'Understanding assumptions in data analysis', 'summary': 'Discusses the key assumptions in data analysis, including multicollinearity, autocorrelation, and homoscedasticity, and demonstrates how to identify and address these assumptions using correlation analysis and visualization techniques, with practical examples and code snippets.', 'duration': 628.534, 'highlights': ['Multicollinearity means that two independent variables should not be heavily dependent on each other, which can be identified using correlation analysis and heat maps to ensure minimal or no multicollinearity in the data. The concept of multicollinearity is explained, emphasizing the importance of minimal or no dependence between independent variables, demonstrated through the example of date of birth and age, and the use of correlation analysis and heat maps for identification.', 'Autocorrelation in data analysis implies that the prediction vector should not be dependent on previous assumptions, commonly observed in time series data like stock market analysis, and can be visualized using line plots or geometric plots. Explanation of autocorrelation in the context of sales data, emphasizing the independence of predictions from previous values and its relevance in time series analysis, with visualization methods such as line plots or geometric plots for assessment.', 'Homoscedasticity, a property of regression models, requires that errors are consistent across input values, ensuring a scatter plot without drastic variations in errors, and can be assessed using visualization techniques. Definition and importance of homoscedasticity in regression models, stressing the need for consistent errors across input values and the impact of heteroscedasticity on the regression model, with the use of scatter plots for evaluation.', 'Identification of the features in the dataset, including longitude, latitude, housing median age, total rooms, total bedrooms, population, households, median income, median house value, and ocean proximity, with a focus on the target variable as the median house value. Listing and explanation of the features in the dataset, highlighting the target variable as the median house value and emphasizing the relevance of the features in predicting the housing median age, total rooms, total bedrooms, population, and median house value.', 'Discussion of handling null values in the dataset, including methods such as removing rows with null values, dropping columns with null values, or filling null values based on correlation analysis with the target variable, demonstrating a systematic approach to null value treatment. Explanation of various methods for dealing with null values in the dataset, including removal of rows, dropping columns, or filling null values based on correlation analysis with the target variable, highlighting the importance of a systematic approach for null value treatment.']}], 'duration': 1604.137, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI30717191.jpg', 'highlights': ['The main focus in linear regression is to minimize the distance between the regression line and the predicted data points, aiming for the least possible error (24.2, 19.2, 17).', 'Understanding the goodness of fit is achieved through terms like sum of squares due to regression, sum of squares of error, and sum of squares total, which provide insights into the accuracy and errors of the regression model.', 'Data should follow a normal distribution, automatically occurring when the dataset has more than 30 observations, confirmed by drawing a histogram.', 'Multicollinearity means that two independent variables should not be heavily dependent on each other, which can be identified using correlation analysis and heat maps to ensure minimal or no multicollinearity in the data.', 'Homoscedasticity, a property of regression models, requires that errors are consistent across input values, ensuring a scatter plot without drastic variations in errors, and can be assessed using visualization techniques.', 'Identification of the features in the dataset, including longitude, latitude, housing median age, total rooms, total bedrooms, population, households, median income, median house value, and ocean proximity, with a focus on the target variable as the median house value.', 'Discussion of handling null values in the dataset, including methods such as removing rows with null values, dropping columns with null values, or filling null values based on correlation analysis with the target variable, demonstrating a systematic approach to null value treatment.']}, {'end': 34503.882, 'segs': [{'end': 32463.653, 'src': 'embed', 'start': 32434.204, 'weight': 4, 'content': [{'end': 32437.427, 'text': 'And for permanent changes, in place is equals to true.', 'start': 32434.204, 'duration': 3.223}, {'end': 32439.43, 'text': 'And then you have house.head.', 'start': 32437.588, 'duration': 1.842}, {'end': 32443.594, 'text': 'So what does it mean? That your total underscore bedroom has been dropped.', 'start': 32439.55, 'duration': 4.044}, {'end': 32446.097, 'text': 'Okay, so I think we should proceed now.', 'start': 32444.014, 'duration': 2.083}, {'end': 32449.404, 'text': 'cool, so i just dropped this column.', 'start': 32446.843, 'duration': 2.561}, {'end': 32457.029, 'text': 'see, now all seems to be good in my data set, but the problem is with the ocean underscore proximity column.', 'start': 32449.404, 'duration': 7.625}, {'end': 32463.653, 'text': 'i told you that we cannot pass the text categorical data to the machine learning models.', 'start': 32457.029, 'duration': 6.624}], 'summary': 'Dropped total bedroom column, issue with ocean_proximity categorical data.', 'duration': 29.449, 'max_score': 32434.204, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI32434204.jpg'}, {'end': 33688.173, 'src': 'embed', 'start': 33641.397, 'weight': 5, 'content': [{'end': 33646.082, 'text': 'So if the length of the item set is 6, then the color of the dot would be yellow.', 'start': 33641.397, 'duration': 4.685}, {'end': 33651.548, 'text': 'Similarly, if the length of the item set is 2, then the color of the dot would be purple.', 'start': 33646.583, 'duration': 4.965}, {'end': 33657.774, 'text': 'Similarly, if the length of the item set is 5, then the color of the dot would be green over here.', 'start': 33652.088, 'duration': 5.686}, {'end': 33660.918, 'text': "So let's examine this rule over here.", 'start': 33658.675, 'duration': 2.243}, {'end': 33669.088, 'text': 'So since the length of the item set is 6, so there are 5 items in the antecedent and there is 1 item in the consequent.', 'start': 33661.457, 'duration': 7.631}, {'end': 33676.343, 'text': 'So here the support value is 0.007 and the confidence value is 0.9.', 'start': 33669.849, 'duration': 6.494}, {'end': 33678.365, 'text': "Similarly, let's examine this over here.", 'start': 33676.343, 'duration': 2.022}, {'end': 33685.651, 'text': 'So the length of the rule is 3 over here and the items in the antecedent are pink Regency teacup and saucer,', 'start': 33678.905, 'duration': 6.746}, {'end': 33688.173, 'text': 'and we also have roses Regency teacup and saucer.', 'start': 33685.651, 'duration': 2.522}], 'summary': 'Analysis of item sets: 6 items yield yellow dot, 2 items yield purple, and 5 items yield green. support value: 0.007, confidence value: 0.9.', 'duration': 46.776, 'max_score': 33641.397, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI33641397.jpg'}, {'end': 33803.333, 'src': 'embed', 'start': 33771.406, 'weight': 2, 'content': [{'end': 33775.168, 'text': 'These item sets are completely different and these item sets are completely different.', 'start': 33771.406, 'duration': 3.762}, {'end': 33780.05, 'text': 'That is, there is no association between these three set of items over here.', 'start': 33775.528, 'duration': 4.522}, {'end': 33783.957, 'text': 'So let me zoom in again and let me have a glance at this rule.', 'start': 33781.155, 'duration': 2.802}, {'end': 33792.804, 'text': "So this is rule number 100 over here, which states that if someone buys Dolly Girl Children's Cup and Space Boy Children's Bowl,", 'start': 33784.638, 'duration': 8.166}, {'end': 33795.507, 'text': "then he's also likely to buy Dolly Girl Children's Bowl.", 'start': 33792.804, 'duration': 2.703}, {'end': 33803.333, 'text': 'Similarly let me zoom out and let me have a glance at this rule over here.', 'start': 33797.368, 'duration': 5.965}], 'summary': "Association rule 100 indicates buying dolly girl children's cup and space boy children's bowl is likely to lead to buying dolly girl children's bowl.", 'duration': 31.927, 'max_score': 33771.406, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI33771406.jpg'}, {'end': 34085.681, 'src': 'embed', 'start': 34058.143, 'weight': 0, 'content': [{'end': 34061.564, 'text': 'So you see these bright red colored dots over here.', 'start': 34058.143, 'duration': 3.421}, {'end': 34070.09, 'text': "So these are the dots where the maximum left, So lift of these dots is more than 85, so it's around 89, 90, right?", 'start': 34062.085, 'duration': 8.005}, {'end': 34081.238, 'text': 'So if you want a lift of 90, then the confidence should be greater than 0.8 and the support should be less than 0.01..', 'start': 34070.651, 'duration': 10.587}, {'end': 34083.379, 'text': "Now let's go ahead and make another plot.", 'start': 34081.238, 'duration': 2.141}, {'end': 34085.681, 'text': "So this time we'll be making the two key plot.", 'start': 34083.859, 'duration': 1.822}], 'summary': 'Identifying dots with lift > 85, suggesting lift of 89-90, requiring confidence > 0.8 and support < 0.01.', 'duration': 27.538, 'max_score': 34058.143, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI34058143.jpg'}, {'end': 34323.654, 'src': 'embed', 'start': 34236.645, 'weight': 1, 'content': [{'end': 34243.447, 'text': 'Here we will build a priori algorithm with support value 0.02 and confidence value 0.5.', 'start': 34236.645, 'duration': 6.802}, {'end': 34249.091, 'text': 'Then we sort the rules with respect to support and inspect the top five rules and the bottom five rules.', 'start': 34243.447, 'duration': 5.644}, {'end': 34252.834, 'text': 'And finally, we will plot the rules using different methods.', 'start': 34249.772, 'duration': 3.062}, {'end': 34255.456, 'text': 'So guys, we are back to our studio.', 'start': 34253.734, 'duration': 1.722}, {'end': 34259.479, 'text': "So let's go ahead and build a third set of association rules.", 'start': 34255.756, 'duration': 3.723}, {'end': 34262.22, 'text': 'And this would be the command for that.', 'start': 34260.579, 'duration': 1.641}, {'end': 34276.417, 'text': 'So here on top of the market basket data set, I am building the a priori algorithm where the support value was 0.02 and the confidence value is 0.5.', 'start': 34263.171, 'duration': 13.246}, {'end': 34278.818, 'text': 'And I am sorting those rules by support.', 'start': 34276.417, 'duration': 2.401}, {'end': 34284.561, 'text': 'Right So now let me have a glance at the summary of these three rules.', 'start': 34280.919, 'duration': 3.642}, {'end': 34287.302, 'text': 'So summary of rule three.', 'start': 34285.241, 'duration': 2.061}, {'end': 34289.476, 'text': 'Let me maximize this.', 'start': 34288.396, 'duration': 1.08}, {'end': 34293.818, 'text': 'So we have item sets with just two different length distributions over here.', 'start': 34290.457, 'duration': 3.361}, {'end': 34298.32, 'text': 'So there are 26 rules with the item set length to be two.', 'start': 34294.398, 'duration': 3.922}, {'end': 34302.322, 'text': 'And there are three rules whose item set length is three.', 'start': 34298.94, 'duration': 3.382}, {'end': 34305.043, 'text': 'Now let me examine the top five rules.', 'start': 34302.922, 'duration': 2.121}, {'end': 34318.311, 'text': 'right. so this is the first rule which tells us if someone buys jumbo bag pink polka dot, he is also likely to buy jumbo bag red retro spot,', 'start': 34308.945, 'duration': 9.366}, {'end': 34323.654, 'text': 'and the support for this is 0.029 and the confidence for this is 0.62.', 'start': 34318.311, 'duration': 5.343}], 'summary': 'Built a priori algorithm, sorted rules by support, and analyzed top & bottom five rules.', 'duration': 87.009, 'max_score': 34236.645, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI34236645.jpg'}, {'end': 34423.584, 'src': 'embed', 'start': 34374.065, 'weight': 10, 'content': [{'end': 34376.606, 'text': "So plot of rule three, I'm making a basic plot.", 'start': 34374.065, 'duration': 2.541}, {'end': 34380.643, 'text': "Now we see that we can't really find some association with this.", 'start': 34378.22, 'duration': 2.423}, {'end': 34382.245, 'text': 'So these are quite scattered over here.', 'start': 34380.843, 'duration': 1.402}, {'end': 34386.389, 'text': "So let's go ahead and make the two key plot now.", 'start': 34383.766, 'duration': 2.623}, {'end': 34391.095, 'text': "Right So I'm using the plot function.", 'start': 34389.112, 'duration': 1.983}, {'end': 34393.798, 'text': "I'm making the plot for rule three and the method is two key.", 'start': 34391.395, 'duration': 2.403}, {'end': 34399.513, 'text': 'So we have two different colors over here.', 'start': 34397.332, 'duration': 2.181}, {'end': 34407.116, 'text': 'Yellow is for those rules where the length of the item set is 3 and purple is for those rules where the length of the item set is 2.', 'start': 34400.133, 'duration': 6.983}, {'end': 34410.938, 'text': 'So now if we take these three dots over here, this is for yellow.', 'start': 34407.116, 'duration': 3.822}, {'end': 34415.1, 'text': 'So this is for all of those rules whose length of the item set is 3.', 'start': 34411.538, 'duration': 3.562}, {'end': 34423.584, 'text': 'And we see that the support value needs to be less than 0.02 and the confidence value needs to be greater than 0.7.', 'start': 34415.1, 'duration': 8.484}], 'summary': 'Plotting a visualization for rule three, showing support and confidence values for item sets of length 2 and 3.', 'duration': 49.519, 'max_score': 34374.065, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI34374065.jpg'}], 'start': 32322.008, 'title': 'Supermarket analysis', 'summary': 'Covers feature selection, label encoding, and association rules analysis in a supermarket dataset, with key points such as removal of weak predictor, identification of frequently bought items, and application of a priori algorithm with varying support and confidence values, resulting in insights into association rules and purchase probabilities.', 'chapters': [{'end': 32712.286, 'start': 32322.008, 'title': 'Feature selection and label encoding', 'summary': "Discusses the identification of strong and weak predictors for a target variable, leading to the removal of a weak predictor column 'total bedroom' and the process of label encoding for the 'ocean proximity' categorical column using sklearn library.", 'duration': 390.278, 'highlights': ['The identified strong predictors for the target variable are median income, total rooms, and housing median age, while weak predictors include total bedroom and household, with a correlation coefficient of -0.007, indicating a very weak correlation.', 'The negative predictors, longitude and population, are also recognized and dropped from the dataset due to their weak correlation with the target variable, with longitude having a correlation coefficient of -0.04 and population having a correlation coefficient of 0.004.', "The process of dropping the 'total bedroom' column from the dataset is highlighted, involving the use of 'dot drop' method in pandas to remove the column containing null values, ensuring the data set is optimized for model training.", "The necessity of converting the 'ocean proximity' categorical column into numeric values is emphasized, with the explanation of label encoding using the sklearn library, which assigns ordinal values to the categorical data, transforming the text categories into numerical representations for machine learning models.", "The label encoding process is explained in detail, highlighting the use of the label encoder from the sklearn library to transform the 'ocean proximity' column, encoding categories such as 'near bay' to the numerical value 3, enabling the conversion of text categorical data into numeric representations for model compatibility."]}, {'end': 33152.64, 'start': 32712.286, 'title': 'Supermarket cross-selling analysis', 'summary': 'Discusses the task of increasing cross-selling in a supermarket by finding the association between different items, with a focus on understanding transactions, finding total number of transactions and items, and identifying the 10 most frequently bought items using rstudio.', 'duration': 440.354, 'highlights': ["The total number of transactions in the data set is 18,440, and the total number of items in the inventory is 22,346. The number of transactions and items in the inventory are quantified as 18,440 and 22,346 respectively, providing essential insights into the dataset's scale and scope.", 'In total, 485,582 items were purchased, indicating the high volume of items transacted in the supermarket. The total number of items purchased is quantified as 485,582, showcasing the substantial volume of items being transacted in the supermarket.', 'The chapter discusses implementing the market basket dataset in RStudio to analyze transactions and find the 10 most frequently bought items. The process of implementing the market basket dataset in RStudio to analyze transactions and identify the 10 most frequently bought items is outlined, emphasizing practical application and analysis.']}, {'end': 33468.711, 'start': 33152.64, 'title': 'Association rules analysis', 'summary': 'Covers the analysis of the 10 most frequently purchased items, building an a priori algorithm with support value 0.005 and confidence value 0.8, and sorting and inspecting rules with respect to confidence and lift, with key points including the top and bottom rules and their respective support and confidence values.', 'duration': 316.071, 'highlights': ['The white hanging heart tea light holder is the most frequently purchased item. This item is identified as the most frequently purchased one from the entire dataset.', 'There are 6 rules comprised of 6 items, 79 rules with 5 items, 211 rules with 4 items, 201 rules with 3 items, and 64 rules with 2 items. The distribution of rules based on the number of items in the antecedent and consequent.', 'The top 5 rules sorted with respect to confidence include purchasing garbage design and key fob with a support value of 0.005 and a confidence value of 1. Detailed description of the top 5 rules sorted by confidence, including support and confidence values.', 'The bottom 5 rules include antecedents with two items and a consequent with one item. Description of the bottom 5 rules, highlighting the structure of antecedents and consequents.']}, {'end': 33744.433, 'start': 33469.352, 'title': 'Association rule analysis insights', 'summary': 'Explores association rule analysis insights from a dataset, including support values and confidence intervals, with a focus on identifying item set likelihood and purchase probabilities, sorting rules with respect to lift, and visualizing data through plotting and graph methods.', 'duration': 275.081, 'highlights': ["The support value of 0.007 indicates that if someone buys herb marker mint, herb marker parsley, and herb marker thyme, there's a 0.7% likelihood of also buying herb marker chives. Support value of 0.007 for the rule involving herb marker mint, herb marker parsley, and herb marker thyme, with a 0.7% likelihood of also buying herb marker chives.", "The confidence value of 0.96 suggests that if someone buys Dolly Girl Children's Cup and Space Boy Children's Bowl, there's a 96% likelihood of also buying Dolly Girl Children's Bowl. Confidence value of 0.96 for the rule regarding Dolly Girl Children's Cup and Space Boy Children's Bowl, with a 96% likelihood of also buying Dolly Girl Children's Bowl.", 'The chapter emphasizes sorting rules with respect to lift and inspecting the top five rules, providing insights into item set probabilities and purchase likelihood. Emphasis on sorting rules with respect to lift and inspecting the top five rules to provide insights into item set probabilities and purchase likelihood.', 'Utilizing plotting and graph methods to visualize data, with a focus on segregating points based on the length of the item set and showcasing graphical results for rule analysis. Use of plotting and graph methods to visualize data, segregating points based on the length of the item set, and showcasing graphical results for rule analysis.']}, {'end': 34085.681, 'start': 33744.433, 'title': 'Association rules analysis', 'summary': 'Discusses association rules analysis, with a focus on building a priori algorithm with support value 0.009 and confidence value 0.3, resulting in 837 rules with item set lengths of 2, 3, or 4, and highlights the top and bottom rules, showcasing insights into likelihood and confidence values.', 'duration': 341.248, 'highlights': ['The a priori algorithm is built with a support value of 0.009 and a confidence value of 0.3, resulting in 837 rules with item set lengths of 2, 3, or 4.', 'The top five rules showcase high confidence levels, such as 100% likelihood of buying specific items, with support values ranging from 0.011 to 0.013.', 'Inspection of the bottom five rules reveals lower confidence levels, with support values around 0.011 to 0.012 and a confidence value of 0.3, indicating a 30% likelihood of buying specific items.', 'An interactive plot is used to visualize the association rules, emphasizing the relationship between lift, confidence, and support values to identify significant patterns.']}, {'end': 34503.882, 'start': 34085.781, 'title': 'Association rules analysis', 'summary': 'Discusses the analysis of association rules, including support and confidence values for different item set lengths, with a focus on rules with length 2 and 3, and the application of a priori algorithm with support value 0.02 and confidence value 0.5, resulting in the identification and examination of top and bottom rules.', 'duration': 418.101, 'highlights': ['The chapter discusses the analysis of association rules, including support and confidence values for different item set lengths, with a focus on rules with length 2 and 3. It explains the categorization of rules based on item set lengths and the corresponding support and confidence value criteria.', 'The application of a priori algorithm with support value 0.02 and confidence value 0.5, resulting in the identification and examination of top and bottom rules. It mentions the specific parameters used for the a priori algorithm and the subsequent examination of top and bottom rules based on support and confidence values.', 'The identification and examination of top and bottom rules based on support and confidence values. It emphasizes the process of identifying and analyzing the top and bottom rules, providing examples of specific rules and their associated support and confidence values.']}], 'duration': 2181.874, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI32322008.jpg', 'highlights': ['The identified strong predictors for the target variable are median income, total rooms, and housing median age, while weak predictors include total bedroom and household, with a correlation coefficient of -0.007, indicating a very weak correlation.', 'The negative predictors, longitude and population, are also recognized and dropped from the dataset due to their weak correlation with the target variable, with longitude having a correlation coefficient of -0.04 and population having a correlation coefficient of 0.004.', 'The total number of transactions in the data set is 18,440, and the total number of items in the inventory is 22,346.', 'In total, 485,582 items were purchased, indicating the high volume of items transacted in the supermarket.', 'The white hanging heart tea light holder is the most frequently purchased item.', 'There are 6 rules comprised of 6 items, 79 rules with 5 items, 211 rules with 4 items, 201 rules with 3 items, and 64 rules with 2 items.', "The support value of 0.007 indicates that if someone buys herb marker mint, herb marker parsley, and herb marker thyme, there's a 0.7% likelihood of also buying herb marker chives.", "The confidence value of 0.96 suggests that if someone buys Dolly Girl Children's Cup and Space Boy Children's Bowl, there's a 96% likelihood of also buying Dolly Girl Children's Bowl.", 'The a priori algorithm is built with a support value of 0.009 and a confidence value of 0.3, resulting in 837 rules with item set lengths of 2, 3, or 4.', 'The top five rules showcase high confidence levels, such as 100% likelihood of buying specific items, with support values ranging from 0.011 to 0.013.', 'An interactive plot is used to visualize the association rules, emphasizing the relationship between lift, confidence, and support values to identify significant patterns.', 'The chapter discusses the analysis of association rules, including support and confidence values for different item set lengths, with a focus on rules with length 2 and 3.', 'The application of a priori algorithm with support value 0.02 and confidence value 0.5, resulting in the identification and examination of top and bottom rules.', 'The identification and examination of top and bottom rules based on support and confidence values.']}, {'end': 36980.373, 'segs': [{'end': 34567.543, 'src': 'embed', 'start': 34534.563, 'weight': 2, 'content': [{'end': 34536.845, 'text': 'So there are a total of 980,000 ratings for 10,000 books from 53,424 users.', 'start': 34534.563, 'duration': 2.282}, {'end': 34550.095, 'text': "So the books.csv contains more information on the books such as the author's name, publication year, book ID and so on.", 'start': 34543.072, 'duration': 7.023}, {'end': 34552.736, 'text': 'Then we have the booktags.csv file.', 'start': 34550.835, 'duration': 1.901}, {'end': 34559.319, 'text': 'So this file comprises of all tag IDs users have assigned to the books and the corresponding tag counts.', 'start': 34553.317, 'duration': 6.002}, {'end': 34567.543, 'text': 'So the tag IDs basically denote the categories into which the books fall into and the counts denote the number of books belonging to each category.', 'start': 34560.039, 'duration': 7.504}], 'summary': '980,000 ratings for 10,000 books from 53,424 users, along with additional details on authors, publication years, and book categories.', 'duration': 32.98, 'max_score': 34534.563, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI34534563.jpg'}, {'end': 34977.4, 'src': 'embed', 'start': 34946.584, 'weight': 1, 'content': [{'end': 34949.445, 'text': "So I will select this command over here and I'll paste it over here.", 'start': 34946.584, 'duration': 2.861}, {'end': 34954.747, 'text': "So I have given ratings over here and I'm grouping this ratings with respect to user ID.", 'start': 34949.905, 'duration': 4.842}, {'end': 34963.991, 'text': 'After which I am using the mutate function and over here again I am adding a new column, and that new column would be ratings given,', 'start': 34955.347, 'duration': 8.644}, {'end': 34969.152, 'text': 'and I will get that ratings given column with the help of this n function from the deployer package.', 'start': 34963.991, 'duration': 5.161}, {'end': 34977.4, 'text': 'So this n function from the deployer package would basically give me the number of ratings given by each user right.', 'start': 34969.733, 'duration': 7.667}], 'summary': 'Using mutate and n function to calculate ratings given by each user.', 'duration': 30.816, 'max_score': 34946.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI34946584.jpg'}, {'end': 35875.575, 'src': 'embed', 'start': 35849.976, 'weight': 7, 'content': [{'end': 35856.96, 'text': "and since we'd want to make a bar plot, we'll be using the jomba function and the stat which I've used as identity.", 'start': 35849.976, 'duration': 6.984}, {'end': 35865.267, 'text': "And I'll also use the quad flip function over here because I'd want these bars to be stacked horizontally and not vertically.", 'start': 35857.501, 'duration': 7.766}, {'end': 35870.571, 'text': 'And the color to these bars would be determined by this palette over here.', 'start': 35865.947, 'duration': 4.624}, {'end': 35872.713, 'text': 'So this is Y-L-O-R-D.', 'start': 35870.651, 'duration': 2.062}, {'end': 35875.575, 'text': 'So this would be for yellow, orange, and red.', 'start': 35872.893, 'duration': 2.682}], 'summary': 'Using jomba function for horizontal bar plot with y-l-o-r-d color palette.', 'duration': 25.599, 'max_score': 35849.976, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI35849976.jpg'}, {'end': 36206.832, 'src': 'embed', 'start': 36177.44, 'weight': 11, 'content': [{'end': 36181.744, 'text': "and we'll also remove the user id column because it doesn't serve a purpose,", 'start': 36177.44, 'duration': 4.304}, {'end': 36187.468, 'text': 'and i will store it in a new object and name that object to be rating mat.', 'start': 36181.744, 'duration': 5.724}, {'end': 36191.131, 'text': 'so let me hit enter right.', 'start': 36187.468, 'duration': 3.663}, {'end': 36193.613, 'text': 'so we have created our rating matrix.', 'start': 36191.131, 'duration': 2.482}, {'end': 36197.785, 'text': 'So we have created the rating mat object.', 'start': 36195.523, 'duration': 2.262}, {'end': 36200.467, 'text': 'Now let me have a glance at the class of this.', 'start': 36197.925, 'duration': 2.542}, {'end': 36203.309, 'text': 'So class of rating mat.', 'start': 36200.967, 'duration': 2.342}, {'end': 36206.832, 'text': 'So we see that this is still in the form of a data frame,', 'start': 36203.889, 'duration': 2.943}], 'summary': "Removed user id column, stored in new object 'rating mat', created rating matrix as a data frame.", 'duration': 29.392, 'max_score': 36177.44, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI36177440.jpg'}, {'end': 36314.05, 'src': 'embed', 'start': 36281.215, 'weight': 0, 'content': [{'end': 36283.296, 'text': 'So these any values which you see over here.', 'start': 36281.215, 'duration': 2.081}, {'end': 36288.298, 'text': 'So this basically means that the first user has not rated the first book.', 'start': 36283.676, 'duration': 4.622}, {'end': 36290.899, 'text': 'The first user has not rated the second book.', 'start': 36288.718, 'duration': 2.181}, {'end': 36294.801, 'text': 'Similarly, the fourth user has not rated the third book and so on.', 'start': 36291.26, 'duration': 3.541}, {'end': 36297.502, 'text': 'Right So we have our rating metrics ready.', 'start': 36295.421, 'duration': 2.081}, {'end': 36303.065, 'text': 'Now, let me also assign the dimension names to the dimension names of this rating mat object.', 'start': 36297.742, 'duration': 5.323}, {'end': 36314.05, 'text': "So here I am assigning all of the dimension names which I've extracted to the dim names of the rating mat.", 'start': 36305.788, 'duration': 8.262}], 'summary': 'Rating matrix shows missing ratings for users and books.', 'duration': 32.835, 'max_score': 36281.215, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI36281215.jpg'}, {'end': 36542.871, 'src': 'embed', 'start': 36518.432, 'weight': 3, 'content': [{'end': 36527.642, 'text': 'So it is a rating matrix where there are 900 rows and 8431 columns and it is of class real rating matrix with 18,832 ratings.', 'start': 36518.432, 'duration': 9.21}, {'end': 36531.781, 'text': 'Right, right.', 'start': 36527.662, 'duration': 4.119}, {'end': 36536.846, 'text': 'so we finally have our real rating metrics ready, so now we can go ahead and build a model on it.', 'start': 36531.781, 'duration': 5.065}, {'end': 36542.871, 'text': "so what we'll do is, well, go ahead and split the data set into train and test sets, so it'll be 80 20 split.", 'start': 36536.846, 'duration': 6.025}], 'summary': 'A real rating matrix with 900 rows, 8431 columns, and 18,832 ratings is ready for model building and will be split into 80:20 for training and testing.', 'duration': 24.439, 'max_score': 36518.432, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI36518432.jpg'}], 'start': 34508.257, 'title': 'Book recommendation dataset and models', 'summary': 'Provides an overview of a book recommendation dataset containing 980,000 ratings for 10,000 books from 53,424 users, covers data cleaning resulting in 960,595 entries, explores genre distribution and top books analysis, and details the process of building user-based and collaborative filtering models for a recommender system.', 'chapters': [{'end': 34583.143, 'start': 34508.257, 'title': 'Book recommendation dataset overview', 'summary': 'Discusses the dataset for the book recommendation case study, which includes 980,000 ratings for 10,000 books from 53,424 users, along with information on book details, tag assignments, and corresponding tag counts.', 'duration': 74.886, 'highlights': ['The dataset comprises 980,000 ratings for 10,000 books from 53,424 users, providing a substantial amount of user feedback.', 'The booktags.csv file contains tag IDs and corresponding tag counts, indicating the categories into which the books fall and the number of books in each category.', 'The tags.csv file provides tag names corresponding to the tag IDs, offering labels for different tag categories.', 'The ratings.csv file contains all user ratings of the books, giving insight into user preferences and book popularity.', "The books.csv file includes additional information on the books, such as author's name, publication year, and book ID, enriching the dataset with comprehensive book details."]}, {'end': 35503.048, 'start': 34583.663, 'title': 'Data cleaning and exploration', 'summary': 'Covers data cleaning, including removing duplicate ratings and users with fewer than three ratings, resulting in 960,595 entries. it also includes data exploration, such as extracting a 2% sample of records, creating a bar plot for rating distribution, and finding the number of ratings for each book.', 'duration': 919.385, 'highlights': ['The data cleaning phase involved removing duplicate ratings, resulting in 4487 instances where the same user rated the same book more than once. 4487 instances of duplicate ratings were identified and removed from the dataset, ensuring data integrity.', 'After data cleaning, there were 960,595 entries remaining, and the second phase involved data exploration, including extracting a 2% sample of records and creating a bar plot for rating distribution. The dataset was reduced to 960,595 entries after cleaning. Data exploration involved extracting a 2% sample of records and creating a bar plot for rating distribution.', 'The plot for the number of ratings for each book revealed that there were more than 2500 instances where a single book was rated by only one user. The analysis showed that more than 2500 books were rated by only one user, providing insights into the distribution of ratings for each book.']}, {'end': 36066.82, 'start': 35505.651, 'title': 'Genre distribution and top books analysis', 'summary': 'Demonstrates the process of obtaining the percentage distribution of different genres, extracting available genres and tags, and analyzing the top 10 books with highest ratings and popularity.', 'duration': 561.169, 'highlights': ["The process involves creating an object 'genres' containing a list of different genres, extracting available genres from the dataset, and obtaining the count and percentage of each genre. Demonstrating the process of obtaining the percentage distribution of different genres, including creating an object 'genres' and extracting available genres from the dataset.", 'Extracting and grouping available tag IDs based on the available genres, followed by creating a plot to visualize the percentage distribution of each genre. Explaining the process of extracting and grouping available tag IDs based on the available genres, and creating a plot to visualize the percentage distribution of each genre.', 'Identifying and arranging the top 10 books with the highest ratings, including their titles and average ratings. Detailing the process of identifying and arranging the top 10 books with the highest ratings, along with their titles and average ratings.', 'Arranging the top 10 most popular books based on the ratings count, and displaying their titles and ratings count. Describing the process of arranging the top 10 most popular books based on the ratings count, and displaying their titles and ratings count.']}, {'end': 36383.964, 'start': 36067.577, 'title': 'Building user-based collaborative filtering model', 'summary': 'Covers building a user-based collaborative filtering model, restructuring data into a matrix, and converting the data frame to a matrix, resulting in a rating matrix with 900 users and 8,431 books, to be used for a recommender system.', 'duration': 316.387, 'highlights': ['Converting data frame to a matrix resulted in a rating matrix with 900 users and 8,431 books. The dimension names were extracted, and the data frame was converted into a matrix, resulting in a rating matrix with 900 users and 8,431 books.', 'Extracting unique user IDs and unique book IDs for structuring the data. Unique user IDs and book IDs were extracted and stored for structuring the data into a matrix.', 'Building user-based collaborative filtering model on the real rating matrix. The data frame was converted into a real rating matrix, necessary for building a user-based collaborative filtering model.']}, {'end': 36980.373, 'start': 36384.932, 'title': 'Building collaborative filtering model', 'summary': 'Details the process of transforming a rating matrix into a real rating matrix, splitting the data into training and testing sets, building a user-based collaborative filtering model, predicting and recommending books to users, and extracting the book titles and authors.', 'duration': 595.441, 'highlights': ['The process of transforming a rating matrix into a real rating matrix is detailed, resulting in a real rating matrix with 900 rows, 8431 columns, and 18,832 ratings. The transformation process yields a real rating matrix with 900 rows, 8431 columns, and 18,832 ratings, providing essential quantifiable data.', 'The method and parameters used in splitting the data into training and testing sets are explained, with an 80-20 split achieved using the sample function. The method and parameters used in splitting the data into training and testing sets are explained, with an 80-20 split achieved using the sample function, providing clear details of the data splitting process.', 'The process of building a user-based collaborative filtering model using the recommender function and specifying the number of books to recommend is outlined. The process of building a user-based collaborative filtering model and specifying the number of books to recommend is outlined, demonstrating a key step in the model building process.', 'The extraction of recommended books for specific users and the retrieval of book titles and authors based on the recommended book IDs is described. The extraction of recommended books for specific users and the retrieval of book titles and authors based on the recommended book IDs is described, providing insight into the recommendation process and user interaction.']}], 'duration': 2472.116, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI34508257.jpg', 'highlights': ['The dataset comprises 980,000 ratings for 10,000 books from 53,424 users, providing a substantial amount of user feedback.', 'The booktags.csv file contains tag IDs and corresponding tag counts, indicating the categories into which the books fall and the number of books in each category.', 'The ratings.csv file contains all user ratings of the books, giving insight into user preferences and book popularity.', 'The data cleaning phase involved removing duplicate ratings, resulting in 4487 instances where the same user rated the same book more than once.', 'After data cleaning, there were 960,595 entries remaining, and the second phase involved data exploration, including extracting a 2% sample of records and creating a bar plot for rating distribution.', "The process involves creating an object 'genres' containing a list of different genres, extracting available genres from the dataset, and obtaining the count and percentage of each genre.", 'Identifying and arranging the top 10 books with the highest ratings, including their titles and average ratings.', 'Converting data frame to a matrix resulted in a rating matrix with 900 users and 8,431 books.', 'The process of transforming a rating matrix into a real rating matrix is detailed, resulting in a real rating matrix with 900 rows, 8431 columns, and 18,832 ratings.', 'The method and parameters used in splitting the data into training and testing sets are explained, with an 80-20 split achieved using the sample function.', 'The process of building a user-based collaborative filtering model using the recommender function and specifying the number of books to recommend is outlined.', 'The extraction of recommended books for specific users and the retrieval of book titles and authors based on the recommended book IDs is described.']}, {'end': 38781.412, 'segs': [{'end': 37561.017, 'src': 'embed', 'start': 37527.967, 'weight': 7, 'content': [{'end': 37530.289, 'text': 'Now again let me have a glance at the modified data frame.', 'start': 37527.967, 'duration': 2.322}, {'end': 37533.492, 'text': 'So view of iris.mis.', 'start': 37530.429, 'duration': 3.063}, {'end': 37536.575, 'text': 'So this is our modified data frame.', 'start': 37534.814, 'duration': 1.761}, {'end': 37541.761, 'text': 'So this 4.2 which you see, this is nothing but the median of this column.', 'start': 37536.796, 'duration': 4.965}, {'end': 37547.526, 'text': 'So wherever there were any values, those any values have been replaced with the median of the petal length.', 'start': 37542.181, 'duration': 5.345}, {'end': 37549.609, 'text': "Now let's head on to the next question.", 'start': 37548.087, 'duration': 1.522}, {'end': 37552.311, 'text': 'So what do you understand by linear regression?', 'start': 37550.129, 'duration': 2.182}, {'end': 37561.017, 'text': 'Well, linear regression is a supervised learning algorithm which helps us in finding the linear relationship between two variables.', 'start': 37552.731, 'duration': 8.286}], 'summary': 'Data frame modified with median of petal length. linear regression is a supervised learning algorithm for finding linear relationship between two variables.', 'duration': 33.05, 'max_score': 37527.967, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI37527967.jpg'}, {'end': 37646.39, 'src': 'embed', 'start': 37623.071, 'weight': 8, 'content': [{'end': 37632.838, 'text': "and now the data scientist at this company wants to understand if there's a linear relationship between the monthly charges incurred by the customer and the tenure of the customer.", 'start': 37623.071, 'duration': 9.767}, {'end': 37639.864, 'text': 'So he collects all of the data and builds a linear model between the monthly charges and the tenure.', 'start': 37633.419, 'duration': 6.445}, {'end': 37646.39, 'text': 'So here monthly charges would be the dependent variable and tenure would be the independent variable,', 'start': 37640.444, 'duration': 5.946}], 'summary': 'Data scientist examines linear relationship between monthly charges and customer tenure.', 'duration': 23.319, 'max_score': 37623.071, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI37623071.jpg'}, {'end': 37729.061, 'src': 'embed', 'start': 37704.765, 'weight': 1, 'content': [{'end': 37711.21, 'text': 'so, as the question stated, mpg is our dependent variable and displacement is our independent variable,', 'start': 37704.765, 'duration': 6.445}, {'end': 37716.954, 'text': 'and we want to try to understand how does mpg vary with respect to the displacement of this column?', 'start': 37711.21, 'duration': 5.744}, {'end': 37721.037, 'text': 'so now, before we go ahead and implement the model on top of this data frame,', 'start': 37716.954, 'duration': 4.083}, {'end': 37729.061, 'text': "We're supposed to divide this data frame into training and testing set so that the model does not overfit the data.", 'start': 37721.397, 'duration': 7.664}], 'summary': 'Analyzing how mpg varies with displacement to create a model for prediction.', 'duration': 24.296, 'max_score': 37704.765, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI37704765.jpg'}, {'end': 37809.277, 'src': 'embed', 'start': 37774.431, 'weight': 5, 'content': [{'end': 37776.872, 'text': 'And then we are supposed to give in the split ratio.', 'start': 37774.431, 'duration': 2.441}, {'end': 37780.873, 'text': 'So I am giving the split ratio would be 0.65.', 'start': 37777.252, 'duration': 3.621}, {'end': 37791.016, 'text': 'That is 65% of the labels of this column would have the true label and the rest 35% of the records would have the false label.', 'start': 37780.873, 'duration': 10.143}, {'end': 37795.078, 'text': 'And I am storing this into the split tag object.', 'start': 37791.457, 'duration': 3.621}, {'end': 37796.92, 'text': "So again, I'm retreating it.", 'start': 37795.638, 'duration': 1.282}, {'end': 37809.277, 'text': 'So I am taking this empty cast dollar mpg column data set and 65% of the records would have the true tag associated with it and the rest 35% of the records would have the false tag associated with it.', 'start': 37797.3, 'duration': 11.977}], 'summary': 'Splitting data with 65% true label and 35% false label.', 'duration': 34.846, 'max_score': 37774.431, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI37774431.jpg'}, {'end': 37895.453, 'src': 'embed', 'start': 37870.955, 'weight': 3, 'content': [{'end': 37878.739, 'text': "now we'll go ahead and build our model on top of the training set and to build the simple linear model we would require the lm function.", 'start': 37870.955, 'duration': 7.784}, {'end': 37881.301, 'text': 'This lm function again takes in two parameters.', 'start': 37879.159, 'duration': 2.142}, {'end': 37882.902, 'text': 'First is the formula.', 'start': 37881.761, 'duration': 1.141}, {'end': 37887.867, 'text': 'So over here the formula is mpg tilde symbol displacement.', 'start': 37883.403, 'duration': 4.464}, {'end': 37895.453, 'text': 'So, whatever column we give on the left side of the tilde symbol, that is taken as the dependent variable,', 'start': 37888.287, 'duration': 7.166}], 'summary': 'Building a linear model using lm function with formula mpg ~ displacement.', 'duration': 24.498, 'max_score': 37870.955, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI37870955.jpg'}, {'end': 38018.389, 'src': 'embed', 'start': 37990.415, 'weight': 4, 'content': [{'end': 37997.317, 'text': 'So to get an estimate of average error during prediction, RMSE or root mean square error is used.', 'start': 37990.415, 'duration': 6.902}, {'end': 38002.843, 'text': "So again, let's go ahead and calculate the RMSE value for the model which we've just built.", 'start': 37998.021, 'duration': 4.822}, {'end': 38009.926, 'text': "So now that we have the actual values and the predicted values, let's bind both of them into a single data frame.", 'start': 38003.763, 'duration': 6.163}, {'end': 38018.389, 'text': 'So for that purpose, I will use the cbind function and our actual values are basically present in this mpg column from the test set.', 'start': 38010.466, 'duration': 7.923}], 'summary': 'Rmse is used to estimate average prediction error. calculating rmse for the model.', 'duration': 27.974, 'max_score': 37990.415, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI37990415.jpg'}, {'end': 38166.666, 'src': 'embed', 'start': 38140.082, 'weight': 12, 'content': [{'end': 38146.748, 'text': 'Now to get the average error which is present throughout all of the predictions, we would have to calculate the root mean square error.', 'start': 38140.082, 'duration': 6.666}, {'end': 38148.369, 'text': 'So the root mean square error.', 'start': 38147.288, 'duration': 1.081}, {'end': 38154.214, 'text': "as the name states, first we'd have to calculate the error, then we'd have to take the square of it,", 'start': 38148.369, 'duration': 5.845}, {'end': 38157.697, 'text': "then we'd have to take the mean of it and then finally we'll take the square root.", 'start': 38154.214, 'duration': 3.483}, {'end': 38161.6, 'text': 'So this is how we can calculate the root mean square error.', 'start': 38158.317, 'duration': 3.283}, {'end': 38166.666, 'text': "So final data dollar error and we'll square this error first.", 'start': 38162.061, 'duration': 4.605}], 'summary': 'Calculate root mean square error for prediction accuracy.', 'duration': 26.584, 'max_score': 38140.082, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI38140082.jpg'}, {'end': 38580.111, 'src': 'embed', 'start': 38551.281, 'weight': 9, 'content': [{'end': 38555.483, 'text': 'which basically comprises of the independent variable and the dependent variable.', 'start': 38551.281, 'duration': 4.202}, {'end': 38556.604, 'text': "i'll click on one.", 'start': 38555.483, 'duration': 1.121}, {'end': 38561.205, 'text': 'Now let me have a glance at the intercept and the m value of it.', 'start': 38557.404, 'duration': 3.801}, {'end': 38567.627, 'text': 'So linear regression is nothing but y equals mx plus c and that c is nothing but the intercept.', 'start': 38561.805, 'duration': 5.822}, {'end': 38573.689, 'text': 'So you see that the intercept is 34.12 and m which you see, this is the coefficient.', 'start': 38567.787, 'duration': 5.902}, {'end': 38580.111, 'text': 'this coefficient is nothing but the slope and you see that the coefficient is minus 0.91..', 'start': 38573.689, 'duration': 6.422}], 'summary': 'Linear regression: intercept 34.12, slope -0.91.', 'duration': 28.83, 'max_score': 38551.281, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI38551281.jpg'}, {'end': 38781.412, 'src': 'embed', 'start': 38756.762, 'weight': 0, 'content': [{'end': 38764.865, 'text': "So if the number of runs scored by Virat Kohli is around 60, then the probability of Team India winning the match would be let's say around 65% or so.", 'start': 38756.762, 'duration': 8.103}, {'end': 38767.106, 'text': "Again, let's take this value here.", 'start': 38765.586, 'duration': 1.52}, {'end': 38771.068, 'text': "So let's say this is around 97 runs or 95 runs.", 'start': 38767.546, 'duration': 3.522}, {'end': 38778.35, 'text': "And if Virat Kohli scores 95 or 97 runs, then the probability of Team India winning the match is 1,, which is 100%, isn't it?", 'start': 38771.468, 'duration': 6.882}, {'end': 38781.412, 'text': 'So similarly, this value here.', 'start': 38780.051, 'duration': 1.361}], 'summary': "If virat kohli scores around 95-97 runs, team india's win probability is 100%.", 'duration': 24.65, 'max_score': 38756.762, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI38756762.jpg'}], 'start': 36980.993, 'title': 'Exploring collaborative filtering and regression', 'summary': 'Explores implementing user-based collaborative filtering and data visualization, imputing missing values in the iris dataset, and covering the basics and model building of linear regression, achieving an rmse value, and demonstrating its implementation in python.', 'chapters': [{'end': 37291.801, 'start': 36980.993, 'title': 'Implementing user-based collaborative filtering model and data visualization with ggplot2', 'summary': 'Discusses the successful implementation of a user-based collaborative filtering model, recommending six books to two users, and demonstrates data manipulation using dplyr to extract 14,700 records where the diamond cut is ideal and the price is greater than 1000, as well as creating a scatter plot to visualize the relationship between price and carat in the diamonds dataset.', 'duration': 310.808, 'highlights': ['Successfully implemented user-based collaborative filtering model and recommended six books to two different users. The chapter discusses the successful implementation of a user-based collaborative filtering model and the recommendation of six books to two different users.', 'Demonstrated data manipulation using dplyr to extract 14,700 records where the diamond cut is ideal and the price is greater than 1000. Demonstrated data manipulation using dplyr to extract 14,700 records where the diamond cut is ideal and the price is greater than 1000 out of 53,940 total records.', 'Created a scatter plot to visualize the relationship between price and carat in the diamonds dataset using ggplot2. Created a scatter plot to visualize the relationship between price and carat in the diamonds dataset using ggplot2, showing the correlation between diamond price, carat value, and cut quality.']}, {'end': 37552.311, 'start': 37291.801, 'title': 'Imputing missing values in iris dataset', 'summary': 'Demonstrates introducing 25% missing values in the iris dataset, using miss forest package, and imputing the sepal length column with the mean and petal length column with the median using the hmisc package.', 'duration': 260.51, 'highlights': ['Introducing 25% missing values in iris dataset using miss forest package The presenter introduces 25% missing values in the iris dataset using the miss forest package, which will be totally random.', 'Imputing sepal length column with the mean The sepal length column is imputed with the mean using the HMISC package, resulting in the replacement of missing values with the mean of the column.', 'Imputing petal length column with the median The petal length column is imputed with the median using the HMISC package, resulting in the replacement of missing values with the median of the column.']}, {'end': 37870.955, 'start': 37552.731, 'title': 'Linear regression basics and implementation', 'summary': 'Introduces linear regression as a supervised learning algorithm to find the linear relationship between two variables, with an example of predicting monthly charges based on customer tenure. it further explains the implementation of simple linear regression in r using the empty cars dataset, emphasizing the importance of dividing the dataset into training and testing sets to avoid overfitting.', 'duration': 318.224, 'highlights': ['Linear regression is a supervised learning algorithm used to find the linear relationship between two variables, with an example of predicting monthly charges based on customer tenure. The chapter introduces the concept of linear regression, demonstrating its application in predicting monthly charges based on customer tenure for a telecom company called NEO.', "Importance of dividing the dataset into training and testing sets to avoid overfitting, with a split ratio of 65% for training and 35% for testing. Emphasizes the necessity of splitting the dataset into training and testing sets to prevent overfitting, using a split ratio of 65% for training and 35% for testing to ensure the model's generalizability.", 'Implementation of simple linear regression in R using the empty cars dataset, with mpg as the dependent variable and displacement as the independent variable. Explains the implementation of simple linear regression in R using the empty cars dataset, where mpg is the dependent variable and displacement is the independent variable.']}, {'end': 38370.556, 'start': 37870.955, 'title': 'Simple linear regression model building', 'summary': 'Covers building a simple linear regression model on a training set, predicting values on a test set, calculating the rmse value for the model, and implementing simple linear regression in python on the boston dataset.', 'duration': 499.601, 'highlights': ['The chapter covers building a simple linear regression model on a training set and predicting values on a test set. It explains using the lm function to build the model on the training set and using the predict function to predict values on the test set.', "Calculating the RMSE value for the model built and its significance in evaluating the model's performance. It demonstrates the calculation of the root mean square error (RMSE) to estimate the average error during prediction and emphasizes that a lower RMSE value indicates a better model.", "Implementing simple linear regression in Python on the Boston dataset with 'medv' as the dependent variable and 'lstat' as the independent variable. It describes the process of loading the Boston dataset, separating the independent and dependent variables, and visualizing the 'lstat' and 'medv' columns."]}, {'end': 38781.412, 'start': 38370.556, 'title': 'Implementing simple linear regression', 'summary': 'Demonstrates the implementation of simple linear regression in python to build a model with an inverse relationship between medv and lstat, achieving a mean absolute error of 4.69, mean squared error of 43, and root mean squared error of 6.62, with 80% of the records in the training set and 20% in the testing set.', 'duration': 410.856, 'highlights': ['Built a model with an inverse relationship between MEDV and LSTAT Demonstrated an inverse relationship between MEDV and LSTAT, where as LSTAT increases, MEDV decreases, indicating a negative coefficient value of -0.91.', "Achieved mean absolute error of 4.69, mean squared error of 43, and root mean squared error of 6.62 Obtained a mean absolute error of 4.69, mean squared error of 43, and root mean squared error of 6.62, indicating the model's performance and accuracy in prediction.", 'Implemented 80-20 data split for training and testing Utilized train test split function with a test size of 0.2, resulting in 80% of records in the training set and 20% in the testing set, with a random state for reproducibility.']}], 'duration': 1800.419, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI36980993.jpg', 'highlights': ['Successfully implemented user-based collaborative filtering model and recommended six books to two different users.', 'Demonstrated data manipulation using dplyr to extract 14,700 records where the diamond cut is ideal and the price is greater than 1000.', 'Created a scatter plot to visualize the relationship between price and carat in the diamonds dataset using ggplot2.', 'Introduced 25% missing values in iris dataset using miss forest package.', 'Imputed sepal length column with the mean using the HMISC package.', 'Imputed petal length column with the median using the HMISC package.', 'Introduced the concept of linear regression, demonstrating its application in predicting monthly charges based on customer tenure for a telecom company called NEO.', 'Emphasized the necessity of splitting the dataset into training and testing sets to prevent overfitting, using a split ratio of 65% for training and 35% for testing.', 'Explained the implementation of simple linear regression in R using the empty cars dataset, where mpg is the dependent variable and displacement is the independent variable.', 'Explained using the lm function to build the model on the training set and using the predict function to predict values on the test set.', 'Demonstrated the calculation of the root mean square error (RMSE) to estimate the average error during prediction and emphasized that a lower RMSE value indicates a better model.', "Described the process of loading the Boston dataset, separating the independent and dependent variables, and visualizing the 'lstat' and 'medv' columns.", 'Demonstrated an inverse relationship between MEDV and LSTAT, where as LSTAT increases, MEDV decreases, indicating a negative coefficient value of -0.91.', "Obtained a mean absolute error of 4.69, mean squared error of 43, and root mean squared error of 6.62, indicating the model's performance and accuracy in prediction.", 'Utilized train test split function with a test size of 0.2, resulting in 80% of records in the training set and 20% in the testing set, with a random state for reproducibility.']}, {'end': 39999.616, 'segs': [{'end': 39315.918, 'src': 'embed', 'start': 39288.093, 'weight': 2, 'content': [{'end': 39291.414, 'text': 'So I have my training and my testing sets ready.', 'start': 39288.093, 'duration': 3.321}, {'end': 39295.555, 'text': 'Now let me have a glance at the number of rows of the training and testing set.', 'start': 39292.014, 'duration': 3.541}, {'end': 39299.956, 'text': 'So n row of train there are 213 rows and n row of test there are 90 rows.', 'start': 39296.115, 'duration': 3.841}, {'end': 39312.216, 'text': 'So now that we have the training and testing sets ready, let me go ahead and build a model on top of the training set.', 'start': 39305.791, 'duration': 6.425}, {'end': 39315.918, 'text': "So again, we'll use this GLM function and build the model.", 'start': 39312.976, 'duration': 2.942}], 'summary': '213 rows in training set and 90 rows in testing set for model building using glm function', 'duration': 27.825, 'max_score': 39288.093, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI39288093.jpg'}, {'end': 39428.221, 'src': 'embed', 'start': 39399.779, 'weight': 0, 'content': [{'end': 39407.377, 'text': 'So what is a confusion matrix? So confusion matrix is actually a table which is used to estimate the performance of a model.', 'start': 39399.779, 'duration': 7.598}, {'end': 39413.158, 'text': 'It tabulates actual values and the predicted values in a two cross two matrix.', 'start': 39407.917, 'duration': 5.241}, {'end': 39416.939, 'text': 'So these are the actual values and these are the predicted values.', 'start': 39413.638, 'duration': 3.301}, {'end': 39419.599, 'text': 'So this what you see true positives.', 'start': 39417.399, 'duration': 2.2}, {'end': 39428.221, 'text': 'So this denotes all of those records where the actual values were true and the predicted values were also true.', 'start': 39420.019, 'duration': 8.202}], 'summary': 'A confusion matrix is a table used to estimate model performance, tabulating actual and predicted values.', 'duration': 28.442, 'max_score': 39399.779, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI39399779.jpg'}, {'end': 39723.937, 'src': 'embed', 'start': 39697.393, 'weight': 1, 'content': [{'end': 39701.917, 'text': 'So ROC curve, which actually stands for receiver operating characteristic,', 'start': 39697.393, 'duration': 4.524}, {'end': 39706.74, 'text': 'is basically a plot between the true positive rate and the false positive rate.', 'start': 39701.917, 'duration': 4.823}, {'end': 39717.088, 'text': 'And it helps us to find out the right trade off between the true positive rate and the false positive rate for different probability thresholds of the predicted values.', 'start': 39707.26, 'duration': 9.828}, {'end': 39723.937, 'text': 'so the closer the curve is to the upper left corner, the better the model is.', 'start': 39717.935, 'duration': 6.002}], 'summary': 'Roc curve plots tpr vs fpr to find model trade-off.', 'duration': 26.544, 'max_score': 39697.393, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI39697393.jpg'}], 'start': 38781.753, 'title': 'Logistic regression for heart data', 'summary': 'Discusses implementing logistic regression on the heart data set, demonstrating model summary, prediction analysis, model for heart disease, confusion matrix evaluation, and customer churn prediction, achieving a probability of 81.2% for the person not having heart disease at the age of 30 and an accuracy of 53% for model evaluation.', 'chapters': [{'end': 38991.616, 'start': 38781.753, 'title': 'Implementing logistic regression on heart data', 'summary': "Discusses implementing logistic regression on the heart data set, where the dependent variable is the 'target' column and the independent variable is the 'age' column, and demonstrates the process of renaming columns, converting data types, and building a logistic regression model without splitting the dataset into training and testing sets.", 'duration': 209.863, 'highlights': ["The chapter discusses implementing logistic regression on the heart data set, where the dependent variable is the 'target' column and the independent variable is the 'age' column. The process involves renaming columns, converting data types, and building a logistic regression model without splitting the dataset into training and testing sets.", 'The Y value in logistic regression lies within 0 and 1 range, representing the probability of Team India winning the match. The probability of Team India winning the match is 0 when Virat Kohli scores 5 or 10 runs, demonstrating the Y value within the 0-1 range in logistic regression.', 'The target column is converted into a categorical value with two levels: 0 and 1. The integer target column is successfully converted into a categorical value with levels 0 and 1, preparing it for logistic regression modeling.']}, {'end': 39212.898, 'start': 38992.117, 'title': 'Model summary & prediction analysis', 'summary': 'Covers the summary and analysis of a logistic regression model, showing a strong relationship between age and the target column, with a probability of 81.2% for the person not having heart disease at the age of 30 and 26% at the age of 77.', 'duration': 220.781, 'highlights': ['The null hypothesis stating no relationship between age and the target column is rejected due to three stars associated with the p value, indicating a strong relationship. three stars associated with the p value', 'The residual deviance drops from 417 to 401 when the age column is included, indicating a strong relationship between age and the target column. residual deviance drops from 417 to 401', 'Probability of the person not having heart disease is 81.2% at the age of 30 and 26% at the age of 77, showing a noticeable decrease as age increases. probability of the person not having heart disease: 81.2% at age 30, 26% at age 77']}, {'end': 39399.218, 'start': 39213.198, 'title': 'Logistic regression model for heart disease', 'summary': 'Details the process of dividing a data set into training and testing sets, building a logistic regression model to predict the probability of heart disease based on age, achieving a range of predicted values from 21% to 86% and obtaining training and testing set sizes of 213 and 90 rows respectively.', 'duration': 186.02, 'highlights': ['Divided data set into training and test sets with a split ratio of 70% for true label and 30% for false label The data set was divided into training and test sets with a split ratio of 70% for true label and 30% for false label, resulting in the training set containing 213 rows and the testing set containing 90 rows.', "Built a logistic regression model with the formula 'target ~ age' and family as binomial A logistic regression model was built using the formula 'target ~ age' and the family specified as binomial to determine the probability of heart disease based on age.", "Obtained a range of predicted values for heart disease probability from 21% to 86% The range of predicted values for heart disease probability varied from 21% to 86%, indicating the model's ability to predict a wide range of probabilities."]}, {'end': 39999.616, 'start': 39399.779, 'title': 'Understanding confusion matrix and model evaluation', 'summary': "Explains the construction of a confusion matrix to evaluate the performance of a model, resulting in an accuracy of 53%, followed by the explanation of true positive rate, false positive rate, and roc curve to assess the model's trade-off between true positive rate and false positive rate, and determining the ideal threshold value. additionally, it discusses building a logistic regression model in python to predict customer churn based on monthly charges and finding the log loss of the model.", 'duration': 599.837, 'highlights': ['Building a confusion matrix for the model resulted in an accuracy of 53% with a probability threshold of 0.6 for predicted values. The accuracy of the model was calculated as 53% using the true positives and true negatives divided by all values, with a probability threshold of 0.6 for predicted values.', 'Explaining the true positive rate and false positive rate, where the true positive rate measures the percentage of actual positives correctly identified and the false positive rate calculates the probability of falsely rejecting the null hypothesis for a particular test. The true positive rate measures the percentage of actual positives correctly identified, while the false positive rate calculates the probability of falsely rejecting the null hypothesis for a particular test.', 'Discussing the ROC curve as a plot between the true positive rate and false positive rate to find the right trade-off, with the closer the curve to the upper left corner indicating a better model, and the greater area under the curve denoting a better model. The ROC curve is a plot between the true positive rate and false positive rate to find the right trade-off, where a curve closer to the upper left corner and covering a greater area under it signifies a better model.', 'Building a logistic regression model in Python to predict customer churn based on monthly charges and finding the log loss of the model. A logistic regression model was built in Python to predict customer churn based on monthly charges, and the log loss of the model was determined.']}], 'duration': 1217.863, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI38781753.jpg', 'highlights': ['The target column is converted into a categorical value with two levels: 0 and 1, preparing it for logistic regression modeling.', 'The residual deviance drops from 417 to 401 when the age column is included, indicating a strong relationship between age and the target column.', 'Divided data set into training and test sets with a split ratio of 70% for true label and 30% for false label, resulting in the training set containing 213 rows and the testing set containing 90 rows.', 'Building a confusion matrix for the model resulted in an accuracy of 53% with a probability threshold of 0.6 for predicted values.']}, {'end': 41350.006, 'segs': [{'end': 40271.161, 'src': 'embed', 'start': 40241.613, 'weight': 2, 'content': [{'end': 40244.215, 'text': "So let's say if the age of the patient is greater than 50.", 'start': 40241.613, 'duration': 2.602}, {'end': 40246.697, 'text': "If the condition is true, we'll come here.", 'start': 40244.215, 'duration': 2.482}, {'end': 40248.859, 'text': "If the condition is false, then we'll come here.", 'start': 40246.837, 'duration': 2.022}, {'end': 40253.143, 'text': "After that, over here, we'll check if the person smokes or not.", 'start': 40249.5, 'duration': 3.643}, {'end': 40255.524, 'text': 'And if that person smokes, will come here.', 'start': 40253.763, 'duration': 1.761}, {'end': 40257.665, 'text': "If that person doesn't smoke, will come here.", 'start': 40255.684, 'duration': 1.981}, {'end': 40262.028, 'text': 'Similarly, over here, the test condition could be whether the patient has any children or not.', 'start': 40258.106, 'duration': 3.922}, {'end': 40264.199, 'text': "If the condition is true, we'll come here.", 'start': 40262.438, 'duration': 1.761}, {'end': 40265.999, 'text': "If the condition is false, we'll come here.", 'start': 40264.319, 'duration': 1.68}, {'end': 40268.14, 'text': 'So this is how the decision tree works.', 'start': 40266.399, 'duration': 1.741}, {'end': 40271.161, 'text': "And finally, we'll have class labels over here.", 'start': 40268.52, 'duration': 2.641}], 'summary': 'Decision tree categorizes patients based on age, smoking, and children presence.', 'duration': 29.548, 'max_score': 40241.613, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI40241613.jpg'}, {'end': 41207.437, 'src': 'embed', 'start': 41162.652, 'weight': 0, 'content': [{'end': 41170.316, 'text': "So the model which we've built, the name of that model was RF and we are supposed to predict the values for the test set.", 'start': 41162.652, 'duration': 7.664}, {'end': 41174.919, 'text': "So I'll give in test over here and I will store the result in the P1 object.", 'start': 41170.676, 'duration': 4.243}, {'end': 41177.821, 'text': 'Now let me build a confusion matrix for this.', 'start': 41175.539, 'duration': 2.282}, {'end': 41183.567, 'text': 'So table and this table function will take in the actual values in the predicted values.', 'start': 41178.362, 'duration': 5.205}, {'end': 41191.935, 'text': 'So the actual values are stored in the NSP column of the test set and the predicted values are stored in the p1 object.', 'start': 41184.087, 'duration': 7.848}, {'end': 41193.777, 'text': 'So this is what we have.', 'start': 41192.475, 'duration': 1.302}, {'end': 41198.824, 'text': 'So this left diagonal represents all of those values which have been correctly classified.', 'start': 41194.258, 'duration': 4.566}, {'end': 41201.708, 'text': 'So this 567, which you see.', 'start': 41199.325, 'duration': 2.383}, {'end': 41207.437, 'text': 'so this represents all of those records where the patient actually did not have cancer,', 'start': 41201.708, 'duration': 5.729}], 'summary': 'Using model rf, predicted test values stored in p1, and built a confusion matrix for evaluation.', 'duration': 44.785, 'max_score': 41162.652, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI41162652.jpg'}, {'end': 41342.663, 'src': 'embed', 'start': 41309.99, 'weight': 3, 'content': [{'end': 41311.871, 'text': 'just a quick info, guys.', 'start': 41309.99, 'duration': 1.881}, {'end': 41320.542, 'text': 'intellipaat provides data science architect master program in partnership with IBM and mentored by industry experts.', 'start': 41311.871, 'duration': 8.671}, {'end': 41324.307, 'text': 'The course link of which is given in the description below.', 'start': 41321.464, 'duration': 2.843}, {'end': 41338.962, 'text': 'guys when you do that it really motivates us to bring more such awesome content for you.', 'start': 41334.54, 'duration': 4.422}, {'end': 41342.663, 'text': 'and if you want us to create videos on any other content as well,', 'start': 41338.962, 'duration': 3.701}], 'summary': 'Intellipaat offers data science architect master program in partnership with ibm. check the course link in the description.', 'duration': 32.673, 'max_score': 41309.99, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI41309990.jpg'}], 'start': 40000.197, 'title': 'Building predictive models', 'summary': "Covers building logistic regression model with a log loss value of 0.55, decision tree model's structure and working mechanism, random forest model as an ensemble model, and random forest algorithm overview involving creation of 1 million rows from 1000 rows of data. it also includes building a random forest model on the ctg dataset with an accuracy of 96%.", 'chapters': [{'end': 40175.991, 'start': 40000.197, 'title': 'Building logistic regression model', 'summary': 'Covers separating dependent and independent variables, dividing model into training and testing sets, using train test split function to allocate 70% for training and 30% for testing, building a logistic regression model, predicting probabilities, and obtaining a log loss value of 0.55.', 'duration': 175.794, 'highlights': ['Allocating 70% of the records for training and 30% for testing Using the train test split function with a test size of 0.3 to divide the model into training and testing sets, resulting in 70% of the records being in the training set and the rest 30% in the test set.', "Obtaining a log loss value of 0.55 Using the log loss function with actual values and predicted values to obtain a log loss value of 0.55, indicating the model's performance.", 'Separating dependent and independent variables Separating the monthly charges column as the independent variable (X) and the churn column as the dependent variable (Y) into two separate objects.', 'Building a logistic regression model Importing the logistic regression function, creating an instance of logistic regression, fitting the model on the training set, and predicting the probabilities on the test set to build a logistic regression model.']}, {'end': 40682.949, 'start': 40176.411, 'title': 'Decision tree model and random forest model', 'summary': 'Covers the explanation of decision tree model, including its structure and working mechanism for building a model, followed by the accuracy assessment. it also explains the working mechanism of the random forest model as an ensemble model combining multiple decision trees to produce the final output.', 'duration': 506.538, 'highlights': ['The chapter covers the explanation of decision tree model, including its structure and working mechanism for building a model, followed by the accuracy assessment. It explains the structure of decision tree, the process of building the model on the iris dataset, and the accuracy assessment achieving 96% accuracy.', 'It also explains the working mechanism of the random forest model as an ensemble model combining multiple decision trees to produce the final output. The working mechanism of random forest model as an ensemble model that combines multiple decision trees to produce the final output is explained.']}, {'end': 40906.829, 'start': 40683.49, 'title': 'Random forest algorithm overview', 'summary': 'Explains the process of creating multiple data sets from a single dataset using sampling with replacement, and then using these data sets to build x decision trees in a random forest, resulting in 1 million rows from just 1000 rows of data.', 'duration': 223.339, 'highlights': ['The process of creating multiple data sets from a single dataset using sampling with replacement, and then using these data sets to build X decision trees in a random forest, resulting in 1 million rows from just 1000 rows of data.', "Explaining the mechanism of providing a random subset of columns to the random forest algorithm for splitting nodes in decision trees, ensuring the diversity of the X trees and the final prediction based on the majority vote of individual trees' predictions.", "The random forest algorithm creates X decision trees using a random subset of columns for splitting nodes, ensuring the diversity of the trees, and makes predictions based on the majority vote of individual trees' predictions.", "The random forest algorithm's process involves creating multiple data sets from a single dataset, using sampling with replacement, and then using these data sets to build X decision trees, resulting in 1 million rows from just 1000 rows of data."]}, {'end': 41350.006, 'start': 40907.269, 'title': 'Building random forest model on ctg dataset', 'summary': 'Involves building a random forest model on the ctg dataset, with an accuracy of 96%, by initially loading and examining the dataset, converting the dependent variable, dividing the dataset into training and testing sets, and finally building and evaluating the random forest model.', 'duration': 442.737, 'highlights': ['The model achieved an accuracy of 96%, showcasing its effectiveness in classifying patients as not having cancer, suspected to have cancer, or having cancer. The accuracy of the random forest model was calculated to be 96%, indicating its high effectiveness in classifying patients.', 'The dataset was divided into a training set of 1383 records and a testing set of 743 records, enabling the model to be trained and evaluated effectively. The dataset was divided into a training set consisting of 1383 records and a testing set consisting of 743 records, facilitating effective model training and evaluation.', 'The dependent variable NSP was converted from an integer to a factor with three levels, representing the absence of cancer, suspicion of cancer, and confirmation of cancer. The NSP column, representing the presence of cancer, was converted from an integer to a factor with three levels: absence of cancer, suspicion of cancer, and confirmation of cancer.', 'The random forest model was built on top of the training set, using the NSP column as the dependent variable and all other columns as the independent variables to classify patients. The random forest model was built on the training set, with the NSP column as the dependent variable and the remaining columns as independent variables for patient classification.', "The CTG dataset was loaded and examined, providing an overview of the data's structure and the distribution of NSP values among the patients. The CTG dataset was loaded and examined, offering insights into the data's structure and the distribution of NSP values among the patients."]}], 'duration': 1349.809, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MFv2v86C8VI/pics/MFv2v86C8VI40000197.jpg', 'highlights': ['Building a logistic regression model with a log loss value of 0.55', 'Explaining the working mechanism of the random forest model as an ensemble model', 'Creating 1 million rows from 1000 rows of data using the random forest algorithm', 'Building a random forest model on the ctg dataset with an accuracy of 96%']}], 'highlights': ['Around 60-70% of data science job descriptions ask for Python skills, indicating its importance for aspiring data scientists.', 'The exponential growth of data has changed the way business decisions are made, with an increasing necessity to have data to back business decisions.', "Python's open-source nature has led to the development of a rich ecosystem of libraries and packages by the community, contributing to its widespread adoption.", 'Data cleaning and pre-processing are essential due to 1460 values for each feature, removing columns with less than 30% non-null values.', 'The logistic regression model achieved an accuracy of 77.50%, with 935 true positives and 157 true negatives out of the total 935+157+106+211 instances.', 'The most important part in the entire data science lifecycle is the data processing part, involving data manipulation, visualization, and implementing machine learning algorithms.', 'The dataset comprises 980,000 ratings for 10,000 books from 53,424 users, providing a substantial amount of user feedback.', 'Successfully implemented user-based collaborative filtering model and recommended six books to two different users.', 'Building a logistic regression model with a log loss value of 0.55', 'Creating 1 million rows from 1000 rows of data using the random forest algorithm']}