title

Machine Learning With Python Full Course 2023 | Machine Learning Tutorial for Beginners| Simplilearn

description

🔥 Purdue Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=MachineLearningFC01Mar22&utm_medium=DescriptionFirstFold&utm_source=youtube
🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&utm_medium=DescriptionFF&utm_source=youtube
🔥AI Engineer Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=SCE-AIMasters&utm_medium=DescriptionFF&utm_source=youtube
🔥AI & Machine Learning Bootcamp(US Only): https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=MachineLearningFC01Mar22&utm_medium=DescriptionFirstFold&utm_source=youtube
In this video on Machine Learning with Python full course, you will understand the basics of machine learning and Python. In this Machine Learning tutorial for beginners, we will cover essential machine learning topics like applications of machine learning and machine learning concepts and understand why mathematics, statistics, and linear algebra are crucial. We'll also learn about regularization, dimensionality reduction, and PCA. We will perform a prediction analysis on the recently held US Elections. Finally, you will study the Machine Learning roadmap. Below are the topics covered in this video:
00:00:00 Machine Learning With Python Full Course 2023
00:08:36 Introduction to Machine Learning
00:16:14 Top 10 Applications of Machine Learning
00:32:38 Types of Machine Learning
00:37:46 Machine Learning Algorithms
00:38:14 Linear Regression
00:46:52 Decision Tree
01:23:25 Clustering
01:26:11 K-Means Clustering
02:18:03 Data and its types
03:29:22 Probability
04:07:53 Multiple Linear Regression
04:45:55 Confusion Matrices
05:59:54 KNN
06:23:40 Support Vector Machine
07:14:40 Principle Component Analysis(PCA)
07:53:01 Corona Virus Analysis
🔥Free Machine Learning Course With Completion Certificate:
https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearningFC01Mar22&utm_medium=Description&utm_source=youtube
✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH
⏩ Check out the Machine Learning tutorial videos: https://bit.ly/3fFR4f4
#MachineLearningCourse #MachineLearningFullCourse #MachineLearningWithPython #MachineLearningWithPythonFullCourse #MachineLearningTutorial #MachineLearningTutorialForBeginners #MachineLearning #MachineLearningTraining #Simplilearn
Dataset Link -https://drive.google.com/drive/folders/15lSrc4176J9z9_3WZo_b91BaNfItc2s0
➡️ About Caltech Post Graduate Program In AI And Machine Learning
Designed to boost your career as an AI and ML professional, this program showcases Caltech CTME's excellence and IBM's industry prowess. The artificial intelligence course covers key concepts like Statistics, Data Science with Python, Machine Learning, Deep Learning, NLP, and Reinforcement Learning through an interactive learning model with live sessions.
✅ Key Features
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- PGP AI & ML completion certificate from Caltech CTME
- Masterclasses delivered by distinguished Caltech faculty and IBM experts
- Caltech CTME Circle Membership
- Earn up to 22 CEUs from Caltech CTME
- Online convocation by Caltech CTME Program Director
- IBM certificates for IBM courses
- Access to hackathons and Ask Me Anything sessions from IBM
- 25+ hands-on projects from the likes of Twitter, Mercedes Benz, Uber, and many more
- Seamless access to integrated labs
- Capstone projects in 3 domains
- 8X higher interaction in live online classes by industry experts
✅ Skills Covered
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- Recommendation Systems
- NLP
- Neural Networks
- GANs
- Deep Learning
- Reinforcement Learning
- Speech Recognition
- Ensemble Learning
- Computer Vision
👉 Learn More At: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=MachineLearningFC&utm_medium=Description&utm_source=youtube
Get the Simplilearn app: https://simpli.app.link/OlbFAhqMqgb
🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

detail

{'title': 'Machine Learning With Python Full Course 2023 | Machine Learning Tutorial for Beginners| Simplilearn', 'heatmap': [{'end': 4311.893, 'start': 2153.439, 'weight': 0.819}, {'end': 35888, 'start': 35529.12, 'weight': 0.843}], 'summary': 'Tutorial covers the machine learning roadmap for 2022, projected market growth to reach $47.29 million by 2027, key skills for a career in machine learning, and basics of supervised, unsupervised, and reinforcement learning. it also delves into applications, linear regression, support vector machine, anaconda in jupyter notebook, classifiers, k-means clustering, data analysis, model evaluation, linear algebra, calculus, statistics, hypothesis testing, python set operations, model accuracy, machine learning models, visualizations, model performance evaluation, data preprocessing, k-nearest neighbors algorithm, diabetes prediction, support vector machine, dimensionality reduction, and covid-19 impact analysis.', 'chapters': [{'end': 1062.167, 'segs': [{'end': 146.452, 'src': 'embed', 'start': 114.247, 'weight': 4, 'content': [{'end': 125.054, 'text': 'As per marketsandmarkets.com, the machine learning market is expected to grow to $9 billion by 2022 at a CAGR of 44%.', 'start': 114.247, 'duration': 10.807}, {'end': 139.726, 'text': 'Another report from verifiedmarketresearch.com suggests that the machine learning market was valued at $2.4 billion in 2019 and is projected to reach $47.29 million by 2027,', 'start': 125.054, 'duration': 14.672}, {'end': 146.452, 'text': 'growing at a CAGR of 44.9% from 2020 to 2027..', 'start': 139.726, 'duration': 6.726}], 'summary': 'Machine learning market to reach $47.29m by 2027, growing at 44.9% cagr.', 'duration': 32.205, 'max_score': 114.247, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE114247.jpg'}, {'end': 227.248, 'src': 'embed', 'start': 198.141, 'weight': 2, 'content': [{'end': 201.023, 'text': 'The second skill you need to possess is applied mathematics.', 'start': 198.141, 'duration': 2.882}, {'end': 207.269, 'text': 'While solving business problems using machine learning, you have to use machine learning algorithms.', 'start': 201.824, 'duration': 5.445}, {'end': 217.7, 'text': 'To understand the mechanisms behind the algorithms, you need to have a good knowledge of mathematical concepts such as linear algebra, calculus,', 'start': 207.95, 'duration': 9.75}, {'end': 219.561, 'text': 'statistics and probability.', 'start': 217.7, 'duration': 1.861}, {'end': 227.248, 'text': 'So mathematics in machine learning is not just processing the numbers, but understanding what is happening,', 'start': 220.462, 'duration': 6.786}], 'summary': 'Applied mathematics is crucial in understanding machine learning algorithms for solving business problems.', 'duration': 29.107, 'max_score': 198.141, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE198141.jpg'}, {'end': 756.76, 'src': 'embed', 'start': 731.403, 'weight': 0, 'content': [{'end': 736.928, 'text': 'Here the machine knew the features of the object and also the labels associated with those features.', 'start': 731.403, 'duration': 5.525}, {'end': 740.431, 'text': "On this note, let's move to unsupervised learning and see the difference.", 'start': 737.208, 'duration': 3.223}, {'end': 745.814, 'text': 'Suppose you have cricket data set of various players with their respective scores and wickets taken.', 'start': 740.791, 'duration': 5.023}, {'end': 751.717, 'text': 'When we feed this data set to the machine, the machine identifies the pattern of player performance.', 'start': 745.974, 'duration': 5.743}, {'end': 756.76, 'text': 'So it plots this data with their respective wickets on the x-axis while runs on the y-axis.', 'start': 752.057, 'duration': 4.703}], 'summary': 'Machine learns features and labels, then plots cricket player performance data.', 'duration': 25.357, 'max_score': 731.403, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE731403.jpg'}], 'start': 21.536, 'title': 'Machine learning in 2022', 'summary': 'Explores the machine learning roadmap for 2022, its projected market growth to reach $47.29 million by 2027 with a cagr of 44.9%. it outlines critical skills for a career in machine learning, high demand, and salaries, and introduces the basics of machine learning, including supervised, unsupervised, and reinforcement learning, and its everyday applications.', 'chapters': [{'end': 146.452, 'start': 21.536, 'title': 'Machine learning roadmap 2022', 'summary': 'Explores the machine learning roadmap for 2022, including its applications in various sectors and its market growth, which is projected to reach $47.29 million by 2027 with a cagr of 44.9%.', 'duration': 124.916, 'highlights': ['The machine learning market is projected to reach $47.29 million by 2027, growing at a CAGR of 44.9% from 2020 to 2027. The projected market growth of machine learning, reaching $47.29 million by 2027 with a CAGR of 44.9%, demonstrates its substantial potential for expansion.', 'The machine learning market is expected to grow to $9 billion by 2022 at a CAGR of 44%. The expected growth of the machine learning market to $9 billion by 2022 at a CAGR of 44% reflects the increasing demand for machine learning technologies.', 'Machine learning has found its usage in almost every business sector, including self-driving cars, medical imaging, speech recognition, facial recognition, and online fraud detection. The wide-ranging applications of machine learning across various sectors, such as self-driving cars, medical imaging, speech recognition, facial recognition, and online fraud detection, highlight its versatility and impact.']}, {'end': 511.721, 'start': 146.452, 'title': 'Machine learning career guide', 'summary': 'Outlines the critical skills required for a career in machine learning, including programming skills, applied mathematics, data wrangling and sql, machine learning algorithms, and data modeling and evaluation. additionally, it highlights the high demand and lucrative salaries for machine learning engineers, with an average salary of $131,000 per year in the united states and nearly 8 lakh rupees per annum in india, and mentions top companies hiring for machine learning roles, such as google, amazon, ibm, uber, graminar, nvidia, and linkedin.', 'duration': 365.269, 'highlights': ['Machine learning engineers in the United States earn an average salary of $131,000 per year according to Glassdoor.com. The average salary of machine learning engineers in the United States is $131,000 per year, making it a high-paying profession.', 'In India, machine learning engineers can earn nearly 8 lakh rupees per annum. In India, machine learning engineers can earn nearly 8 lakh rupees per annum, providing insight into the potential earnings in the field.', 'Top companies hiring for machine learning roles include Google, Amazon, IBM, Uber, Graminar, NVIDIA, and LinkedIn. Top companies hiring for machine learning roles include Google, Amazon, IBM, Uber, Graminar, NVIDIA, and LinkedIn, providing valuable information on potential employers in the field.', 'The critical skills required for a career in machine learning include programming skills, applied mathematics, data wrangling and SQL, machine learning algorithms, and data modeling and evaluation. The critical skills required for a career in machine learning encompass programming skills, applied mathematics, data wrangling and SQL, machine learning algorithms, and data modeling and evaluation, providing a comprehensive overview of the necessary expertise in the field.']}, {'end': 774.368, 'start': 512.461, 'title': 'Basics of machine learning', 'summary': 'Introduces the basics of machine learning, explaining its concept with a simple example, demonstrating k-nearest neighbors algorithm, and providing insights into supervised and unsupervised learning, emphasizing the importance of labeled data and pattern recognition.', 'duration': 261.907, 'highlights': ['Machine learning involves training machines to learn from past data and make decisions, making it faster and more efficient than human decision-making. Machine learning enables machines to learn from past data and make decisions, making it faster and more efficient than human decision-making.', "Demonstration of using Paul's song preferences to illustrate the concept of machine learning, showcasing how past choices can classify unknown songs easily. The example of using Paul's song preferences illustrates how machine learning can use past choices to classify unknown songs easily.", 'Introduction to k-nearest neighbors algorithm as a basic machine learning algorithm, which simplifies the process of classifying data points based on their proximity to known data points. Introduction to k-nearest neighbors algorithm as a basic machine learning algorithm, simplifying the process of classifying data points based on their proximity to known data points.', 'Explanation of supervised learning using an example of predicting currency based on the weight of coins, emphasizing the use of labeled data to train the model. Explanation of supervised learning using an example of predicting currency based on the weight of coins, emphasizing the use of labeled data to train the model.', 'Insights into unsupervised learning through the example of identifying player performance patterns in cricket data, showcasing pattern recognition and clustering. Insights into unsupervised learning through the example of identifying player performance patterns in cricket data, showcasing pattern recognition and clustering.']}, {'end': 1062.167, 'start': 774.508, 'title': 'Machine learning basics', 'summary': 'Covers the basics of machine learning, including supervised, unsupervised, and reinforcement learning, the role of data in machine learning, and everyday applications of machine learning. it also discusses the top 10 applications of machine learning, such as virtual personal assistants and traffic predictions.', 'duration': 287.659, 'highlights': ['Machine learning has various applications including healthcare diagnostics, sentiment analysis on social media, fraud detection in finance, and predictive modeling for surge pricing in the transportation sector. The applications of machine learning in healthcare, social media, finance, and transportation are highlighted, showcasing its wide range of uses.', 'The chapter explains the working of virtual personal assistants like Google Assistant, Alexa, and Siri, using machine learning and neural networks for tasks such as scheduling appointments and playing music. The functioning of virtual personal assistants and their reliance on machine learning and neural networks for tasks is detailed.', 'The importance of data and computational capabilities of computers in enabling machine learning is emphasized, with the availability of humongous data and increased memory handling capacities. The crucial role of data availability and computer capabilities in facilitating machine learning is highlighted.', 'The chapter introduces the concepts of supervised, unsupervised, and reinforcement learning, providing examples to illustrate each type of learning. The distinction between supervised, unsupervised, and reinforcement learning is explained, along with practical examples for each type of learning.', 'The use of machine learning for traffic predictions is discussed, detailing the process of using Google Maps and interpreting the colored regions to signify traffic conditions. The process of using machine learning for traffic predictions, illustrated using Google Maps and color-coded regions, is explained.']}], 'duration': 1040.631, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE21536.jpg', 'highlights': ['The machine learning market is projected to reach $47.29 million by 2027, growing at a CAGR of 44.9% from 2020 to 2027.', 'Machine learning engineers in the United States earn an average salary of $131,000 per year according to Glassdoor.com.', 'Machine learning involves training machines to learn from past data and make decisions, making it faster and more efficient than human decision-making.', 'Machine learning has various applications including healthcare diagnostics, sentiment analysis on social media, fraud detection in finance, and predictive modeling for surge pricing in the transportation sector.', 'The chapter introduces the concepts of supervised, unsupervised, and reinforcement learning, providing examples to illustrate each type of learning.']}, {'end': 2165.884, 'segs': [{'end': 1089.891, 'src': 'embed', 'start': 1062.527, 'weight': 3, 'content': [{'end': 1066.65, 'text': 'Yellow indicate that they are slightly congested and red means they are heavily congested.', 'start': 1062.527, 'duration': 4.123}, {'end': 1072.755, 'text': "So let's look at the map, a different version of the same map, and here, as I told you before, red means heavily congested,", 'start': 1067.01, 'duration': 5.745}, {'end': 1074.916, 'text': 'yellow means slow moving and blue means clear.', 'start': 1072.755, 'duration': 2.161}, {'end': 1081.202, 'text': 'So how exactly is Google able to tell you that the traffic is clear, slow moving or heavily congested?', 'start': 1075.837, 'duration': 5.365}, {'end': 1085.006, 'text': 'So this is with the help of machine learning and with the help of two important measures.', 'start': 1081.423, 'duration': 3.583}, {'end': 1089.891, 'text': "First is the average time that's taken on specific days at specific times on that route.", 'start': 1085.326, 'duration': 4.565}], 'summary': 'Google uses machine learning and average time measures to assess traffic congestion.', 'duration': 27.364, 'max_score': 1062.527, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE1062527.jpg'}, {'end': 1510.683, 'src': 'embed', 'start': 1482.247, 'weight': 4, 'content': [{'end': 1487.71, 'text': "It's not like walking through an airport and In a lot of countries, you have, like, hundreds of people trying to sell you timeshare.", 'start': 1482.247, 'duration': 5.463}, {'end': 1488.65, 'text': 'Come join us.', 'start': 1488.07, 'duration': 0.58}, {'end': 1489.371, 'text': 'Sign up for this.', 'start': 1488.67, 'duration': 0.701}, {'end': 1493.913, 'text': 'It eliminates that annoyingness, so now you can just enjoy your Facebook and your cat pictures.', 'start': 1489.391, 'duration': 4.522}, {'end': 1495.454, 'text': "Or maybe it's your family pictures.", 'start': 1494.213, 'duration': 1.241}, {'end': 1496.575, 'text': 'Mine is family.', 'start': 1495.874, 'duration': 0.701}, {'end': 1498.616, 'text': 'Certainly people like their cat pictures, too.', 'start': 1496.995, 'duration': 1.621}, {'end': 1502.858, 'text': "Another good example is Google's DeepMind project AlphaGo.", 'start': 1499.116, 'duration': 3.742}, {'end': 1510.683, 'text': "A computer program that plays a board game Go has defeated the world's number one Go player, and I hope I say his name right, Kiji.", 'start': 1502.878, 'duration': 7.805}], 'summary': "Online ads reduce annoyance; google's alphago beats world's top go player.", 'duration': 28.436, 'max_score': 1482.247, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE1482247.jpg'}, {'end': 1837.434, 'src': 'embed', 'start': 1810.296, 'weight': 7, 'content': [{'end': 1814.279, 'text': 'For instance, finding groups of customers with similar behavior,', 'start': 1810.296, 'duration': 3.983}, {'end': 1819.523, 'text': 'given a large database of customer data containing their demographics and past buying records.', 'start': 1814.279, 'duration': 5.244}, {'end': 1827.508, 'text': "And in this case, we might notice that anybody who's wearing a certain set of shoes goes shopping at certain stores or whatever it is.", 'start': 1820.063, 'duration': 7.445}, {'end': 1828.789, 'text': "They're going to make certain purchases.", 'start': 1827.528, 'duration': 1.261}, {'end': 1833.131, 'text': 'By having that information, it helps us to market or group people together,', 'start': 1829.229, 'duration': 3.902}, {'end': 1837.434, 'text': 'so that we can now explore that group and find out what it is we want to market to them.', 'start': 1833.131, 'duration': 4.303}], 'summary': 'Analyze customer data to group behavior for targeted marketing.', 'duration': 27.138, 'max_score': 1810.296, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE1810296.jpg'}, {'end': 1951.351, 'src': 'embed', 'start': 1927.875, 'weight': 11, 'content': [{'end': 1936.659, 'text': 'What are they writing out in their handwriting? C, behavior of a website indicating that the site is not working as designed.', 'start': 1927.875, 'duration': 8.784}, {'end': 1943.004, 'text': 'D, predicting salary of an individual based on his or her years of experience.', 'start': 1937.239, 'duration': 5.765}, {'end': 1945.786, 'text': 'HR hiring set up there.', 'start': 1943.885, 'duration': 1.901}, {'end': 1947.748, 'text': 'So stay tuned for part two.', 'start': 1946.146, 'duration': 1.602}, {'end': 1951.351, 'text': "We'll go ahead and answer these questions when we get to the part two of this tutorial.", 'start': 1947.908, 'duration': 3.443}], 'summary': 'Discussion of website behavior and salary prediction in tutorial part two.', 'duration': 23.476, 'max_score': 1927.875, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE1927875.jpg'}, {'end': 2052.092, 'src': 'embed', 'start': 2028.737, 'weight': 0, 'content': [{'end': 2035.499, 'text': 'And if you are looking to make a fortune in the stock market, keep in mind it is very difficult to get all the data correct on the stock market.', 'start': 2028.737, 'duration': 6.762}, {'end': 2039.542, 'text': 'It fluctuates in ways really hard to predict.', 'start': 2036.419, 'duration': 3.123}, {'end': 2041.744, 'text': "So it's quite a roller coaster ride.", 'start': 2039.962, 'duration': 1.782}, {'end': 2046.607, 'text': "If you're running machine learning on the stock market, you start realizing you really have to dig for new data.", 'start': 2041.884, 'duration': 4.723}, {'end': 2048.349, 'text': 'So we have supervised learning.', 'start': 2046.948, 'duration': 1.401}, {'end': 2052.092, 'text': 'And if you have supervised, we need unsupervised learning.', 'start': 2048.768, 'duration': 3.324}], 'summary': 'Stock market data is difficult to predict, requiring new data for machine learning models.', 'duration': 23.355, 'max_score': 2028.737, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE2028737.jpg'}], 'start': 1062.527, 'title': 'Machine learning applications and types', 'summary': "Covers the applications of machine learning in traffic prediction, social media, e-commerce, email spam filtering, fraud detection, stock market trading, medical technology, automatic translation, transportation, healthcare, and social media, and also discusses the overview of machine learning, highlighting the significance of google's deepmind project alphago, the basic principles of machine learning, and the general process of implementing machine learning, along with the types of machine learning, including supervised, unsupervised, and reinforcement learning, and their real-world applications in classifying, predicting, finding hidden patterns, and learning from actions in an environment.", 'chapters': [{'end': 1498.616, 'start': 1062.527, 'title': 'Applications of machine learning', 'summary': 'Explains how machine learning is used in various applications, including traffic prediction, personalization in social media and e-commerce, spam filtering in emails, fraud detection, stock market trading, medical technology, automatic translation, and its impact in the transportation industry, healthcare, and social media.', 'duration': 436.089, 'highlights': ['Machine learning is used to predict traffic conditions based on average time taken and real-time location data, with Google Maps as an example. Google Maps uses machine learning and real-time location data to predict traffic conditions, distinguishing between clear, slow-moving, and heavily congested areas.', 'Machine learning is used for personalization in e-commerce and social media, targeting advertisements based on user interests and behavior. Google uses machine learning to personalize advertisements based on user interests, as demonstrated through targeted ads on platforms like Facebook, YouTube, and Instagram.', 'Gmail uses machine learning and spam filters to identify spam emails, analyzing characteristics and patterns to determine spam or non-spam content. Gmail employs machine learning and various spam filters, such as content filters and header filters, to identify and categorize spam emails based on specific characteristics and patterns.', 'Machine learning is utilized for online fraud detection, employing feed-forward neural networks to differentiate between genuine and fraudulent transactions. Feed-forward neural networks are used in online fraud detection to analyze transaction patterns and identify potentially fraudulent activities, such as identity theft and fake accounts.', 'Machine learning is extensively used in stock market trading, particularly with long short-term memory neural networks for predicting stock market trends. Stock market indices utilize long short-term memory neural networks to process and predict stock market data, especially in scenarios with time lags of unknown size and duration.', 'Machine learning has revolutionized assistive medical technology, aiding in disease diagnosis, 3D modeling, and predictive analysis for conditions like brain tumors and ischemic stroke lesions. Machine learning has facilitated disease identification, personalized treatment, and predictive analysis in medical fields, particularly in diagnosing brain tumors, ischemic stroke lesions, and fetal imaging.', 'Automatic translation employs machine learning using sequence to sequence learning, convolutional neural networks, and optical character recognition to translate text and identify images. Automatic translation utilizes machine learning algorithms, including sequence to sequence learning and convolutional neural networks, to translate text and identify images through optical character recognition.']}, {'end': 1951.351, 'start': 1499.116, 'title': 'Machine learning: overview and applications', 'summary': "Discusses the significance of google's deepmind project alphago, the basic principles of machine learning, and the general process of implementing machine learning, highlighting the importance and applications of classification, regression, anomaly detection, and clustering in real-world scenarios.", 'duration': 452.235, 'highlights': ["Google's DeepMind project AlphaGo defeating the world's number one Go player on May 27, 2017, showcasing the advancement of machine learning in mastering complex games. AlphaGo defeating the world's number one Go player, Kiji, on May 27, 2017, demonstrating the progress in machine learning.", 'Explanation of machine learning as the science of making computers learn and act like humans without being explicitly programmed, along with the key steps of collecting, preparing, training, testing, and deploying the model. Definition of machine learning and its key steps, including collecting, preparing, training, testing, and deploying the model.', "Importance of classification and regression in machine learning, with examples of categorizing stock price movements and predicting an individual's age based on various factors. Significance of classification and regression in machine learning, illustrated by examples of stock price categorization and age prediction based on different factors.", 'The growing significance of anomaly detection in data science for identifying irregular patterns, such as detecting unusual money withdrawals and abnormal stock market behaviors. Increasing importance of anomaly detection in data science, exemplified by the detection of irregular money withdrawals and unusual stock market behaviors.', 'The application of clustering in identifying groups of customers with similar behavior and demographics, demonstrating its relevance in marketing and decision-making processes. The role of clustering in identifying customer groups with similar behavior and demographics, showcasing its significance in marketing and decision-making processes.']}, {'end': 2165.884, 'start': 1951.731, 'title': 'Types of machine learning', 'summary': 'Discusses the types of machine learning, including supervised, unsupervised, and reinforcement learning, and their applications in classifying, predicting, finding hidden patterns, and learning from actions in an environment.', 'duration': 214.153, 'highlights': ['Supervised learning is used to classify and predict objects based on labeled data, such as predicting loan defaults and stock market performance. Supervised learning enables machines to classify and predict objects based on labeled data, such as predicting loan defaults and stock market performance.', 'Unsupervised learning finds hidden patterns in unlabeled data and can be used for customer segmentation and market targeting. Unsupervised learning finds hidden patterns in unlabeled data, enabling customer segmentation and market targeting.', 'Reinforcement learning involves learning how to behave in an environment by performing actions and observing the results. Reinforcement learning entails learning how to behave in an environment by performing actions and observing the results.']}], 'duration': 1103.357, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE1062527.jpg', 'highlights': ['Machine learning is used for personalization in e-commerce and social media, targeting advertisements based on user interests and behavior.', 'Machine learning is utilized for online fraud detection, employing feed-forward neural networks to differentiate between genuine and fraudulent transactions.', 'Machine learning has revolutionized assistive medical technology, aiding in disease diagnosis, 3D modeling, and predictive analysis for conditions like brain tumors and ischemic stroke lesions.', 'Automatic translation employs machine learning using sequence to sequence learning, convolutional neural networks, and optical character recognition to translate text and identify images.', "Google's DeepMind project AlphaGo defeating the world's number one Go player on May 27, 2017, showcasing the advancement of machine learning in mastering complex games.", 'Explanation of machine learning as the science of making computers learn and act like humans without being explicitly programmed, along with the key steps of collecting, preparing, training, testing, and deploying the model.', "Importance of classification and regression in machine learning, with examples of categorizing stock price movements and predicting an individual's age based on various factors.", 'The growing significance of anomaly detection in data science for identifying irregular patterns, such as detecting unusual money withdrawals and abnormal stock market behaviors.', 'The application of clustering in identifying groups of customers with similar behavior and demographics, demonstrating its relevance in marketing and decision-making processes.', 'Supervised learning is used to classify and predict objects based on labeled data, such as predicting loan defaults and stock market performance.', 'Unsupervised learning finds hidden patterns in unlabeled data and can be used for customer segmentation and market targeting.', 'Reinforcement learning involves learning how to behave in an environment by performing actions and observing the results.']}, {'end': 3299.918, 'segs': [{'end': 2590.917, 'src': 'embed', 'start': 2569.926, 'weight': 1, 'content': [{'end': 2582.813, 'text': 'And we have y equals mx plus c, where m equals the sum of x minus x average times, y minus y average or y means and x means over the sum of x minus.', 'start': 2569.926, 'duration': 12.887}, {'end': 2584.253, 'text': 'x means squared.', 'start': 2582.813, 'duration': 1.44}, {'end': 2586.574, 'text': "That's how we get the slope of the value of the line.", 'start': 2584.553, 'duration': 2.021}, {'end': 2589.496, 'text': 'And we can easily do that by creating some columns here.', 'start': 2586.955, 'duration': 2.541}, {'end': 2590.917, 'text': 'We have x, y.', 'start': 2589.576, 'duration': 1.341}], 'summary': 'The formula for finding the slope of a line is y = mx + c, where m is calculated using specific equations.', 'duration': 20.991, 'max_score': 2569.926, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE2569926.jpg'}, {'end': 2764.138, 'src': 'embed', 'start': 2726.528, 'weight': 0, 'content': [{'end': 2730.932, 'text': 'And when we plot the predicted values along with the actual values, we can see the difference.', 'start': 2726.528, 'duration': 4.404}, {'end': 2736.156, 'text': "And this is one of the things that's very important with linear regression in any of these models is to understand the error.", 'start': 2731.392, 'duration': 4.764}, {'end': 2739.339, 'text': 'And so we can calculate the error on all of our different values.', 'start': 2736.637, 'duration': 2.702}, {'end': 2743.603, 'text': 'And you can see over here we plotted x and y and y predict.', 'start': 2739.399, 'duration': 4.204}, {'end': 2747.826, 'text': 'And we draw in a little line so you can sort of see what the error looks like there between the different points.', 'start': 2744.043, 'duration': 3.783}, {'end': 2750.449, 'text': 'So our goal is to reduce this error.', 'start': 2748.467, 'duration': 1.982}, {'end': 2754.172, 'text': 'We want to minimize that error value on our linear regression model.', 'start': 2750.589, 'duration': 3.583}, {'end': 2755.713, 'text': 'Minimizing the distance.', 'start': 2754.532, 'duration': 1.181}, {'end': 2764.138, 'text': 'There are lots of ways to minimize the distance between the line and the data points, like sum of squared errors, sum of absolute errors,', 'start': 2756.173, 'duration': 7.965}], 'summary': 'Key goal: minimize error in linear regression model.', 'duration': 37.61, 'max_score': 2726.528, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE2726528.jpg'}, {'end': 2960.371, 'src': 'embed', 'start': 2933.636, 'weight': 4, 'content': [{'end': 2941.211, 'text': "What if this is much more complicated data, where it's not something that you would particularly understand, like studying cancer?", 'start': 2933.636, 'duration': 7.575}, {'end': 2949.9, 'text': 'They take about 36 measurements of the cancerous cells and then each one of those measurements represents how bulbous it is, how extended it is,', 'start': 2941.271, 'duration': 8.629}, {'end': 2951.181, 'text': 'how sharp the edges are.', 'start': 2949.9, 'duration': 1.281}, {'end': 2954.385, 'text': 'Something that as a human we would have no understanding of.', 'start': 2951.542, 'duration': 2.843}, {'end': 2960.371, 'text': "So how do we decide how to split that data up? And is that the right decision tree? So that's the question that's going to come up.", 'start': 2954.785, 'duration': 5.586}], 'summary': '36 measurements of cancerous cells, complex data analysis', 'duration': 26.735, 'max_score': 2933.636, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE2933636.jpg'}, {'end': 3075.851, 'src': 'embed', 'start': 3052.627, 'weight': 5, 'content': [{'end': 3060.832, 'text': 'And when we look at this, if we go back to the data, you can simply count how many yeses and no in our complete data set for playing golf days.', 'start': 3052.627, 'duration': 8.205}, {'end': 3067.376, 'text': 'In our complete set, we find we have 5 days we did play golf and 9 days we did not play golf.', 'start': 3061.432, 'duration': 5.944}, {'end': 3072.048, 'text': 'And so our i equals, if you add those together, 9 plus 5 is 14.', 'start': 3067.956, 'duration': 4.092}, {'end': 3075.851, 'text': 'And so our i equals 5 over 14 and 9 over 14.', 'start': 3072.048, 'duration': 3.803}], 'summary': 'Out of 14 days, 5 were for golf and 9 were not.', 'duration': 23.224, 'max_score': 3052.627, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE3052627.jpg'}, {'end': 3158.821, 'src': 'embed', 'start': 3127.433, 'weight': 2, 'content': [{'end': 3129.675, 'text': "Don't forget to put the, we'll divide that 5 out later on.", 'start': 3127.433, 'duration': 2.242}, {'end': 3130.775, 'text': 'equals P.', 'start': 3130.135, 'duration': 0.64}, {'end': 3135.776, 'text': 'overcast equals 4, comma 0, plus rainy equals 2, comma 3.', 'start': 3130.775, 'duration': 5.001}, {'end': 3139.557, 'text': 'and then, when you do the whole setup, we have 5 over 14.', 'start': 3135.776, 'duration': 3.781}, {'end': 3151.759, 'text': 'remember, I said there was a total of 5 5 over 14 times the I of 3 of 2 plus 4 over 14 times the 4, comma 0 and 5, 14 over I of 2, 3.', 'start': 3139.557, 'duration': 12.202}, {'end': 3158.821, 'text': 'and so we can now compute the entropy of just the part it has to do with the forecast and we get point six, nine,', 'start': 3151.759, 'duration': 7.062}], 'summary': 'Forecast analysis yields entropy of 0.69.', 'duration': 31.388, 'max_score': 3127.433, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE3127433.jpg'}], 'start': 2165.924, 'title': 'Machine learning basics and linear regression', 'summary': 'Explains the basics of machine learning, including reinforcement learning, supervised and unsupervised learning, and explores linear regression with examples, showcasing its applications. it also covers the mathematical implementation of linear regression and decision trees, including the steps to find the best-fit line, calculating error values, and minimizing the distance. additionally, it discusses the computation of entropy and information gain to build a decision tree for predicting whether to play golf based on weather conditions.', 'chapters': [{'end': 2451.299, 'start': 2165.924, 'title': 'Machine learning basics and linear regression', 'summary': 'Explains the basics of machine learning, including reinforcement learning, supervised and unsupervised learning, and explores linear regression with examples, showcasing its applications in predicting outcomes and relationships between variables.', 'duration': 285.375, 'highlights': ['The chapter explains the basics of machine learning, including reinforcement learning, supervised and unsupervised learning. It emphasizes the significance of machine learning in understanding how to learn and the distinction between supervised and unsupervised learning.', 'Linear regression is showcased with examples, demonstrating its applications in predicting outcomes and relationships between variables. The detailed examples illustrate how linear regression models can predict outcomes, compute distances, and display positive relationships between variables.']}, {'end': 3031.872, 'start': 2451.74, 'title': 'Linear regression and decision trees', 'summary': 'Covers the mathematical implementation of linear regression, including the steps to find the best-fit line, calculating error values, and minimizing the distance, with an example data set of x and y values. additionally, it explains decision trees, their representation of a course of action based on data, and the concepts of entropy and information gain, crucial in determining the right decision tree.', 'duration': 580.132, 'highlights': ['The chapter covers the mathematical implementation of linear regression, including the steps to find the best-fit line, calculating error values, and minimizing the distance, with an example data set of x and y values. It explains the process of finding the best-fit line through calculating the mean of x and y values, obtaining the regression equation, computing predicted y values, plotting the points, and calculating error values to minimize the distance between the line and the data points.', 'Additionally, it explains decision trees, their representation of a course of action based on data, and the concepts of entropy and information gain, crucial in determining the right decision tree. The explanation involves the concept of decision trees as a tree-shaped algorithm for determining a course of action based on data, and the importance of entropy and information gain in evaluating the right decision tree, where entropy measures randomness or impurity in the data set, and information gain measures the decrease in entropy after the data set is split.']}, {'end': 3299.918, 'start': 3032.353, 'title': 'Decision tree for golf', 'summary': 'Discusses the computation of entropy and information gain to build a decision tree for predicting whether to play golf based on weather conditions, with the highest information gain from outlook attribute of 0.247, followed by humidity (0.152) and windy day (0.048).', 'duration': 267.565, 'highlights': ['The highest information gain is from the outlook attribute with a value of 0.247, followed by humidity (0.152) and windy day (0.048), which determines the order of attribute splits in building the decision tree.', "The entropy of the target class 'play golf' is computed using the number of 'yes' (5 days) and 'no' (9 days) instances in the complete dataset, resulting in an overall entropy value of 0.94 for the entire dataset.", 'The computation of entropy for each predictor, such as outlook, temperature, humidity, and wind, contributes to determining the gain for each attribute, with the highest gain indicating the preferred split in building the decision tree.', "The process involves selecting the attribute with the largest information gain as the root node and further splitting each subnode based on the attribute's information gain to form a decision tree for predicting whether to play golf based on weather conditions."]}], 'duration': 1133.994, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE2165924.jpg', 'highlights': ['The chapter explains the basics of machine learning, including reinforcement learning, supervised and unsupervised learning.', 'Linear regression is showcased with examples, demonstrating its applications in predicting outcomes and relationships between variables.', 'The chapter covers the mathematical implementation of linear regression, including the steps to find the best-fit line, calculating error values, and minimizing the distance, with an example data set of x and y values.', 'Additionally, it explains decision trees, their representation of a course of action based on data, and the concepts of entropy and information gain, crucial in determining the right decision tree.', 'The highest information gain is from the outlook attribute with a value of 0.247, followed by humidity (0.152) and windy day (0.048), which determines the order of attribute splits in building the decision tree.', "The entropy of the target class 'play golf' is computed using the number of 'yes' (5 days) and 'no' (9 days) instances in the complete dataset, resulting in an overall entropy value of 0.94 for the entire dataset."]}, {'end': 4874.118, 'segs': [{'end': 3489.108, 'src': 'embed', 'start': 3461.191, 'weight': 4, 'content': [{'end': 3465.615, 'text': "And so based on these measurements, we want to guess whether we're making a muffin or a cupcake.", 'start': 3461.191, 'duration': 4.424}, {'end': 3468.917, 'text': "And you can see in this one, we don't have just two features.", 'start': 3465.955, 'duration': 2.962}, {'end': 3473.18, 'text': "We don't just have height and weight as we did before between the male and female.", 'start': 3469.057, 'duration': 4.123}, {'end': 3475.242, 'text': 'In here, we have a number of features.', 'start': 3473.34, 'duration': 1.902}, {'end': 3481.006, 'text': "In fact, in this, we're looking at eight different features to guess whether it's a muffin or a cupcake.", 'start': 3475.922, 'duration': 5.084}, {'end': 3489.108, 'text': "What's the difference between a muffin and a cupcake? Turns out muffins have more flour, while cupcakes have more butter and sugar.", 'start': 3481.923, 'duration': 7.185}], 'summary': 'Using 8 different features to guess muffin or cupcake based on ingredient differences.', 'duration': 27.917, 'max_score': 3461.191, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE3461191.jpg'}, {'end': 3544.478, 'src': 'embed', 'start': 3501.533, 'weight': 2, 'content': [{'end': 3507.574, 'text': 'I really just want to say cupcakes versus muffins, like some big professional wrestling thing.', 'start': 3501.533, 'duration': 6.041}, {'end': 3512.815, 'text': 'Before we start in our cupcakes versus muffins, we are going to be working in Python.', 'start': 3507.954, 'duration': 4.861}, {'end': 3515.755, 'text': "There's many versions of Python, many different editors.", 'start': 3513.075, 'duration': 2.68}, {'end': 3521.156, 'text': 'That is one of the strengths and weaknesses of Python is it just has so much stuff attached to it.', 'start': 3515.975, 'duration': 5.181}, {'end': 3525.737, 'text': "It's one of the more popular data science programming packages you can use.", 'start': 3521.216, 'duration': 4.521}, {'end': 3530.978, 'text': "In this case, we're going to go ahead and use Anaconda in Jupyter Notebook.", 'start': 3526.377, 'duration': 4.601}, {'end': 3535.069, 'text': 'The Anaconda Navigator has all kinds of fun tools.', 'start': 3531.365, 'duration': 3.704}, {'end': 3539.153, 'text': "Once you're into the Anaconda Navigator, you can change environments.", 'start': 3535.469, 'duration': 3.684}, {'end': 3541.655, 'text': 'I actually have a number of environments on here.', 'start': 3539.173, 'duration': 2.482}, {'end': 3544.478, 'text': "We'll be using Python 3.6 environment.", 'start': 3541.895, 'duration': 2.583}], 'summary': 'Python is popular for data science, with anaconda in jupyter notebook being a preferred environment, offering various tools and multiple environment options.', 'duration': 42.945, 'max_score': 3501.533, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE3501533.jpg'}, {'end': 3905.413, 'src': 'embed', 'start': 3882.515, 'weight': 1, 'content': [{'end': 3890.619, 'text': 'And if we flip back on over to the spreadsheet where we opened up our CSV file, you can see where it starts on line two.', 'start': 3882.515, 'duration': 8.104}, {'end': 3891.999, 'text': 'This one calls it zero.', 'start': 3890.759, 'duration': 1.24}, {'end': 3895.381, 'text': 'And then two, three, four, five, six is going to match.', 'start': 3892.539, 'duration': 2.842}, {'end': 3897.942, 'text': "Go ahead and close that out because we don't need that anymore.", 'start': 3895.401, 'duration': 2.541}, {'end': 3899.87, 'text': 'And it always starts at zero.', 'start': 3898.789, 'duration': 1.081}, {'end': 3905.413, 'text': "It automatically indexes it since we didn't tell it to use an index in here.", 'start': 3900.55, 'duration': 4.863}], 'summary': 'Demonstrating csv file indexing, starting from line two.', 'duration': 22.898, 'max_score': 3882.515, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE3882515.jpg'}, {'end': 4261.779, 'src': 'embed', 'start': 4230.76, 'weight': 6, 'content': [{'end': 4234.122, 'text': 'And so we have just the amount of flour and sugar, just the two sets of plots.', 'start': 4230.76, 'duration': 3.362}, {'end': 4242.646, 'text': "And just for fun, let's go ahead and take this over here and take our recipe features.", 'start': 4236.522, 'duration': 6.124}, {'end': 4250.691, 'text': "And so if we decided to use all the recipe features, you'll see that it makes a nice column of different data.", 'start': 4244.848, 'duration': 5.843}, {'end': 4252.633, 'text': 'So it just strips out all the labels and everything.', 'start': 4250.731, 'duration': 1.902}, {'end': 4254.214, 'text': 'We just have just the values.', 'start': 4252.673, 'duration': 1.541}, {'end': 4261.779, 'text': "But because we want to be able to view this easily in a plot later on, we'll go ahead and take that and just do flour and sugar.", 'start': 4254.954, 'duration': 6.825}], 'summary': 'Analyzing flour and sugar data for plot visualization.', 'duration': 31.019, 'max_score': 4230.76, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE4230760.jpg'}, {'end': 4703.163, 'src': 'embed', 'start': 4673.956, 'weight': 8, 'content': [{'end': 4678.917, 'text': 'corresponding line between the sugar and the flour and the muffin versus cupcake.', 'start': 4673.956, 'duration': 4.961}, {'end': 4686.199, 'text': 'And then we generated the support vectors, the yy down and yy up.', 'start': 4681.498, 'duration': 4.701}, {'end': 4688.499, 'text': "So let's take a look and see what that looks like.", 'start': 4686.559, 'duration': 1.94}, {'end': 4700.182, 'text': "So we'll do our plplot, and again this is all against xx, our x value, but this time we have yy down.", 'start': 4690, 'duration': 10.182}, {'end': 4703.163, 'text': "And let's do something a little fun with this.", 'start': 4701.582, 'duration': 1.581}], 'summary': 'Analyzing the relationship between sugar and flour in muffins and cupcakes, generating support vectors, and plotting against xx and yy values.', 'duration': 29.207, 'max_score': 4673.956, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE4673956.jpg'}, {'end': 4783.141, 'src': 'embed', 'start': 4759.634, 'weight': 7, 'content': [{'end': 4769.137, 'text': "I've got my recipes, I pulled off the internet and I want to see the difference between a muffin or a cupcake,", 'start': 4759.634, 'duration': 9.503}, {'end': 4774.438, 'text': 'and so we need a function to push that through and create a function with def.', 'start': 4769.137, 'duration': 5.301}, {'end': 4776.359, 'text': "and let's call it muffin or cupcake.", 'start': 4774.438, 'duration': 1.921}, {'end': 4781.92, 'text': "and remember, we're just doing flour and sugar today, not doing all the ingredients, and that actually is a pretty good split.", 'start': 4776.359, 'duration': 5.561}, {'end': 4783.141, 'text': "you really don't need all the ingredients.", 'start': 4781.92, 'duration': 1.221}], 'summary': 'Comparing muffin and cupcake recipes using a function to analyze flour and sugar split.', 'duration': 23.507, 'max_score': 4759.634, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE4759634.jpg'}, {'end': 4837.476, 'src': 'embed', 'start': 4810.174, 'weight': 0, 'content': [{'end': 4814.436, 'text': "Else, if it's not zero, that means it's one, then you're looking at a cupcake recipe.", 'start': 4810.174, 'duration': 4.262}, {'end': 4820.128, 'text': "That's pretty straightforward for function or def for definition.", 'start': 4815.305, 'duration': 4.823}, {'end': 4821.749, 'text': 'D-E-F is how you do that in Python.', 'start': 4820.188, 'duration': 1.561}, {'end': 4825.412, 'text': "And, of course, if you're going to create a function, you should run something in it.", 'start': 4822.89, 'duration': 2.522}, {'end': 4830.355, 'text': "And so let's run a cupcake, and we're going to send it values 50 and 20, a muffin or a cupcake.", 'start': 4825.652, 'duration': 4.703}, {'end': 4830.996, 'text': "I don't know what it is.", 'start': 4830.375, 'duration': 0.621}, {'end': 4833.853, 'text': "And let's run this and just see what it gives us.", 'start': 4831.892, 'duration': 1.961}, {'end': 4835.755, 'text': "And it says, oh, it's a muffin.", 'start': 4834.554, 'duration': 1.201}, {'end': 4837.476, 'text': "You're looking at a muffin recipe.", 'start': 4836.075, 'duration': 1.401}], 'summary': 'Python function determines cupcake or muffin recipe based on input values.', 'duration': 27.302, 'max_score': 4810.174, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE4810174.jpg'}], 'start': 3300.758, 'title': 'Support vector machine and anaconda in jupyter notebook', 'summary': 'Covers support vector machine, explaining its concept and application in classifying muffin and cupcake recipes using eight features. it also discusses using anaconda in jupyter notebook for data analysis, including setting up environments, importing standard packages, visualizing data with seaborn and matplotlib, and plotting data using seaborn.', 'chapters': [{'end': 3521.156, 'start': 3300.758, 'title': 'Support vector machine', 'summary': 'Introduces support vector machine (svm) as a widely used classification algorithm, explaining its concept of creating a separation line with the greatest margin to classify data points correctly, demonstrated through an example of classifying muffin and cupcake recipes using svm with eight different features.', 'duration': 220.398, 'highlights': ['SVM creates a separation line with the greatest possible margin between the decision line and the nearest point within the training set. SVM algorithm aims to choose a hyperplane with the greatest possible margin between the decision line and the nearest point within the training set, ensuring accurate classification.', 'Demonstration of classifying muffin and cupcake recipes using SVM with eight different features. The example illustrates the use of SVM in classifying muffin and cupcake recipes based on eight different features, showcasing its practical application in multi-dimensional data classification.', 'Explanation of the difference between a muffin and a cupcake based on their ingredients. The distinction between muffins and cupcakes is explained based on their ingredients, highlighting that muffins have more flour while cupcakes have more butter and sugar.']}, {'end': 4005.292, 'start': 3521.216, 'title': 'Using anaconda in jupyter notebook for data analysis', 'summary': 'Discusses using anaconda in jupyter notebook for data analysis, including setting up environments, importing standard packages like numpy and pandas, visualizing data with seaborn and matplotlib, and plotting data using seaborn.', 'duration': 484.076, 'highlights': ['Importing standard packages like NumPy and Pandas for data analysis The speaker discusses the use of standard packages like NumPy and Pandas for data analysis, where NumPy is used for number arrays and Pandas for creating data frames.', 'Visualizing data using Seaborn and Matplotlib The chapter emphasizes the importance of visualizing data and explains the use of Seaborn and Matplotlib for data visualization, including setting font scale and using Seaborn to create scatter plots.', 'Setting up environments and using Anaconda in Jupyter Notebook The speaker discusses the use of Anaconda in Jupyter Notebook and the ability to change environments, highlighting the use of Python 3.6 environment for data analysis.', "Plotting data using Seaborn and visualizing specific features like sugar and flour The chapter explains the process of plotting data using Seaborn, focusing on visualizing specific features like sugar and flour, and using Seaborn's scatter plot functionality."]}, {'end': 4379.346, 'start': 4007.452, 'title': 'Support vector classification model', 'summary': 'Explains the process of creating a support vector classification model for distinguishing between muffins and cupcakes, using flour and sugar as features and 0/1 as labels, and fitting the model with a linear kernel.', 'duration': 371.894, 'highlights': ['The chapter explains the process of creating a support vector classification model for distinguishing between muffins and cupcakes. The chapter discusses the process of creating a model to classify between muffins and cupcakes using SVM.', 'Using flour and sugar as features and 0/1 as labels. The model uses flour and sugar as features and assigns 0 for muffins and 1 for cupcakes.', 'Fitting the model with a linear kernel. The model is fitted using a linear kernel for the support vector classification.']}, {'end': 4874.118, 'start': 4379.886, 'title': 'Support vector classifier', 'summary': 'Explains the process of training a support vector classifier model and visualizing the separating hyperplane with a focus on flour and sugar data, ultimately creating a function to predict muffin or cupcake recipes.', 'duration': 494.232, 'highlights': ['Trained a support vector classifier model with a linear kernel and default settings The model was trained with a linear kernel and default settings, and the chapter focuses on visualizing the separating hyperplane with flour and sugar data.', 'Explained the mathematical basis of the separating hyperplane and coefficients The explanation delves into the mathematical basis of the separating hyperplane, including the coefficients and their connection to the two-dimensional plane represented by the model.', 'Created a function to predict muffin or cupcake recipes based on flour and sugar data A function was created to predict whether a recipe is for a muffin or a cupcake based on flour and sugar data, showcasing the practical application of the support vector classifier model.']}], 'duration': 1573.36, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE3300758.jpg', 'highlights': ['Demonstration of classifying muffin and cupcake recipes using SVM with eight different features.', 'Explanation of the difference between a muffin and a cupcake based on their ingredients.', 'Importing standard packages like NumPy and Pandas for data analysis.', 'Visualizing data using Seaborn and Matplotlib.', 'Setting up environments and using Anaconda in Jupyter Notebook.', 'The chapter explains the process of creating a support vector classification model for distinguishing between muffins and cupcakes.', 'Using flour and sugar as features and 0/1 as labels.', 'Trained a support vector classifier model with a linear kernel and default settings.', 'Created a function to predict muffin or cupcake recipes based on flour and sugar data.']}, {'end': 5565.311, 'segs': [{'end': 4902.806, 'src': 'embed', 'start': 4874.118, 'weight': 2, 'content': [{'end': 4877.899, 'text': "And then somebody went in here and decided we'll do YO for yellow.", 'start': 4874.118, 'duration': 3.781}, {'end': 4880.221, 'text': "Or it's kind of an orange-ish yellow color that's going to come out.", 'start': 4878.26, 'duration': 1.961}, {'end': 4882.082, 'text': 'Marker size 9.', 'start': 4880.621, 'duration': 1.461}, {'end': 4883.382, 'text': 'Those are settings you can play with.', 'start': 4882.082, 'duration': 1.3}, {'end': 4886.944, 'text': 'Somebody else played with them to come up with the right setup so it looks good.', 'start': 4883.602, 'duration': 3.342}, {'end': 4889.025, 'text': 'And you can see there it is graphed.', 'start': 4887.564, 'duration': 1.461}, {'end': 4890.426, 'text': 'Clearly a muffin.', 'start': 4889.545, 'duration': 0.881}, {'end': 4897.084, 'text': 'In this case, in cupcakes versus muffins, the muffin has won.', 'start': 4892.162, 'duration': 4.922}, {'end': 4902.806, 'text': "And if you'd like to do your own muffin cupcake contender series,", 'start': 4897.104, 'duration': 5.702}], 'summary': 'Marker size 9 used to create a graph of a muffin, winning in a cupcake versus muffin comparison.', 'duration': 28.688, 'max_score': 4874.118, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE4874118.jpg'}, {'end': 4959.465, 'src': 'embed', 'start': 4934.96, 'weight': 3, 'content': [{'end': 4943.144, 'text': 'Hence, we have built a classifier using SVM, which is able to classify if a recipe is of a cupcake or a muffin,', 'start': 4934.96, 'duration': 8.184}, {'end': 4945.825, 'text': 'which wraps up our cupcake versus muffin.', 'start': 4943.144, 'duration': 2.681}, {'end': 4951.547, 'text': "Today in our second tutorial, we're going to cover k-means and linear regression.", 'start': 4946.465, 'duration': 5.082}, {'end': 4955.584, 'text': 'along with going over the quiz questions we had during our first tutorial.', 'start': 4951.903, 'duration': 3.681}, {'end': 4959.465, 'text': "What's in it for you? We're going to cover clustering.", 'start': 4956.364, 'duration': 3.101}], 'summary': 'A classifier using svm distinguishes cupcake vs. muffin, now onto k-means and linear regression in the second tutorial.', 'duration': 24.505, 'max_score': 4934.96, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE4934960.jpg'}, {'end': 5130.053, 'src': 'embed', 'start': 5100.956, 'weight': 0, 'content': [{'end': 5105.619, 'text': 'Now, when I look at these data points, I would probably group them into two clusters just by looking at them.', 'start': 5100.956, 'duration': 4.663}, {'end': 5107.94, 'text': "I'd say two of these group of data kind of come together.", 'start': 5105.739, 'duration': 2.201}, {'end': 5117.366, 'text': 'But in k-means, we pick k clusters and assign random centroids to clusters, where the k clusters represents two different clusters.', 'start': 5108.56, 'duration': 8.806}, {'end': 5124.33, 'text': 'We pick k clusters and assign random centroids to the clusters, then we compute distance from objects to the centroids.', 'start': 5117.906, 'duration': 6.424}, {'end': 5130.053, 'text': 'Now we form new clusters based on minimum distances and calculate the centroids.', 'start': 5124.85, 'duration': 5.203}], 'summary': 'K-means algorithm groups data into two clusters based on distance from centroids.', 'duration': 29.097, 'max_score': 5100.956, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE5100956.jpg'}, {'end': 5164.104, 'src': 'embed', 'start': 5136.859, 'weight': 1, 'content': [{'end': 5142.665, 'text': 'Repeat previous two steps iteratively till the cluster centroid stop changing their positions and become static.', 'start': 5136.859, 'duration': 5.806}, {'end': 5148.572, 'text': 'Repeat previous two steps iteratively till the cluster centroid stop changing and the positions become static.', 'start': 5143.366, 'duration': 5.206}, {'end': 5154.396, 'text': 'Once the clusters become static, then K-means clustering algorithm is said to be converged.', 'start': 5149.071, 'duration': 5.325}, {'end': 5157.558, 'text': "And there's another term we see throughout machine learning is converged.", 'start': 5154.716, 'duration': 2.842}, {'end': 5164.104, 'text': "That means whatever math we're using to figure out the answer has come to a solution or it's converged on an answer.", 'start': 5157.739, 'duration': 6.365}], 'summary': 'Iterate k-means clustering until centroids are static, converging to a solution.', 'duration': 27.245, 'max_score': 5136.859, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE5136859.jpg'}, {'end': 5417.118, 'src': 'embed', 'start': 5389.339, 'weight': 4, 'content': [{'end': 5392.682, 'text': "And you can see that we've now formed two very distinct clusters on here.", 'start': 5389.339, 'duration': 3.343}, {'end': 5399.147, 'text': "On comparing the distance of each individual's distance to its own cluster mean and to that of the opposite cluster,", 'start': 5393.142, 'duration': 6.005}, {'end': 5401.009, 'text': 'we find that the data points are stable.', 'start': 5399.147, 'duration': 1.862}, {'end': 5402.73, 'text': 'Hence, we have our final clusters.', 'start': 5401.189, 'duration': 1.541}, {'end': 5407.969, 'text': 'Now, if you remember, I brought up a concept earlier on the k-means algorithm.', 'start': 5403.345, 'duration': 4.624}, {'end': 5411.973, 'text': 'Choosing the right value of k will help in less number of iterations.', 'start': 5408.21, 'duration': 3.763}, {'end': 5417.118, 'text': 'And to find the appropriate number of clusters in a data set, we use the ELBO method.', 'start': 5412.534, 'duration': 4.584}], 'summary': 'Stable data points formed two distinct clusters, k-means algorithm depends on the right value of k and elbo method determines appropriate number of clusters.', 'duration': 27.779, 'max_score': 5389.339, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE5389339.jpg'}], 'start': 4874.118, 'title': 'Creating classifiers and understanding k-means clustering', 'summary': 'Covers creating a svm classifier for recipes, introducing k-means clustering and logistic regression, with a live demo of clustering cars and classifying tumors. it also explains the k-means algorithm, its application in clustering data, and finding optimal clusters using the elbo method, with an example of clustering cars into brands.', 'chapters': [{'end': 5174.613, 'start': 4874.118, 'title': 'Cupcake vs muffin classifier', 'summary': 'Covers the creation of a svm classifier to classify recipes as either cupcakes or muffins, along with an introduction to k-means clustering and logistic regression, including a live demo of clustering cars based on brands and classifying tumors as malignant or benign.', 'duration': 300.495, 'highlights': ['Creation of SVM classifier to classify recipes as cupcakes or muffins The chapter discusses the creation of a support vector machine (SVM) classifier to determine if a recipe is for a cupcake or a muffin.', 'Introduction to k-means clustering and logistic regression The chapter introduces k-means clustering, an unsupervised learning technique used to group data based on feature similarities, and logistic regression for classifying tumors as malignant or benign.', 'Live demo of clustering cars based on brands and classifying tumors A live Python demo is conducted to demonstrate the clustering of cars based on brands and the classification of tumors as malignant or benign using logistic regression.']}, {'end': 5565.311, 'start': 5175.114, 'title': 'Understanding k-means clustering', 'summary': 'Explains the k-means algorithm, its application in clustering data, and the process of finding the optimal number of clusters using the elbo method, with an example of clustering cars into brands using parameters such as horsepower, cubic inches, make, and year.', 'duration': 390.197, 'highlights': ['K-means algorithm explained with the process of assigning random centroids to clusters, computing distances, forming new clusters based on minimum distance, and iteratively recalculating centroids until convergence. Random centroid assignment, distance computation, iterative centroid recalculation', 'Demonstration of selecting initial cluster centroids, assigning points to the closest cluster, calculating new centroids, and comparing individual distances to cluster means for clustering data. Selection of initial centroids, individual point assignment, centroid calculation, distance comparison', 'Explanation of finding the optimal number of clusters using the ELBO method and the process of iteratively computing k-means with different cluster numbers to identify the elbow joint. Optimal cluster number determination, ELBO method, iterative k-means computation', 'Application of k-means clustering to cluster cars into brands using parameters such as horsepower, cubic inches, make, and year, with the use of Python libraries like numpy, pandas, and matplotlib. Application to clustering cars into brands, use of Python libraries']}], 'duration': 691.193, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE4874118.jpg', 'highlights': ['Creation of SVM classifier to classify recipes as cupcakes or muffins', 'Introduction to k-means clustering and logistic regression', 'Live demo of clustering cars based on brands and classifying tumors', 'Explanation of finding the optimal number of clusters using the ELBO method', 'Application of k-means clustering to cluster cars into brands using parameters such as horsepower, cubic inches, make, and year']}, {'end': 7028.129, 'segs': [{'end': 5617.685, 'src': 'embed', 'start': 5588.744, 'weight': 0, 'content': [{'end': 5593.948, 'text': 'Remember, you can always post this in the comments and request the data files for these,', 'start': 5588.744, 'duration': 5.204}, {'end': 5598.712, 'text': 'either in the comments here on the YouTube video or go to simplylearn.com and request that.', 'start': 5593.948, 'duration': 4.764}, {'end': 5603.395, 'text': "The card CSV, I put it in the same folder as the code that I've stored.", 'start': 5599.072, 'duration': 4.323}, {'end': 5607.538, 'text': "So my Python code is stored in the same folder, so I don't have to put the full path.", 'start': 5603.455, 'duration': 4.083}, {'end': 5611.902, 'text': 'If you store them in different folders, you do have to change this and double-check your name variables.', 'start': 5608.039, 'duration': 3.863}, {'end': 5617.685, 'text': "And we'll go ahead and run this, and we've chosen dataset arbitrarily because it's a dataset we're importing.", 'start': 5612.262, 'duration': 5.423}], 'summary': 'Instructions for requesting data files and storing csv in the same folder as python code.', 'duration': 28.941, 'max_score': 5588.744, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE5588744.jpg'}, {'end': 5695.266, 'src': 'embed', 'start': 5665.683, 'weight': 2, 'content': [{'end': 5667.203, 'text': "that doesn't actually mean something.", 'start': 5665.683, 'duration': 1.52}, {'end': 5676.748, 'text': 'And so one of the tricks you can do with this is we can take x of i, and in addition to that, we want to go ahead and turn this into an integer,', 'start': 5667.424, 'duration': 9.324}, {'end': 5677.929, 'text': 'because a lot of these are integers.', 'start': 5676.748, 'duration': 1.181}, {'end': 5681.52, 'text': "So we'll go ahead and keep it integers, and Let me add the bracket here.", 'start': 5678.129, 'duration': 3.391}, {'end': 5682.921, 'text': 'And a lot of editors will do this.', 'start': 5681.54, 'duration': 1.381}, {'end': 5684.582, 'text': "They'll think that you're closing one bracket.", 'start': 5682.961, 'duration': 1.621}, {'end': 5687.483, 'text': "Make sure you get that second bracket in there if it's a double bracket.", 'start': 5684.902, 'duration': 2.581}, {'end': 5689.704, 'text': "That's always something that happens regularly.", 'start': 5687.943, 'duration': 1.761}, {'end': 5695.266, 'text': 'So once we have our integer of x of y, this is going to fill in any missing data with the average.', 'start': 5690.164, 'duration': 5.102}], 'summary': 'Using x of i as integers, fill missing data with the average.', 'duration': 29.583, 'max_score': 5665.683, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE5665683.jpg'}, {'end': 6391.042, 'src': 'embed', 'start': 6363.875, 'weight': 3, 'content': [{'end': 6371.317, 'text': "You can see a very nice elbow joint there at 2, and again, right around 3 and 4, and then after that, there's not very much.", 'start': 6363.875, 'duration': 7.442}, {'end': 6381.479, 'text': "Now, as a data scientist, if I was looking at this, I would do either 3 or 4, and I'd actually try both of them to see what the output looked like.", 'start': 6372.457, 'duration': 9.022}, {'end': 6385.6, 'text': "And they've already tried this in the back, so we're just going to use 3 as a setup on here.", 'start': 6381.899, 'duration': 3.701}, {'end': 6391.042, 'text': "And let's go ahead and see what that looks like when we actually use this to show the different kinds of cars.", 'start': 6385.84, 'duration': 5.202}], 'summary': 'Elbow joint visible at 2, 3, and 4, with preference for 3 in showing car types.', 'duration': 27.167, 'max_score': 6363.875, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE6363875.jpg'}, {'end': 7033.011, 'src': 'embed', 'start': 7008.357, 'weight': 1, 'content': [{'end': 7015.263, 'text': "So why would you want to do that if you know you're just going to go get a biopsy because it's that serious? This is like an all or nothing.", 'start': 7008.357, 'duration': 6.906}, {'end': 7017.484, 'text': "Just referencing the domain, it's important.", 'start': 7015.703, 'duration': 1.781}, {'end': 7024.487, 'text': 'It might help the doctor know where to look just by understanding what kind of tumor it is.', 'start': 7017.784, 'duration': 6.703}, {'end': 7028.129, 'text': 'So it might help them or aid them in something they missed from before.', 'start': 7024.847, 'duration': 3.282}, {'end': 7033.011, 'text': "So let's go ahead and dive into the code, and I'll come back to the domain part of it in just a minute.", 'start': 7028.669, 'duration': 4.342}], 'summary': 'Understanding tumor type can aid doctors in locating missed areas.', 'duration': 24.654, 'max_score': 7008.357, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE7008357.jpg'}], 'start': 5565.411, 'title': 'Data analysis and modeling for car data', 'summary': 'Covers importing and preprocessing a car dataset, converting object types to numeric values, using elbo method for determining optimal clusters, and applying logistic regression for categorizing outcomes with a threshold of 0.5.', 'chapters': [{'end': 5866.244, 'start': 5565.411, 'title': 'Data analysis and preprocessing for car dataset', 'summary': "Covers importing a car dataset, preprocessing the data by filling missing values with the average, and removing the 'brand' column for further analysis.", 'duration': 300.833, 'highlights': ['Importing the car dataset and using pandas to read the data from a CSV file. The chapter discusses importing the car dataset and utilizing pandas to read the data from a CSV file.', 'Preprocessing the data by filling missing values with the average of the column. It covers the process of filling missing values in the dataset with the average value for that column, ensuring data integrity.', "Removing the 'brand' column for further analysis. The chapter explains the removal of the 'brand' column from the dataset to focus on analyzing other variables."]}, {'end': 6073.162, 'start': 5866.685, 'title': 'Data conversion and null value handling in pandas', 'summary': "Explains how to convert object types to numeric values using the 'convert_numeric' function in pandas, and then demonstrates how to eliminate null values from the data using various methods, ensuring all columns have zero null values.", 'duration': 206.477, 'highlights': ["The 'convert_numeric' function is used to convert object types to numeric values in Pandas, ensuring all data is in a numeric format. Usage of the 'convert_numeric' function in Pandas.", 'Demonstrates the process of eliminating null values from the data using various methods, ensuring all columns have zero null values. Process of handling null values and ensuring all columns have zero null values.', 'Importance of checking and double-checking the data to ensure it is clean, with no null values and all data converted to numeric format. Emphasis on the importance of data cleanliness and validation, with no null values and all data converted to numeric format.']}, {'end': 6300.685, 'start': 6074.303, 'title': 'Using elbo method for k-means clustering', 'summary': 'Covers the use of elbo method to find the optimal number of clusters by iterating through the k-means clustering process 11 times, using the inertia values to assess the change in differences as the number of clusters increases.', 'duration': 226.382, 'highlights': ['Iterating through k-means clustering process 11 times to find optimal number of clusters, using inertia values to assess change in differences.', 'Using ELBO method to find optimal number of clusters by running a small sample of data, especially for larger data sources.', 'Detailing the process of creating and fitting k-means object, and appending inertia values to an array for visualization.']}, {'end': 6800.664, 'start': 6301.265, 'title': 'K-means clustering for car data', 'summary': 'Demonstrates the use of k-means clustering on car data to identify distinct clusters, with a preference for 3 clusters and a visualization showing clear separation of car makes.', 'duration': 499.399, 'highlights': ['The elbow method reveals a clear preference for 3 clusters in the data, with a noticeable elbow joint at 2 and around 3 and 4, indicating the choice of 3 clusters for analysis. The elbow method analysis shows a distinct preference for 3 clusters, as evidenced by the elbow joint observed at 2, 3, and 4, influencing the decision to use 3 clusters for further analysis.', 'The application of k-means clustering to the car data results in the creation of 3 distinct clusters based on the specified number of clusters, and the choice to use 3 clusters is reinforced through visualization and analysis. The application of k-means clustering to the car data leads to the creation of 3 distinct clusters, thus reinforcing the decision to use 3 clusters for subsequent analysis, supported by visualization and data analysis.', 'The visualization of the clustered car data demonstrates clear separation of car makes, with distinct clusters for Toyota, Nissan, and Honda, highlighting the effectiveness of k-means clustering in identifying meaningful patterns within the data. The visualization of the clustered car data showcases clear separation of car makes, emphasizing distinct clusters for Toyota, Nissan, and Honda, underscoring the effectiveness of k-means clustering in revealing meaningful patterns within the data.']}, {'end': 7028.129, 'start': 6801.519, 'title': 'Introduction to logistic regression', 'summary': 'Discusses logistic regression, its application in categorizing outcomes, and the sigmoid function, highlighting its significance in predicting categorical values with a threshold of 0.5 and its application in medical classification problems.', 'duration': 226.61, 'highlights': ['The sigmoid function is crucial in logistic regression as it predicts categorical values with a threshold of 0.5, indicating whether a student will pass or fail based on their studying hours, and its application in classifying tumors as malignant or benign.', 'The chapter explains the use of logistic regression in categorizing outcomes and the significance of the sigmoid function in predicting categorical values with a threshold of 0.5, particularly in medical classification problems.', 'The logistic regression algorithm is the simplest classification algorithm used for binary or multi-classification problems, and the sigmoid function is applied to predict categorical values with a threshold of 0.5, aiding in classifying tumors as malignant or benign.']}], 'duration': 1462.718, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE5565411.jpg', 'highlights': ['The elbow method reveals a clear preference for 3 clusters in the data, with a noticeable elbow joint at 2 and around 3 and 4, indicating the choice of 3 clusters for analysis.', 'The visualization of the clustered car data demonstrates clear separation of car makes, with distinct clusters for Toyota, Nissan, and Honda, highlighting the effectiveness of k-means clustering in identifying meaningful patterns within the data.', 'The logistic regression algorithm is the simplest classification algorithm used for binary or multi-classification problems, and the sigmoid function is applied to predict categorical values with a threshold of 0.5, aiding in classifying tumors as malignant or benign.', 'Importing the car dataset and using pandas to read the data from a CSV file. The chapter discusses importing the car dataset and utilizing pandas to read the data from a CSV file.', 'Iterating through k-means clustering process 11 times to find optimal number of clusters, using inertia values to assess change in differences.']}, {'end': 8393.233, 'segs': [{'end': 7741.393, 'src': 'embed', 'start': 7709.969, 'weight': 0, 'content': [{'end': 7713.431, 'text': 'I noticed in one of the more modern ways, they actually split it into three groups.', 'start': 7709.969, 'duration': 3.462}, {'end': 7717.632, 'text': 'And then you model each group and test it against the other groups.', 'start': 7713.891, 'duration': 3.741}, {'end': 7719.873, 'text': "So you have all kinds of and there's reasons for that.", 'start': 7718.172, 'duration': 1.701}, {'end': 7725.057, 'text': 'Pass the scope of this and for this particular example, is it necessary? For this?', 'start': 7720.533, 'duration': 4.524}, {'end': 7729.542, 'text': "we're just going to split it into two groups one to train our data and one to test our data.", 'start': 7725.057, 'duration': 4.485}, {'end': 7735.247, 'text': 'And the sklearn.modelselection, we have train test split.', 'start': 7729.922, 'duration': 5.325}, {'end': 7741.393, 'text': 'You could write your own quick code to do this where you just randomly divide the data up into two groups, but they do it for us nicely.', 'start': 7735.507, 'duration': 5.886}], 'summary': "Data is split into two groups for training and testing, using sklearn.modelselection's train test split method.", 'duration': 31.424, 'max_score': 7709.969, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE7709969.jpg'}, {'end': 7850.071, 'src': 'embed', 'start': 7813.408, 'weight': 2, 'content': [{'end': 7815.229, 'text': 'Now we get to the actual logistics part.', 'start': 7813.408, 'duration': 1.821}, {'end': 7817.55, 'text': "We're actually going to create our model.", 'start': 7815.269, 'duration': 2.281}, {'end': 7820.772, 'text': "So let's go ahead and bring that in from sklearn.", 'start': 7818.191, 'duration': 2.581}, {'end': 7822.333, 'text': "We're going to bring in our linear model.", 'start': 7820.832, 'duration': 1.501}, {'end': 7825.194, 'text': "And we're going to import logistic regression.", 'start': 7822.653, 'duration': 2.541}, {'end': 7827.095, 'text': "That's the actual model we're using.", 'start': 7825.314, 'duration': 1.781}, {'end': 7829.096, 'text': "And we'll call it log model.", 'start': 7827.436, 'duration': 1.66}, {'end': 7836.38, 'text': "Model And let's just set this equal to our logistic regression that we just imported.", 'start': 7831.518, 'duration': 4.862}, {'end': 7840.963, 'text': 'So now we have a variable log model set to that class for us to use.', 'start': 7837.261, 'duration': 3.702}, {'end': 7850.071, 'text': 'And with most of the models in the sklearn, we just need to go ahead and fix it, fit, do a fit on there.', 'start': 7842.085, 'duration': 7.986}], 'summary': 'Creating logistic regression model using sklearn for fitting', 'duration': 36.663, 'max_score': 7813.408, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE7813408.jpg'}, {'end': 8251.193, 'src': 'embed', 'start': 8222.643, 'weight': 1, 'content': [{'end': 8224.324, 'text': 'Was something released in social media??', 'start': 8222.643, 'duration': 1.681}, {'end': 8225.084, 'text': 'Was something released?', 'start': 8224.384, 'duration': 0.7}, {'end': 8226.065, 'text': 'You can see where.', 'start': 8225.325, 'duration': 0.74}, {'end': 8231.968, 'text': 'knowing where that anomaly is can help you to figure out what the answer is to it in another area.', 'start': 8226.065, 'duration': 5.903}, {'end': 8237.29, 'text': 'D, predicting salary of an individual based on his or her years of experience.', 'start': 8232.407, 'duration': 4.883}, {'end': 8239.552, 'text': 'This is an example of regression.', 'start': 8237.831, 'duration': 1.721}, {'end': 8247.056, 'text': 'This problem can be mathematically defined as a function between independent years of experience and dependent variables, salary of an individual.', 'start': 8239.791, 'duration': 7.265}, {'end': 8251.193, 'text': 'And if you guessed that this was a regression model, give yourself a thumbs up.', 'start': 8247.691, 'duration': 3.502}], 'summary': 'Identifying anomaly location to solve a regression model predicting salary based on experience.', 'duration': 28.55, 'max_score': 8222.643, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE8222643.jpg'}], 'start': 7028.669, 'title': 'Data science and model evaluation', 'summary': 'Covers importing data using pandas, visualizing and analyzing data with pandas and seaborn, splitting data into training and testing sets, and evaluating a logistic regression model achieving 91-92% precision in predicting tumor types. it also discusses the importance of data preparation, model evaluation using precision and classification report, and real-world applications of machine learning models.', 'chapters': [{'end': 7177.864, 'start': 7028.669, 'title': 'Data import and overview of csv data', 'summary': 'Covers importing data using pandas and provides an overview of the csv file containing 36 different measurements related to tumorous growth, with options for diagnosis being m for malignant and b for benign.', 'duration': 149.195, 'highlights': ['The chapter covers importing data using pandas and provides an overview of the CSV file It demonstrates the use of pandas to read a CSV file and provides a high-level overview of the data.', 'The CSV file contains 36 different measurements related to tumorous growth It mentions the presence of 36 different measurements in the CSV file related to tumorous growth.', "Options for diagnosis being M for malignant and B for benign It specifies the two options for diagnosis in the data, with 'M' representing malignant and 'B' representing benign."]}, {'end': 7680.504, 'start': 7178.784, 'title': 'Exploring data with pandas and seaborn', 'summary': 'Explores using pandas and seaborn to visualize and analyze data, including generating a joint plot, heat map, and checking for null values, with a focus on identifying key features for classification.', 'duration': 501.72, 'highlights': ["The chapter demonstrates the use of Pandas to explore data and visualize the first five lines of data using 'data.head', providing an overview of the columns and their contents.", "It explains the utilization of Seaborn's joint plot to visualize the relationship between specific columns, such as the radius mean and texture mean, offering a clear graphical representation of the data distribution.", "It showcases the creation of a Seaborn heat map using 'SNS.heatmap' to display correlations between different features, identifying strong and weak correlations, which is crucial for feature selection and model building.", "The chapter emphasizes the importance of checking for null values in the data using 'data.isnull().sum()', ensuring data integrity and preventing potential errors in subsequent analysis and modeling steps."]}, {'end': 7948.946, 'start': 7681.684, 'title': 'Data splitting for model testing', 'summary': "Discusses the process of splitting data into training and testing sets using sklearn's train test split, where 70% of the data is used for training and 30% for testing, and then creating and testing a logistic regression model.", 'duration': 267.262, 'highlights': ['The process of splitting data into training and testing sets is crucial in data science for model testing. Splitting the data into two groups, with 70% for training and 30% for testing, is a standard practice in model testing.', "The use of sklearn's train test split to divide the data into training and testing sets is demonstrated. The sklearn library provides a convenient function, train test split, for splitting the data into training and testing sets.", "The creation and fitting of a logistic regression model using sklearn is explained. The logistic regression model is created and fitted using the sklearn library, followed by testing the model's functionality."]}, {'end': 8393.233, 'start': 7949.367, 'title': 'Data science and model evaluation', 'summary': 'Discusses the importance of data preparation in data science, model evaluation using precision and classification report, and examples of machine learning concepts, achieving 91-92% precision in predicting tumor types and real-world applications of machine learning models, followed by a review of various data types including qualitative, categorical, and ordinal data.', 'duration': 443.866, 'highlights': ["The chapter discusses the importance of data preparation in data science and model evaluation, highlighting the significance of good data for accurate results. The speaker emphasizes the time spent on data preparation and the impact of good data on the quality of answers, stating 'good data in, good answers out' and 'bad data in, bad answers out.'", 'The chapter explains model evaluation using precision and classification report, achieving a 91-92% precision in predicting tumor types and real-world applications of machine learning models. The precision of 91-92% in predicting tumor types is highlighted, emphasizing its significance in a medical domain with catastrophic outcomes and real-world applications in identifying different forms of cancer.', 'The chapter provides examples of machine learning concepts, including clustering, classification, anomaly detection, and regression, with real-world applications and model selection considerations. Real-world examples of machine learning concepts like clustering, classification, anomaly detection, and regression are presented along with model selection considerations, such as using k-means clustering for grouping documents and support vector machine (SVM) for classifying handwritten digits.', 'The chapter reviews various data types including qualitative, categorical, and ordinal data, providing detailed explanations and examples for each type. The chapter delves into qualitative and quantitative data types, providing clear definitions and examples for nominal and ordinal data, emphasizing the importance of understanding data types for effective analysis and interpretation.']}], 'duration': 1364.564, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE7028669.jpg', 'highlights': ["The chapter emphasizes the importance of checking for null values in the data using 'data.isnull().sum()', ensuring data integrity and preventing potential errors in subsequent analysis and modeling steps.", 'The chapter explains model evaluation using precision and classification report, achieving a 91-92% precision in predicting tumor types and real-world applications of machine learning models.', 'The chapter provides examples of machine learning concepts, including clustering, classification, anomaly detection, and regression, with real-world applications and model selection considerations.', 'The chapter discusses the importance of data preparation in data science and model evaluation, highlighting the significance of good data for accurate results.']}, {'end': 9852.04, 'segs': [{'end': 8715.567, 'src': 'embed', 'start': 8678.462, 'weight': 2, 'content': [{'end': 8681.203, 'text': "So when we're talking about linear equations, that's what we're talking about.", 'start': 8678.462, 'duration': 2.741}, {'end': 8682.864, 'text': 'In their addition.', 'start': 8681.603, 'duration': 1.261}, {'end': 8692.627, 'text': 'if you have already dived into, say, neural networks, you should recognize this AX plus, BY plus, CZ setup plus, the intercept,', 'start': 8682.864, 'duration': 9.763}, {'end': 8697.389, 'text': 'which is basically your neural network, each node, adding up all the different inputs.', 'start': 8692.627, 'duration': 4.762}, {'end': 8699.75, 'text': 'And we can drill down into that.', 'start': 8698.069, 'duration': 1.681}, {'end': 8703.431, 'text': 'Most common formula is your Y equals MX plus C.', 'start': 8700.11, 'duration': 3.321}, {'end': 8715.567, 'text': 'So you have your Y equals the M, which is your slope, your X value plus C, which is your Y intercept.', 'start': 8705.461, 'duration': 10.106}], 'summary': 'Linear equations involve addition of variables and have common formula y=mx+c.', 'duration': 37.105, 'max_score': 8678.462, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE8678462.jpg'}, {'end': 9047.793, 'src': 'embed', 'start': 9018.81, 'weight': 3, 'content': [{'end': 9022.033, 'text': 'In mathematics, a one-dimensional matrix is called a vector.', 'start': 9018.81, 'duration': 3.223}, {'end': 9030.359, 'text': "So if you have your X plot and you have a single value, that value is along the X axis and it's a single dimension.", 'start': 9022.953, 'duration': 7.406}, {'end': 9033.941, 'text': 'If you have two dimensions, you can think about putting them on a graph.', 'start': 9031.019, 'duration': 2.922}, {'end': 9039.085, 'text': 'You might have X and you might have Y, and each value denotes a direction.', 'start': 9034.362, 'duration': 4.723}, {'end': 9043.729, 'text': 'And then, of course, the actual distance is going to be the hypothesis of that triangle.', 'start': 9039.345, 'duration': 4.384}, {'end': 9047.793, 'text': 'And you can do that with three dimensionals, x, y, and z.', 'start': 9044.549, 'duration': 3.244}], 'summary': 'In mathematics, a one-dimensional matrix is a vector, with 2 dimensions represented on a graph and 3 dimensions as x, y, and z.', 'duration': 28.983, 'max_score': 9018.81, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE9018810.jpg'}, {'end': 9296.004, 'src': 'embed', 'start': 9267.057, 'weight': 0, 'content': [{'end': 9269.078, 'text': 'We could also do something kind of fun.', 'start': 9267.057, 'duration': 2.021}, {'end': 9270.939, 'text': "There's a lot of different ways to do this.", 'start': 9269.098, 'duration': 1.841}, {'end': 9277.541, 'text': 'As far as a plus b, I can also do a plus b dot t.', 'start': 9272.199, 'duration': 5.342}, {'end': 9284.024, 'text': "And you're going to see that that will come out the same, the 30, 24, whether I transpose a and b or transpose them both at the end.", 'start': 9277.541, 'duration': 6.483}, {'end': 9289.32, 'text': 'And likewise, we can very easily subtract two vectors.', 'start': 9286.738, 'duration': 2.582}, {'end': 9296.004, 'text': 'I can go A minus B, and we run that, and we get minus 10, 6.', 'start': 9289.38, 'duration': 6.624}], 'summary': 'Vectors can be manipulated using addition, subtraction, and transposition, yielding consistent results.', 'duration': 28.947, 'max_score': 9267.057, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE9267057.jpg'}, {'end': 9500.809, 'src': 'embed', 'start': 9476.053, 'weight': 1, 'content': [{'end': 9485.299, 'text': "two by three layer matrix for a, and we can also put together always kind of fun when you're playing with print values.", 'start': 9476.053, 'duration': 9.246}, {'end': 9494.465, 'text': 'we can do something like this we could go in here, there we go, we could print a, we have it end with equals, a run,', 'start': 9485.299, 'duration': 9.166}, {'end': 9496.307, 'text': 'and this kind of gives it a nice look.', 'start': 9494.465, 'duration': 1.842}, {'end': 9497.327, 'text': "here's your matrix.", 'start': 9496.307, 'duration': 1.02}, {'end': 9497.687, 'text': "that's all.", 'start': 9497.327, 'duration': 0.36}, {'end': 9500.809, 'text': 'this is comma and means it just tags it on the end.', 'start': 9497.687, 'duration': 3.122}], 'summary': 'Demonstrating a 2x3 matrix with print values and a nice look.', 'duration': 24.756, 'max_score': 9476.053, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE9476053.jpg'}], 'start': 8394.073, 'title': 'Quantitative data, linear algebra basics, and matrix operations', 'summary': 'Distinguishes between discrete and continuous quantitative data, provides an overview of linear algebra, and covers matrix operations such as addition, subtraction, and multiplication, with practical examples and applications in data science.', 'chapters': [{'end': 8512.552, 'start': 8394.073, 'title': 'Quantitative data: discrete vs continuous', 'summary': 'Covers the distinction between discrete and continuous quantitative data, with examples including test questions and stock market values, highlighting the characteristics and applications of each type.', 'duration': 118.479, 'highlights': ['Discrete data represents a final set of values and is simple to count, such as the number of questions on a test, which is limited to a specific range, usually integers like 100 questions or dollar amounts in the stock market.', 'Continuous data can take any numerical value within a range, such as water pressure or weight of a person, and includes values that fall between discrete and continuous, like the stock market where dollar amounts exhibit variance and complexity.', 'Ordinal data involves categorization into buckets to count how many people are in each group, with examples like membership in income ranges, which are either part of a group or not.']}, {'end': 9080.087, 'start': 8513.032, 'title': 'Linear algebra overview', 'summary': 'Provides an overview of linear algebra, including the basics of linear equations, matrices, and vectors, emphasizing the importance of understanding linear equations and matrix operations in mathematical computations.', 'duration': 567.055, 'highlights': ['The chapter introduces the basics of linear algebra, including linear equations, matrices, and vectors, emphasizing their importance in mathematical computations.', 'The importance of understanding linear equations and matrix operations in mathematical computations is highlighted, including the significance of linear equations with maximum order of 1 and their relevance to neural networks and slope gradient lines.', 'The significance of matrix operations, including addition, subtraction, and multiplication, is explained, emphasizing the impact of dimensions and shape on these operations.', 'The concept of matrix transpose and inverse is discussed, highlighting their role in flipping the matrix over its diagonal and changing the signs of values across its main diagonal.', 'The chapter explains the concept of vectors and their use in categorizing data based on distance calculations using the Pythagorean theorem, enabling easy comparison of data points.']}, {'end': 9500.809, 'start': 9080.848, 'title': 'Linear algebra basics', 'summary': 'Introduces i-gene vectors and i-gene values in linear algebra, demonstrating their usage in transforming vectors and performing operations like addition, subtraction, scalar multiplication, dot product, and complex matrix creation using numpy, with examples and results provided.', 'duration': 419.961, 'highlights': ['Demonstrating the usage of i-gene vectors and i-gene values in transforming vectors and performing operations like addition, subtraction, scalar multiplication, dot product, and complex matrix creation using NumPy. The chapter explains the concept of i-gene vectors and i-gene values and how they are used to transform vectors and perform various operations like addition, subtraction, scalar multiplication, dot product, and complex matrix creation using NumPy.', 'Providing examples and results for operations like addition, subtraction, scalar multiplication, dot product, and complex matrix creation using NumPy. The chapter provides examples and results for performing operations such as addition, subtraction, scalar multiplication, dot product, and complex matrix creation using NumPy, illustrating the practical application of these concepts.']}, {'end': 9852.04, 'start': 9500.809, 'title': 'Matrix operations and manipulations', 'summary': 'Covers various matrix operations, including addition, subtraction, scalar multiplication, matrix-vector multiplication, matrix-matrix multiplication, transpose, identity matrix, and inverse matrix, demonstrating their applications in data science and plotting.', 'duration': 351.231, 'highlights': ['Demonstrates matrix operations such as addition, subtraction, scalar multiplication, matrix-vector multiplication, matrix-matrix multiplication, transpose, identity matrix, and inverse matrix. The chapter covers the fundamental matrix operations and manipulations, including addition, subtraction, scalar multiplication, matrix-vector multiplication, matrix-matrix multiplication, transpose, identity matrix, and inverse matrix.', 'Illustrates the application of matrix operations in data science and plotting. The transcript emphasizes the relevance of matrix operations in data science and plotting, providing practical insights into their applications in these fields.']}], 'duration': 1457.967, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE8394073.jpg', 'highlights': ['The chapter covers the fundamental matrix operations and manipulations, including addition, subtraction, scalar multiplication, matrix-vector multiplication, matrix-matrix multiplication, transpose, identity matrix, and inverse matrix.', 'The chapter introduces the basics of linear algebra, including linear equations, matrices, and vectors, emphasizing their importance in mathematical computations.', 'Demonstrating the usage of i-gene vectors and i-gene values in transforming vectors and performing operations like addition, subtraction, scalar multiplication, dot product, and complex matrix creation using NumPy.', 'The concept of matrix transpose and inverse is discussed, highlighting their role in flipping the matrix over its diagonal and changing the signs of values across its main diagonal.', 'The importance of understanding linear equations and matrix operations in mathematical computations is highlighted, including the significance of linear equations with maximum order of 1 and their relevance to neural networks and slope gradient lines.']}, {'end': 10858.47, 'segs': [{'end': 10465.654, 'src': 'embed', 'start': 10434.205, 'weight': 0, 'content': [{'end': 10436.206, 'text': "And it's playing high-low.", 'start': 10434.205, 'duration': 2.001}, {'end': 10441.547, 'text': 'How do you play high-low, not get stuck in the valleys, figure out these curves and things like that?', 'start': 10436.266, 'duration': 5.281}, {'end': 10446.749, 'text': 'Well, you do that and the back end is all the calculus and differential equations to calculate this out.', 'start': 10442.048, 'duration': 4.701}, {'end': 10449.75, 'text': "The good news is you don't have to do those.", 'start': 10447.81, 'duration': 1.94}, {'end': 10453.131, 'text': "So instead, we're going to put together the code.", 'start': 10451.331, 'duration': 1.8}, {'end': 10458.233, 'text': "And let's go ahead and see what we can do with that.", 'start': 10453.851, 'duration': 4.382}, {'end': 10465.654, 'text': 'So guys in the back put together a nice little piece of code here, which is kind of fun.', 'start': 10460.993, 'duration': 4.661}], 'summary': 'Using code to navigate high-low curves without calculus.', 'duration': 31.449, 'max_score': 10434.205, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE10434205.jpg'}, {'end': 10740.214, 'src': 'embed', 'start': 10665.596, 'weight': 1, 'content': [{'end': 10674.819, 'text': 'and with the sklearn kit, and one of the nice reasons of breaking this down the way we did is i could go over those top pieces.', 'start': 10665.596, 'duration': 9.223}, {'end': 10676.499, 'text': 'those top pieces are everything.', 'start': 10674.819, 'duration': 1.68}, {'end': 10691.264, 'text': "when you start looking at these minimization toolkits in built-in code and so from, we'll just do it's actually docs dot, scipy.org,", 'start': 10676.499, 'duration': 14.765}, {'end': 10695.246, 'text': "and we're looking at the scikit.", 'start': 10691.264, 'duration': 3.982}, {'end': 10698.987, 'text': 'there we go optimize, minimize.', 'start': 10695.246, 'duration': 3.741}, {'end': 10701.608, 'text': 'you can only minimize one value you have.', 'start': 10698.987, 'duration': 2.621}, {'end': 10705.69, 'text': "the function that's going in this function can be very complicated.", 'start': 10701.608, 'duration': 4.082}, {'end': 10707.511, 'text': 'so we used a very simple function up here.', 'start': 10705.69, 'duration': 1.821}, {'end': 10708.611, 'text': 'it could be.', 'start': 10707.511, 'duration': 1.1}, {'end': 10711.753, 'text': "There's all kinds of things that could be on there.", 'start': 10709.992, 'duration': 1.761}, {'end': 10715.215, 'text': "And there's a number of methods to solve this as far as how they shrink down.", 'start': 10711.933, 'duration': 3.282}, {'end': 10718.677, 'text': "And your X naught, there's your start value.", 'start': 10715.975, 'duration': 2.702}, {'end': 10725.66, 'text': "So your function, your start value, there's all kinds of things that come in here that you can look at which we're not going to.", 'start': 10718.957, 'duration': 6.703}, {'end': 10730.043, 'text': 'Optimization automatically creates, constraints, bounds.', 'start': 10726.601, 'duration': 3.442}, {'end': 10736.61, 'text': 'Some of this it does automatically, but the big thing I want to point out here is you need to have a starting point.', 'start': 10730.943, 'duration': 5.667}, {'end': 10740.214, 'text': 'You want to start with something that you already know is mostly the answer.', 'start': 10737.15, 'duration': 3.064}], 'summary': 'Using sklearn kit for optimization, emphasizing the need for a starting point.', 'duration': 74.618, 'max_score': 10665.596, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE10665596.jpg'}], 'start': 9852.06, 'title': 'Calculus and statistics in machine learning', 'summary': 'Emphasizes the importance of understanding calculus and differential equations in machine learning, discusses multivariate calculus in neural networks, and explores error minimization, peak maximization, precision in algorithms, and statistics in data science.', 'chapters': [{'end': 10226.147, 'start': 9852.06, 'title': 'Understanding calculus in machine learning', 'summary': 'Delves into the importance of understanding calculus and differential equations in machine learning, emphasizing the need for data scientists to grasp these concepts to effectively solve math equations, especially in the context of large neural networks.', 'duration': 374.087, 'highlights': ['The importance of understanding calculus and differential equations in machine learning Emphasizes the significance of grasping calculus and differential equations for data scientists to effectively solve math equations, especially in the context of large neural networks.', "The significance of calculus in solving linear expressions for infinitesimally small x's and summing them up Explains the significance of calculus in solving linear expressions for infinitesimally small x's and summing them up, providing a deeper understanding of the concept.", 'The complexity of multivariate calculus and its translation into multivariate integration using double integrals Discusses the complexity of multivariate calculus and its translation into multivariate integration using double integrals, highlighting the intricate nature of the subject matter.']}, {'end': 10349.151, 'start': 10226.307, 'title': 'Understanding multivariate calculus in neural networks', 'summary': 'Discusses the application of multivariate calculus in neural networks, including the concept of multivariate integration, solving mathematically for neural networks, and using calculus for building predictive models and gradient descent to find local and global maxima.', 'duration': 122.844, 'highlights': ['Calculus provides tools to build an accurate predictive model for neural networks. Calculus is used to build an accurate predictive model for neural networks, enabling the understanding of how variables change and affect each other.', "Multivariate calculus explains the change in the target variable in relation to the rate of change in the input variables. Multivariate calculus explains the relationship between the change in the target variable and the rate of change in the input variables, helping in understanding the impact of one variable's change on another.", 'Gradient descent is used to find local and global maxima in neural networks. Gradient descent is crucial for guessing the best answer in neural networks, helping in finding local and global maxima to optimize the models.', 'Understanding the change of the change is a key concept in multivariate calculus. The concept of understanding the change of the change is important in multivariate calculus, aiding in predicting the new numbers based on the change in variables.', 'Solving mathematically for neural networks and reverse propagation is enabled by calculus. Calculus allows for the mathematical solving of neural networks and reverse propagation, facilitating the understanding of complex multivariate integrations and change mechanisms in neural networks.']}, {'end': 10521.817, 'start': 10349.652, 'title': 'Error minimization and peak maximization in data science', 'summary': 'Discusses the process of minimizing error and maximizing value in data science, using a high-low strategy and key parameters like starting point and learning rate, while also highlighting the underlying calculus and differential equations involved.', 'duration': 172.165, 'highlights': ['The process involves minimizing error and maximizing value in data science, using a high-low strategy to iteratively approach the minimum or maximum point, as well as identifying key parameters like the starting point and learning rate.', "The algorithm starts at x equals 3 and the model's starting point is arbitrarily picked at 5, demonstrating the importance of determining the initial position in the high-low strategy.", 'The back end involves calculus and differential equations for calculation, but the heavy lifting is done, relieving the user from performing these complex calculations.', 'The function used for the example involves the gradient of 2 times x plus 5, emphasizing the simplicity of the function for illustration purposes.']}, {'end': 10858.47, 'start': 10522.418, 'title': 'Precision in algorithms and statistics', 'summary': 'Discusses precision in algorithms, emphasizing the importance of precision, step size, max iterations, and practical application in finding local minimum. it also touches on the significance of statistics and terminologies in different domains.', 'duration': 336.052, 'highlights': ['The chapter emphasizes the significance of precision, step size, and max iterations in algorithms, with a practical example of finding a local minimum, typically not exceeding 100 or 200 max iterations. 100 or 200 max iterations', 'The discussion highlights the importance of having a starting point and the impact of precision when dealing with minimization toolkits, emphasizing the need for a known starting point for effective calculation. Need for a known starting point', 'The transcript briefly touches on statistics, emphasizing its role in the collection, organization, analysis, interpretation, and presentation of data, as well as the importance of terminologies in different domains. Role in the collection, organization, analysis, interpretation, and presentation of data']}], 'duration': 1006.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE9852060.jpg', 'highlights': ['Emphasizes the importance of understanding calculus and differential equations in machine learning', 'Discusses the complexity of multivariate calculus and its translation into multivariate integration using double integrals', 'Calculus provides tools to build an accurate predictive model for neural networks', 'Gradient descent is used to find local and global maxima in neural networks', 'The process involves minimizing error and maximizing value in data science, using a high-low strategy to iteratively approach the minimum or maximum point', 'The chapter emphasizes the significance of precision, step size, and max iterations in algorithms']}, {'end': 12452.726, 'segs': [{'end': 10889.908, 'src': 'embed', 'start': 10859.311, 'weight': 4, 'content': [{'end': 10862.332, 'text': 'An interesting project that came up for our city a while back.', 'start': 10859.311, 'duration': 3.021}, {'end': 10869.256, 'text': 'So population, all objects are measurements whose properties are being observed.', 'start': 10863.472, 'duration': 5.784}, {'end': 10871.517, 'text': "So that's your population, all the objects.", 'start': 10869.336, 'duration': 2.181}, {'end': 10876.1, 'text': "It's easy to see it with people because we have our population in large.", 'start': 10872.017, 'duration': 4.083}, {'end': 10881.063, 'text': "But in the case of the sewer fans, we're talking about the fan units.", 'start': 10877.14, 'duration': 3.923}, {'end': 10883.104, 'text': "That's the population of fans that we're working with.", 'start': 10881.083, 'duration': 2.021}, {'end': 10889.908, 'text': 'You have a parameter, a matrix that is used to represent a population or characteristic.', 'start': 10884.865, 'duration': 5.043}], 'summary': 'A project for the city involved studying the population of sewer fans and using matrices to represent the characteristic.', 'duration': 30.597, 'max_score': 10859.311, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE10859311.jpg'}, {'end': 11161.963, 'src': 'embed', 'start': 11135.434, 'weight': 6, 'content': [{'end': 11142.276, 'text': "What does it look like? With inferential statistics, we're going to take that from the small population to a large population.", 'start': 11135.434, 'duration': 6.842}, {'end': 11148.358, 'text': "So if you're working with a drug company, you might look at the data and say, these people were helped by this drug.", 'start': 11142.896, 'duration': 5.462}, {'end': 11157.781, 'text': 'They did 80% better as far as their health or 80% better survival rate than the people who did not have the drug.', 'start': 11148.958, 'duration': 8.823}, {'end': 11161.963, 'text': 'So we can infer that that drug will work in the greater populace and will help people.', 'start': 11157.941, 'duration': 4.022}], 'summary': 'Using inferential statistics, a drug showed an 80% better health improvement and survival rate, indicating its potential effectiveness for a larger population.', 'duration': 26.529, 'max_score': 11135.434, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE11135434.jpg'}, {'end': 11233.744, 'src': 'embed', 'start': 11203.913, 'weight': 1, 'content': [{'end': 11209.498, 'text': "And anything that's just a single number is usually your central tendencies, measure of central tendencies.", 'start': 11203.913, 'duration': 5.585}, {'end': 11211.599, 'text': 'So we talk about the mean.', 'start': 11210.479, 'duration': 1.12}, {'end': 11214.362, 'text': 'It is the average of the set of values considered.', 'start': 11211.86, 'duration': 2.502}, {'end': 11222.729, 'text': "What is the average outcome of whatever's going on? And then your median separates the higher half and the lower half of data.", 'start': 11214.742, 'duration': 7.987}, {'end': 11227.901, 'text': "So where's the center point of all your different data points?", 'start': 11224.639, 'duration': 3.262}, {'end': 11233.744, 'text': 'So your mean might have a couple really big numbers that skew it,', 'start': 11228.541, 'duration': 5.203}], 'summary': 'Mean and median are measures of central tendencies in data sets.', 'duration': 29.831, 'max_score': 11203.913, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE11203913.jpg'}, {'end': 11463.305, 'src': 'embed', 'start': 11436.213, 'weight': 0, 'content': [{'end': 11442.596, 'text': 'Marks of a student out of 100, we have here from 50 to 90.', 'start': 11436.213, 'duration': 6.383}, {'end': 11449.899, 'text': 'So the range, maximum marks, minimum marks, we have 90 to 45, and the spread of that is 45, 90 minus 45.', 'start': 11442.596, 'duration': 7.303}, {'end': 11452.26, 'text': 'And then we have the interquartile range.', 'start': 11449.899, 'duration': 2.361}, {'end': 11456.082, 'text': 'Using the same marks over there, you can see here where the median is.', 'start': 11452.7, 'duration': 3.382}, {'end': 11463.305, 'text': "And then there's the first quarter, the second quarter, and the third quarter, based on splitting it apart by those values.", 'start': 11456.662, 'duration': 6.643}], 'summary': 'Student marks range from 50 to 90, with an interquartile range and spread of 45, 90 minus 45.', 'duration': 27.092, 'max_score': 11436.213, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE11436213.jpg'}, {'end': 11826.641, 'src': 'embed', 'start': 11800.216, 'weight': 3, 'content': [{'end': 11804.078, 'text': "And if we do this, you can see we have that there's seven setups.", 'start': 11800.216, 'duration': 3.862}, {'end': 11810.403, 'text': "Here's our mean, our standard deviation, which we didn't compute yet, which would just be a dot STD.", 'start': 11804.539, 'duration': 5.864}, {'end': 11814.906, 'text': 'And you got to be a little careful because when it computes it, it looks for axes and things like that.', 'start': 11810.423, 'duration': 4.483}, {'end': 11818.121, 'text': "We have our minimum value and here's our quartiles.", 'start': 11815.808, 'duration': 2.313}, {'end': 11822.179, 'text': 'Our maximum value and then of course the name salary.', 'start': 11819.797, 'duration': 2.382}, {'end': 11824.8, 'text': 'So these are the basic statistics.', 'start': 11822.999, 'duration': 1.801}, {'end': 11826.641, 'text': 'You can pull them up and just describe.', 'start': 11824.82, 'duration': 1.821}], 'summary': "The data has seven setups, with basic statistics like mean, standard deviation, minimum, maximum, and quartiles available for the 'salary' variable.", 'duration': 26.425, 'max_score': 11800.216, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE11800216.jpg'}], 'start': 10859.311, 'title': 'Sampling methods and statistical analysis', 'summary': 'Covers population sampling methods, stratified sampling, descriptive statistics, income stats analysis with pandas, inferential statistics, and probability with an emphasis on unbiased selection, representative sampling, and key statistical measures. it also explores the application of statistics in analyzing income distribution and making predictions, and emphasizes the significance of 0.05 in hypothesis testing.', 'chapters': [{'end': 10976.478, 'start': 10859.311, 'title': 'Population sampling methods', 'summary': 'Discusses population, parameters, samples, variables, and types of sampling methods, emphasizing probabilistic and non-probabilistic approaches, and the importance of avoiding biased selection.', 'duration': 117.167, 'highlights': ['The population represents all objects being observed, such as people in a city or fan units in a sewer system.', 'Sampling is essential as studying the entire population may not be feasible, leading to the selection of a subset to draw conclusions and test hypotheses.', 'The transcript delves into various types of sampling methods, including probabilistic approaches (random, systematic, stratified) and non-probabilistic approaches (convenience, quota, snowball), emphasizing the potential biases associated with the latter.', 'Probabilistic sampling involves random and systematic methods to select samples from each group or category, ensuring a fair representation of the population and minimizing biases.', 'Non-probabilistic sampling, driven by subjective judgment rather than randomness, can introduce significant biases, thereby requiring careful consideration and scrutiny in research.']}, {'end': 11518.481, 'start': 10976.498, 'title': 'Stratified sampling and descriptive statistics', 'summary': 'Introduces the concept of stratified sampling, emphasizing the importance of selecting representative samples from different groups or categories, and then discusses descriptive and inferential statistics, highlighting the key differences and measures used to analyze data.', 'duration': 541.983, 'highlights': ['Stratified sampling involves selecting approximately equal size samples from different groups or categories, ensuring representation of various cultures and their impact in a specific area.', 'Descriptive statistics are used to describe the basic features of data and form the basis of quantitative analysis, including measures of central tendencies such as mean, median, and mode, as well as measures of spread like range, interquartile range, variance, and standard deviation.', 'Inferential statistics involve predicting how a specific observation will affect the greater population, as exemplified in the context of a drug company analyzing the efficacy of a drug on a small population to infer its impact on a larger populace.', 'The chapter also highlights the significance of understanding central tendencies, including the mean, median, and mode, and provides practical examples such as calculating the average marks of students and identifying the most frequent appearing value in a test.', 'The discussion further delves into measures of spread, including range, interquartile range, variance, and standard deviation, emphasizing their role in understanding the variation and dispersion of data.', 'The detailed explanation on calculating variance and standard deviation, along with practical examples, enhances the understanding of these crucial measures for analyzing data.']}, {'end': 11863.501, 'start': 11518.561, 'title': 'Analyzing income stats with pandas', 'summary': 'Covers using pandas to analyze a sample dataset and demonstrates the calculation of mean, median, mode, range, and other basic statistics, revealing insights into income distribution.', 'duration': 344.94, 'highlights': ['The average income in the sample dataset is $71,000, with the median being $54,000 and the mode at $50,000. Key points: Average income is $71,000, median is $54,000, and mode is $50,000.', 'The range of income in the dataset is $149,000, with the maximum salary being $189,000 and the minimum at an unspecified value. Key points: Range of income is $149,000, maximum salary is $189,000.', 'The chapter also explains the standard deviation and quartiles, providing a comprehensive understanding of the income distribution in the dataset. Key points: Explanation of standard deviation and quartiles for comprehensive income distribution understanding.']}, {'end': 12256.472, 'start': 11863.521, 'title': 'Inferential statistics and salary distribution', 'summary': 'Discusses plotting salary distribution using a histogram, understanding the distribution pattern, and the application of inferential statistics in making predictions and inferences from data, with a focus on point estimation and hypothesis testing.', 'duration': 392.951, 'highlights': ['The chapter demonstrates plotting the salary distribution using a histogram, highlighting the mean and median values for visual representation.', 'It emphasizes the importance of inferential statistics in making predictions and inferences from data, using the example of movie ratings to explain probability and point estimation.', 'It explains the applications of inferential statistics, including hypothesis testing and confidence intervals, along with probability concepts such as the binomial theorem and normal distribution.']}, {'end': 12452.726, 'start': 12256.633, 'title': 'Probability and hypothesis testing', 'summary': 'Discusses the probability of a person getting chosen for a task, illustrating the decreasing probability of rob getting the cleaning job over 12 days, and then delves into important terminologies in hypothesis testing such as null hypothesis, alternative hypothesis, p-value, and t-value, emphasizing the significance of 0.05 in statistics.', 'duration': 196.093, 'highlights': ["Rob's decreasing probability of getting the cleaning job over 12 days indicates cheating, with the probability dropping below 0.05 by day 12. The probability of Rob not doing work on day one is 3 out of 4, with a 0.75 chance. By day 12, the probability drops to 0.032, indicating a high likelihood of cheating.", 'Explanation of important terminologies in hypothesis testing including null hypothesis, alternative hypothesis, p-value, and t-value, and their significance in data science and statistics. The null hypothesis states no relationship between phenomena, while the alternative hypothesis presents a new theory. The p-value measures the probability of finding observed results under the null hypothesis, and the t-value indicates evidence against the null hypothesis.', "Illustration of using hypothesis testing to compare the effectiveness of a new drug in lowering blood pressure compared to an existing drug, emphasizing the significance of the null value and 0.05 in determining correlation. The discussion involves comparing a new drug's effectiveness in lowering blood pressure to the existing drug, highlighting that the null value isn't the absence of a drug but rather the new drug not being better than the existing one. The significance of 0.05 is stressed as a high correlation threshold."]}], 'duration': 1593.415, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE10859311.jpg', 'highlights': ['Probabilistic sampling ensures fair representation and minimizes biases.', 'Stratified sampling ensures representation of various cultures in a specific area.', 'Descriptive statistics describe data features and include measures of central tendencies and spread.', 'Inferential statistics predict impact on a larger population and involve hypothesis testing.', 'Understanding income distribution through average, median, mode, range, and standard deviation.', 'Importance of inferential statistics in making predictions and inferences from data.', 'Explanation of important terminologies in hypothesis testing and their significance in statistics.']}, {'end': 13292.671, 'segs': [{'end': 12576.796, 'src': 'embed', 'start': 12542.641, 'weight': 1, 'content': [{'end': 12544.462, 'text': "in this case it's a normal distribution.", 'start': 12542.641, 'duration': 1.821}, {'end': 12546.543, 'text': 'so you have the nice bell curve equal on both sides.', 'start': 12544.462, 'duration': 2.081}, {'end': 12547.584, 'text': "it's not asymmetrical.", 'start': 12546.543, 'duration': 1.041}, {'end': 12554.307, 'text': 'And 95% of all the values lie within a very small range and then you have your outliers, the 2.5% going each way.', 'start': 12548.064, 'duration': 6.243}, {'end': 12558.356, 'text': 'So we touched upon hypothesis.', 'start': 12556.434, 'duration': 1.922}, {'end': 12561.139, 'text': "We're going to move into probability.", 'start': 12559.017, 'duration': 2.122}, {'end': 12562.881, 'text': 'So you have your hypothesis.', 'start': 12561.76, 'duration': 1.121}, {'end': 12566.465, 'text': "Once you've generated your hypothesis, we want to know the probability of something occurring.", 'start': 12562.981, 'duration': 3.484}, {'end': 12570.129, 'text': 'Probability is a measure of the likelihood of an event to occur.', 'start': 12566.705, 'duration': 3.424}, {'end': 12576.796, 'text': 'Any event can be predicted with total certainty and can only be predicted as a likelihood of its occurrence.', 'start': 12570.589, 'duration': 6.207}], 'summary': 'The normal distribution has a bell curve with 95% of values falling within a small range, and 2.5% being outliers. probability measures the likelihood of an event occurring.', 'duration': 34.155, 'max_score': 12542.641, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE12542641.jpg'}, {'end': 12925.714, 'src': 'embed', 'start': 12859.76, 'weight': 0, 'content': [{'end': 12865.666, 'text': '0878 And of course, we can go on to the probability that Barcelona will win three matches, the .', 'start': 12859.76, 'duration': 5.906}, {'end': 12869.29, 'text': '26, and of course, four matches, and so on.', 'start': 12865.666, 'duration': 3.624}, {'end': 12876.273, 'text': "And it's always nice to take this information and let's find the cumulative discrete probabilities for each of the outcomes.", 'start': 12869.77, 'duration': 6.503}, {'end': 12883.236, 'text': 'Where Barcelona has won three or more matches, x equals 3, x equals 4, x equals 5.', 'start': 12876.773, 'duration': 6.463}, {'end': 12886.057, 'text': 'And we end up with the p equals 0.264 plus 0.395 plus 0.237, which equals 0.89.', 'start': 12883.236, 'duration': 2.821}, {'end': 12896.877, 'text': 'In reality, the probability of Barcelona winning the series is much higher than 0.75.', 'start': 12886.057, 'duration': 10.82}, {'end': 12907.163, 'text': "And it's always nice to put out a nice graph so you can actually see the number of wins to the probability and how that pans out with our binomial case.", 'start': 12896.877, 'duration': 10.286}, {'end': 12911.365, 'text': 'Continuing in our important terminology, location.', 'start': 12908.023, 'duration': 3.342}, {'end': 12915.948, 'text': 'The location of the center of the graph depends on the mean value.', 'start': 12911.566, 'duration': 4.382}, {'end': 12918.389, 'text': 'And this is some very important things.', 'start': 12916.408, 'duration': 1.981}, {'end': 12925.714, 'text': 'So much of the data we look at, and when you start looking at probabilities, almost always has a normalized look, like the graph in the middle.', 'start': 12918.81, 'duration': 6.904}], 'summary': 'Barcelona has a cumulative probability of 0.89 to win 3 or more matches, higher than 0.75.', 'duration': 65.954, 'max_score': 12859.76, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE12859760.jpg'}, {'end': 12984.6, 'src': 'embed', 'start': 12956.888, 'weight': 3, 'content': [{'end': 12961.33, 'text': 'And if the standard deviation is small, then most of your data is going to hit right there in the middle.', 'start': 12956.888, 'duration': 4.442}, {'end': 12962.391, 'text': "You're going to have a nice peak.", 'start': 12961.35, 'duration': 1.041}, {'end': 12969.454, 'text': 'And so being aware of this, you might have a probability that fits certain data, but it has a lot of outliers.', 'start': 12963.411, 'duration': 6.043}, {'end': 12975.398, 'text': "So if you have a really high standard deviation, if you're doing stock market analysis,", 'start': 12969.755, 'duration': 5.643}, {'end': 12979.199, 'text': 'This means your predictions are probably not going to make you much money.', 'start': 12976.418, 'duration': 2.781}, {'end': 12984.6, 'text': 'Where if you have a very small deviation, you might be right on target and set to become a millionaire.', 'start': 12980.039, 'duration': 4.561}], 'summary': 'Small standard deviation leads to accurate predictions with potential for high gains, while high deviation may result in erroneous forecasts and limited profits.', 'duration': 27.712, 'max_score': 12956.888, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE12956888.jpg'}, {'end': 13208.911, 'src': 'embed', 'start': 13179.597, 'weight': 5, 'content': [{'end': 13184.96, 'text': "but if you can find a place where it overlaps, where they're studying the same thing together,", 'start': 13179.597, 'duration': 5.363}, {'end': 13188.942, 'text': 'you can then compute the changes that you need to make in one study to make them equal.', 'start': 13184.96, 'duration': 3.982}, {'end': 13195.965, 'text': 'And this is also true if you have a study of one group and you want to find out more about it.', 'start': 13189.982, 'duration': 5.983}, {'end': 13208.911, 'text': "So this formula is very powerful and it really has to do with the data collection part of the math and data science and understanding where your data is coming from and how you're going to combine different studies in different groups.", 'start': 13196.345, 'duration': 12.566}], 'summary': 'Find overlaps in studies to compute changes and combine data for powerful insights.', 'duration': 29.314, 'max_score': 13179.597, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE13179597.jpg'}, {'end': 13269.993, 'src': 'embed', 'start': 13245.272, 'weight': 4, 'content': [{'end': 13250.554, 'text': 'And finally, 5% of the people continued smoke even when they had lung disease.', 'start': 13245.272, 'duration': 5.282}, {'end': 13256.097, 'text': 'Not the brightest choice, but it is an addiction, so it can be really difficult to kick.', 'start': 13251.274, 'duration': 4.823}, {'end': 13262.559, 'text': 'And so we can look at the probability of A, prior probability of 10% people having lung disease.', 'start': 13256.497, 'duration': 6.062}, {'end': 13269.993, 'text': 'And then probability B, probability that a patient smokes is 15%.', 'start': 13263.5, 'duration': 6.493}], 'summary': '5% of patients with lung disease continued smoking despite the risks.', 'duration': 24.721, 'max_score': 13245.272, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE13245272.jpg'}], 'start': 12453.632, 'title': 'Statistical hypothesis testing and probability', 'summary': 'Covers hypothesis testing, p-value, t-value, confidence intervals, probability, random variables, and binomial distribution in statistics, including examples and calculations, showing the likelihood of events and success rates.', 'chapters': [{'end': 12911.365, 'start': 12453.632, 'title': 'Hypothesis testing and probability in statistics', 'summary': 'Covers hypothesis testing, p-value, t-value, confidence intervals, probability, random variables, and binomial distribution in statistics, including examples and calculations, showing the likelihood of events and success rates.', 'duration': 457.733, 'highlights': ['The new drug does significantly lower the blood pressure more than the existing drug. The alternative hypothesis states that the new drug significantly lowers blood pressure more than the existing drug.', 'The p-value results from medical trials showing positive results which will reject the null hypothesis. The p-value from medical trials showing positive results leads to the rejection of the null hypothesis.', 'A confidence interval is a range of values where we are sure our true observations lie, e.g., around 95% bought around 200 to 300 cans of food, giving a confidence interval of 200-300 for 95% of the observations. The example illustrates a confidence interval, where 95% of people bought 200-300 cans of food, showing a range of values for the true observations.', 'Probability is a measure of the likelihood of an event to occur and can be applied to various scenarios like score prediction, weather prediction, stock prediction, and random phenomena. Probability measures the likelihood of an event occurring and can be applied to scenarios such as score prediction, weather prediction, and stock prediction.', 'The example of rolling two dice illustrates the concept of a random variable and the calculation of probabilities for different outcomes, such as the chance of rolling a total of five. The example of rolling two dice demonstrates the concept of a random variable and the calculation of probabilities for different outcomes, such as the chance of rolling a total of five.', 'Binomial distribution calculates the probability of success or failure in an experiment or trial, demonstrated through the example of the probabilities of Barcelona winning a series of football matches. Binomial distribution calculates the probability of success or failure and is demonstrated through the example of the probabilities of Barcelona winning a series of football matches.']}, {'end': 13292.671, 'start': 12911.566, 'title': 'Understanding probability and skewed data', 'summary': "Discusses the importance of mean, standard deviation, z-score, and conditional probability in understanding skewed data and making predictions, with examples including the central limit theorem and bayes' theorem.", 'duration': 381.105, 'highlights': ['Understanding the Z-score and its significance in predicting outcomes based on the distance from the mean, with 68% and 95% of results found within one and two standard deviations respectively. The Z-score measures how far from the mean a data point is, with around 68% and 95% of the results found between one and two standard deviations.', 'Explaining the Central Limit Theorem and its role in ensuring normal distribution of sample means from large random samples, emphasizing the importance of identifying skewed values. The Central Limit Theorem ensures that the distribution of sample means will be approximately normally distributed and not skewed, highlighting the significance of identifying skewed values.', "Illustrating the application of Bayes' Theorem in combining and comparing different studies or groups, particularly in understanding overlapping data and making necessary adjustments. Bayes' Theorem is powerful in combining and comparing different studies or groups, especially in understanding overlapping data and making necessary adjustments.", 'Calculating the probability of a person having lung disease given they smoke, using prior probabilities and conditional probability calculations. The chapter provides an example of calculating the probability of a person having lung disease given they smoke, using prior probabilities and conditional probability calculations.']}], 'duration': 839.039, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE12453632.jpg', 'highlights': ['The p-value from medical trials showing positive results leads to the rejection of the null hypothesis.', 'The new drug does significantly lower the blood pressure more than the existing drug.', 'Probability measures the likelihood of an event occurring and can be applied to scenarios such as score prediction, weather prediction, and stock prediction.', 'The example illustrates a confidence interval, where 95% of people bought 200-300 cans of food, showing a range of values for the true observations.', 'The example of rolling two dice demonstrates the concept of a random variable and the calculation of probabilities for different outcomes, such as the chance of rolling a total of five.', 'Binomial distribution calculates the probability of success or failure and is demonstrated through the example of the probabilities of Barcelona winning a series of football matches.', 'The Z-score measures how far from the mean a data point is, with around 68% and 95% of the results found between one and two standard deviations.', 'The Central Limit Theorem ensures that the distribution of sample means will be approximately normally distributed and not skewed, highlighting the significance of identifying skewed values.', "Bayes' Theorem is powerful in combining and comparing different studies or groups, especially in understanding overlapping data and making necessary adjustments.", 'The chapter provides an example of calculating the probability of a person having lung disease given they smoke, using prior probabilities and conditional probability calculations.']}, {'end': 14367.333, 'segs': [{'end': 13487.55, 'src': 'embed', 'start': 13455.862, 'weight': 5, 'content': [{'end': 13457.523, 'text': "We'll go ahead and do a little iteration.", 'start': 13455.862, 'duration': 1.661}, {'end': 13461.306, 'text': "We're going to do kind of the dice one.", 'start': 13457.804, 'duration': 3.502}, {'end': 13463.063, 'text': 'Remember 1, 2, 3, 4, 5, 6.', 'start': 13461.586, 'duration': 1.477}, {'end': 13467.291, 'text': "And so we're going to bring in an iteration tool and import product as product.", 'start': 13463.068, 'duration': 4.223}, {'end': 13471.608, 'text': "And I'll show you what that means in just a second.", 'start': 13469.267, 'duration': 2.341}, {'end': 13473.208, 'text': 'So we have our two dice.', 'start': 13472.128, 'duration': 1.08}, {'end': 13475.309, 'text': 'We have dice A.', 'start': 13473.248, 'duration': 2.061}, {'end': 13476.889, 'text': "And it's going to be a set of values.", 'start': 13475.309, 'duration': 1.58}, {'end': 13479.45, 'text': 'They can only have one value for each one.', 'start': 13477.689, 'duration': 1.761}, {'end': 13480.57, 'text': "That's why they put it in a set.", 'start': 13479.47, 'duration': 1.1}, {'end': 13484.171, 'text': 'And if you remember from range, it is up to 7.', 'start': 13481.21, 'duration': 2.961}, {'end': 13487.55, 'text': 'So this is going to be 1, 2, 3, 4, 5, 6.', 'start': 13484.171, 'duration': 3.379}], 'summary': 'Using iteration to simulate dice roll with values 1 to 6.', 'duration': 31.688, 'max_score': 13455.862, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE13455862.jpg'}, {'end': 13551.485, 'src': 'embed', 'start': 13526.65, 'weight': 8, 'content': [{'end': 13532.713, 'text': 'And you remember we had a slide on this earlier, where we talked about all the different outcomes of a dice.', 'start': 13526.65, 'duration': 6.063}, {'end': 13535.474, 'text': 'We can play around with this a little bit.', 'start': 13533.573, 'duration': 1.901}, {'end': 13543.035, 'text': 'We can do end dice equals 2, dice faces 1, 2, 3, 4, 5, 6.', 'start': 13535.995, 'duration': 7.04}, {'end': 13550.604, 'text': 'Another way of doing what we did before and then we can create an event space where we have a set which is the product of the dice faces, repeat,', 'start': 13543.039, 'duration': 7.565}, {'end': 13551.485, 'text': 'equals end dice.', 'start': 13550.604, 'duration': 0.881}], 'summary': 'Discussing outcomes of a dice with 6 faces and creating event space.', 'duration': 24.835, 'max_score': 13526.65, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE13526650.jpg'}, {'end': 13807.698, 'src': 'embed', 'start': 13772.39, 'weight': 4, 'content': [{'end': 13776.392, 'text': 'And we can look at the length of the event space.', 'start': 13772.39, 'duration': 4.002}, {'end': 13785.074, 'text': 'And we have over 7, 776 choices.', 'start': 13784.134, 'duration': 0.94}, {'end': 13785.894, 'text': "That's a lot of choices.", 'start': 13785.134, 'duration': 0.76}, {'end': 13799.411, 'text': 'And if we want to ask the question like we did above, where the sum is a multiple of 5 but not a multiple of 3,', 'start': 13790.402, 'duration': 9.009}, {'end': 13801.733, 'text': 'we can go through all of these different options.', 'start': 13799.411, 'duration': 2.322}, {'end': 13807.698, 'text': 'And then you can see here, D1, D2, D3, D4, D5 equals the outcome.', 'start': 13802.073, 'duration': 5.625}], 'summary': 'Event space has over 7,776 choices for multiple of 5 but not 3.', 'duration': 35.308, 'max_score': 13772.39, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE13772390.jpg'}, {'end': 14011.748, 'src': 'embed', 'start': 13981.564, 'weight': 0, 'content': [{'end': 13986.949, 'text': "You know, cancer, a big one, you don't want to have a false positive, I mean a false negative.", 'start': 13981.564, 'duration': 5.385}, {'end': 13990.813, 'text': "In other words, you don't want to have it tell you that you don't have cancer when you do.", 'start': 13987.149, 'duration': 3.664}, {'end': 13995.397, 'text': "So that would be something you'd really be looking for in this particular domain.", 'start': 13991.373, 'duration': 4.024}, {'end': 13996.699, 'text': "You don't want a false negative.", 'start': 13995.498, 'duration': 1.201}, {'end': 14004.458, 'text': "And this is again, you know, you've created a model, you have hundreds of people or thousands of pieces of data that come in.", 'start': 13997.868, 'duration': 6.59}, {'end': 14011.748, 'text': "There's a real famous case study where they have the imagery and all the measurements they take and there's about 36 different measurements they take.", 'start': 14004.478, 'duration': 7.27}], 'summary': 'In cancer detection, avoiding false negatives is crucial in analyzing thousands of data pieces and 36 measurements.', 'duration': 30.184, 'max_score': 13981.564, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE13981564.jpg'}, {'end': 14372.437, 'src': 'embed', 'start': 14344.604, 'weight': 2, 'content': [{'end': 14351.126, 'text': "Scale it means we're putting it between a value of minus one and one, or someplace in the middle ground there.", 'start': 14344.604, 'duration': 6.522}, {'end': 14363.49, 'text': "This way, if you have any huge, you don't have this huge setup, if we go back up to here where salary, the salary is 20, 000 versus age 35.", 'start': 14351.746, 'duration': 11.744}, {'end': 14367.333, 'text': "Well, there's a good chance, with a lot of the back-end math, that 20,", 'start': 14363.49, 'duration': 3.843}, {'end': 14372.437, 'text': '000 will skew the results and the estimated salary will have a higher impact than the age,', 'start': 14367.333, 'duration': 5.104}], 'summary': 'Using a scale of -1 to 1 helps avoid skewed results in estimation models.', 'duration': 27.833, 'max_score': 14344.604, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE14344604.jpg'}], 'start': 13293.786, 'title': 'Python set operations, probability, and model accuracy', 'summary': 'Covers set operations and iterations in python, probability calculations for rolling dice, including a probability of .3333 for specific outcomes, and the importance of model accuracy, precision, and recall in predictive analysis, illustrated through a naive bayes classifier implementation.', 'chapters': [{'end': 13600.549, 'start': 13293.786, 'title': 'Python set operations and iteration', 'summary': 'Covers set operations, including creating sets with unique values, converting lists to sets, and using logic functions, along with iterations using dice values and event spaces.', 'duration': 306.763, 'highlights': ['Creating sets with unique values, such as {4, 7}, and using dictionaries for key-value setups.', 'Converting lists to sets to remove duplicate values, for example, converting [1, 2, 3, 4, 4] to {1, 2, 3, 4}.', 'Utilizing logic functions with sets, like checking if a specific number is in the set, e.g., 3 in the set (True) and 6 in the set (False).', 'Performing iterations with dice values by creating a product of possible outcomes, showcasing all combinations of dice rolls and creating event spaces for different dice faces and repeat values.']}, {'end': 13996.699, 'start': 13601.09, 'title': 'Probability and confusion matrix in statistics', 'summary': 'Discusses the calculation of probabilities for rolling dice, with 36 total options, 12 adding up to a multiple of 3, resulting in a probability of .3333, and then explores the concept of a confusion matrix in machine learning with a focus on minimizing false positive predictions.', 'duration': 395.609, 'highlights': ['The favorable outcome of rolling dice that adds up to a multiple of 3 is 12 out of 36 total options, resulting in a probability of .3333. The chapter calculates the probability of favorable outcomes for rolling dice, with 12 out of 36 total options resulting in a probability of .3333 for a multiple of 3.', 'The confusion matrix is used to describe the performance of a classification model, particularly in minimizing false positive predictions which is crucial in medical situations. The concept of a confusion matrix in machine learning is explained, emphasizing the importance of minimizing false positive predictions in critical scenarios such as medical tests.', 'The comparison of actual medical reports with machine learning predictions in cancer diagnosis highlights the importance of avoiding false negative predictions. The chapter emphasizes the significance of avoiding false negative predictions in critical domains like cancer diagnosis when comparing actual medical reports with machine learning predictions.']}, {'end': 14367.333, 'start': 13997.868, 'title': 'Model accuracy and naive bayes classifier', 'summary': 'Discusses the importance of model accuracy, precision, and recall in predictive analysis, using a case study with 36 measurements, and demonstrates the implementation of a naive bayes classifier in the context of a social network ads dataset for prediction analysis.', 'duration': 369.465, 'highlights': ['The chapter highlights the importance of model accuracy, precision, and recall in predictive analysis, using a case study with 36 different measurements. The chapter emphasizes the significance of model accuracy, precision, and recall in predictive analysis, particularly in scenarios such as cancer diagnosis or virus testing, and presents a case study involving 36 different measurements.', 'The demonstration of implementing a Naive Bayes classifier in the context of a social network ads dataset for prediction analysis is a key highlight. The chapter demonstrates the implementation of a Naive Bayes classifier using a social network ads dataset, showcasing the process of importing, visualizing, pre-processing, and training/testing the data for prediction analysis.', 'The relevance of feature scaling in pre-processing the data for predictive analysis is mentioned. The importance of feature scaling, which involves putting data between a certain range to prevent data imbalances, is highlighted in the context of pre-processing data for predictive analysis.']}], 'duration': 1073.547, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE13293786.jpg', 'highlights': ['Utilizing logic functions with sets, like checking if a specific number is in the set, e.g., 3 in the set (True) and 6 in the set (False).', 'Converting lists to sets to remove duplicate values, for example, converting [1, 2, 3, 4, 4] to {1, 2, 3, 4}.', 'Creating sets with unique values, such as {4, 7}, and using dictionaries for key-value setups.', 'Performing iterations with dice values by creating a product of possible outcomes, showcasing all combinations of dice rolls and creating event spaces for different dice faces and repeat values.', 'The favorable outcome of rolling dice that adds up to a multiple of 3 is 12 out of 36 total options, resulting in a probability of .3333.', 'The confusion matrix is used to describe the performance of a classification model, particularly in minimizing false positive predictions which is crucial in medical situations.', 'The comparison of actual medical reports with machine learning predictions in cancer diagnosis highlights the importance of avoiding false negative predictions.', 'The chapter emphasizes the significance of model accuracy, precision, and recall in predictive analysis, particularly in scenarios such as cancer diagnosis or virus testing, and presents a case study involving 36 different measurements.', 'The demonstration of implementing a Naive Bayes classifier using a social network ads dataset, showcasing the process of importing, visualizing, pre-processing, and training/testing the data for prediction analysis.', 'The importance of feature scaling, which involves putting data between a certain range to prevent data imbalances, is highlighted in the context of pre-processing data for predictive analysis.']}, {'end': 17575.79, 'segs': [{'end': 14672.017, 'src': 'embed', 'start': 14643.033, 'weight': 0, 'content': [{'end': 14645.535, 'text': 'And then we have those who did make a purchase, the green.', 'start': 14643.033, 'duration': 2.502}, {'end': 14651.02, 'text': 'And you can see that some of the green dots fall into the red area and some of the red dots fall into the green.', 'start': 14645.815, 'duration': 5.205}, {'end': 14653.742, 'text': "So even our training set isn't going to be 100%.", 'start': 14651.7, 'duration': 2.042}, {'end': 14654.242, 'text': "We couldn't do that.", 'start': 14653.742, 'duration': 0.5}, {'end': 14659.444, 'text': "And so we're looking at our different data coming down.", 'start': 14656.86, 'duration': 2.584}, {'end': 14664.552, 'text': 'We can kind of arrange our X1, X2 so we have a nice plot going on.', 'start': 14660.726, 'duration': 3.826}, {'end': 14666.956, 'text': "And then we're going to create the contour.", 'start': 14664.572, 'duration': 2.384}, {'end': 14672.017, 'text': "That's that nice line that's drawn down the middle on here with the red green.", 'start': 14668.695, 'duration': 3.322}], 'summary': 'Data analysis showed overlap between red and green purchases, indicating imperfect training set.', 'duration': 28.984, 'max_score': 14643.033, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE14643033.jpg'}, {'end': 14747.043, 'src': 'embed', 'start': 14715.684, 'weight': 3, 'content': [{'end': 14717.445, 'text': 'And just a quick note, this is kind of funny.', 'start': 14715.684, 'duration': 1.761}, {'end': 14725.088, 'text': 'You can see up here where it says X set, Y set equals X train, Y train, which seems kind of a little weird to do.', 'start': 14717.865, 'duration': 7.223}, {'end': 14729.35, 'text': 'This is because this is probably originally a definition.', 'start': 14726.508, 'duration': 2.842}, {'end': 14735.272, 'text': "So it's its own module that could be called over and over again, which is really a good way to do it,", 'start': 14730.01, 'duration': 5.262}, {'end': 14741.014, 'text': "because the next thing we're going to want to do is do the exact same thing, but we're going to visualize the test set results.", 'start': 14735.272, 'duration': 5.742}, {'end': 14747.043, 'text': 'That way we can see what happened with our test group, our 25%.', 'start': 14741.955, 'duration': 5.088}], 'summary': 'Module allows repetitive calling, visualizing test set results for 25% test group.', 'duration': 31.359, 'max_score': 14715.684, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE14715684.jpg'}, {'end': 15153.525, 'src': 'embed', 'start': 15123.308, 'weight': 5, 'content': [{'end': 15128.211, 'text': 'and SimplyLearn will try to get in contact with you and supply you with that file, so you can try this coding yourself.', 'start': 15123.308, 'duration': 4.903}, {'end': 15141.901, 'text': "So we're going to add this code in here and we're going to see that I have companies equals pd.reader underscore csv and I've changed this path to match my computer c colon slash simplylearn slash 1000 underscore companies dot csv.", 'start': 15128.591, 'duration': 13.31}, {'end': 15147.043, 'text': "And then below there, we're going to set the x equals to companies under the i location.", 'start': 15142.301, 'duration': 4.742}, {'end': 15153.525, 'text': 'And because companies is a PD data set, I can use this nice notation that says take every row.', 'start': 15147.163, 'duration': 6.362}], 'summary': 'Simplylearn will provide the file for coding practice, using pd.reader for 1000 companies data.', 'duration': 30.217, 'max_score': 15123.308, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE15123308.jpg'}, {'end': 15413.641, 'src': 'embed', 'start': 15384.701, 'weight': 9, 'content': [{'end': 15388.063, 'text': 'that shows that this is the highest corresponding data.', 'start': 15384.701, 'duration': 3.362}, {'end': 15393.246, 'text': "that's exactly the same and as it becomes lighter, there's less connections between the data.", 'start': 15388.063, 'duration': 5.183}, {'end': 15394.947, 'text': 'so we can see with profit.', 'start': 15393.246, 'duration': 1.701}, {'end': 15396.748, 'text': 'obviously profit is the same as profit.', 'start': 15394.947, 'duration': 1.801}, {'end': 15402.152, 'text': 'And next, it has a very high correlation with R&D spending, which we looked at earlier,', 'start': 15397.268, 'duration': 4.884}, {'end': 15408.577, 'text': 'and it has a slightly less connection to marketing spending and even less to how much money we put into the administration.', 'start': 15402.152, 'duration': 6.425}, {'end': 15413.641, 'text': "So, now that we have a nice look at the data, let's go ahead and dig in and create some actual,", 'start': 15408.737, 'duration': 4.904}], 'summary': 'High correlation between profit and r&d spending, with less connection to marketing and administration.', 'duration': 28.94, 'max_score': 15384.701, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE15384701.jpg'}, {'end': 15469.078, 'src': 'embed', 'start': 15439.175, 'weight': 10, 'content': [{'end': 15441.717, 'text': "And let's go ahead and paste this code into our Jupyter notebook.", 'start': 15439.175, 'duration': 2.542}, {'end': 15450.082, 'text': "And what we're bringing in is we're going to bring in the sklearn preprocessing where we're going to import the label encoder and the one hot encoder.", 'start': 15441.937, 'duration': 8.145}, {'end': 15457.328, 'text': "To use the label encoder we're going to create a variable called label encoder and set it equal to capital L label capital E encoder.", 'start': 15450.242, 'duration': 7.086}, {'end': 15462.272, 'text': 'This creates a class that we can reuse for transferring the labels back and forth.', 'start': 15457.488, 'duration': 4.784}, {'end': 15462.913, 'text': 'Now about.', 'start': 15462.572, 'duration': 0.341}, {'end': 15464.954, 'text': 'now you should ask what labels are we talking about?', 'start': 15462.913, 'duration': 2.041}, {'end': 15469.078, 'text': "Let's go take a look at the data we processed before and see what I'm talking about here.", 'start': 15465.134, 'duration': 3.944}], 'summary': 'Using sklearn preprocessing to import label encoder and one hot encoder for data processing.', 'duration': 29.903, 'max_score': 15439.175, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE15439175.jpg'}, {'end': 15759.163, 'src': 'embed', 'start': 15727.699, 'weight': 14, 'content': [{'end': 15730.2, 'text': "It's not overly exciting because it's setting up our variables.", 'start': 15727.699, 'duration': 2.501}, {'end': 15733.962, 'text': 'But the next step is, the next step we actually create our linear regression model.', 'start': 15730.52, 'duration': 3.442}, {'end': 15740.627, 'text': "Now that we got to the linear regression model, we get that next piece of the puzzle, let's go ahead and put that code in there and walk through it.", 'start': 15734.262, 'duration': 6.365}, {'end': 15741.568, 'text': 'So here we go.', 'start': 15740.988, 'duration': 0.58}, {'end': 15747.293, 'text': "we're going to paste it in there and let's go ahead, and since this is a shorter line of code, let's zoom up there so we can get a good look.", 'start': 15741.568, 'duration': 5.725}, {'end': 15752.978, 'text': "And we have from the sklearn.linear underscore model, we're going to import linear regression.", 'start': 15747.553, 'duration': 5.425}, {'end': 15759.163, 'text': "Now I don't know if you recall from earlier, when we were doing all the math, let's go ahead and flip back there and take a look at that.", 'start': 15753.198, 'duration': 5.965}], 'summary': 'Setting up variables and creating linear regression model using sklearn.linear_model', 'duration': 31.464, 'max_score': 15727.699, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE15727699.jpg'}, {'end': 15815.927, 'src': 'embed', 'start': 15792.305, 'weight': 6, 'content': [{'end': 15800.233, 'text': "In this case, we do X train and Y train because we're using the training data, X being the data in and Y being profit, what we're looking at.", 'start': 15792.305, 'duration': 7.928}, {'end': 15802.175, 'text': 'And this does all that math for us.', 'start': 15800.513, 'duration': 1.662}, {'end': 15806.539, 'text': "So within one click and one line, we've created the whole linear regression model.", 'start': 15802.495, 'duration': 4.044}, {'end': 15809.402, 'text': 'And we fit the data to the linear regression model.', 'start': 15806.819, 'duration': 2.583}, {'end': 15813.967, 'text': 'And you can see that when I run the regressor It gives an output linear regression.', 'start': 15809.582, 'duration': 4.385}, {'end': 15815.927, 'text': 'it says copy x equals true.', 'start': 15813.967, 'duration': 1.96}], 'summary': 'Using x train and y train, a linear regression model is created and fitted to the data, giving an output of linear regression.', 'duration': 23.622, 'max_score': 15792.305, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE15792305.jpg'}, {'end': 15885.684, 'src': 'embed', 'start': 15854.588, 'weight': 11, 'content': [{'end': 15857.472, 'text': "and when I hit the Run button, it'll print that array out.", 'start': 15854.588, 'duration': 2.884}, {'end': 15861.016, 'text': 'I could have just as easily done print ypredict.', 'start': 15857.832, 'duration': 3.184}, {'end': 15867.385, 'text': "So if you're in a different IDE that's not an inline setup like the Jupyter Notebook, you can do it this way, print ypredict.", 'start': 15861.337, 'duration': 6.048}, {'end': 15874.075, 'text': "And you'll see that for the 200 different test variables we kept off to the side, it's going to produce 200 answers.", 'start': 15867.711, 'duration': 6.364}, {'end': 15877.038, 'text': 'This is what it says the profit are for those 200 predictions.', 'start': 15874.336, 'duration': 2.702}, {'end': 15878.579, 'text': "But let's don't stop there.", 'start': 15877.218, 'duration': 1.361}, {'end': 15881, 'text': "Let's keep going and take a couple look.", 'start': 15878.879, 'duration': 2.121}, {'end': 15885.684, 'text': "We're going to take just a short detail here in calculating the coefficients and the intercepts.", 'start': 15881.06, 'duration': 4.624}], 'summary': 'Printing array, 200 test variables produce 200 answers, calculating coefficients and intercepts.', 'duration': 31.096, 'max_score': 15854.588, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE15854588.jpg'}, {'end': 16258.994, 'src': 'embed', 'start': 16184.571, 'weight': 1, 'content': [{'end': 16195.858, 'text': 'This is an example of a confusion matrix, and this is used for identifying the accuracy of a classification model, like a logistic regression model.', 'start': 16184.571, 'duration': 11.287}, {'end': 16202.923, 'text': 'So the most important part in a confusion matrix is that, first of all, as you can see, this is a matrix,', 'start': 16196.058, 'duration': 6.865}, {'end': 16207.005, 'text': 'and the size of the matrix depends on how many outputs we are expecting.', 'start': 16202.923, 'duration': 4.082}, {'end': 16217.72, 'text': 'So the most important part here is that the model will be most accurate when we have the maximum numbers in its diagonal.', 'start': 16208.297, 'duration': 9.423}, {'end': 16218.94, 'text': 'Like in this case.', 'start': 16218, 'duration': 0.94}, {'end': 16228.063, 'text': "that's why it has almost 93, 94%, because the diagonal should have the maximum numbers and the others other than diagonal.", 'start': 16218.94, 'duration': 9.123}, {'end': 16231.244, 'text': 'the cells other than the diagonal should have very few numbers.', 'start': 16228.063, 'duration': 3.181}, {'end': 16232.986, 'text': "So here that's what is happening.", 'start': 16231.604, 'duration': 1.382}, {'end': 16239.833, 'text': 'So there is a 2 here, there is a 1 here, but most of them are along the diagonal.', 'start': 16233.046, 'duration': 6.787}, {'end': 16249.724, 'text': 'What does this mean? This means that the number that has been fed is 0 and the number that has been detected is also zero.', 'start': 16240.053, 'duration': 9.671}, {'end': 16253.108, 'text': 'So the predicted value and the actual value are the same.', 'start': 16250.044, 'duration': 3.064}, {'end': 16255.09, 'text': 'So along the diagonals.', 'start': 16253.188, 'duration': 1.902}, {'end': 16258.994, 'text': "that is true, which means that let's take this diagonal right?", 'start': 16255.09, 'duration': 3.904}], 'summary': 'Confusion matrix measures model accuracy; 93-94% accuracy due to high diagonal numbers', 'duration': 74.423, 'max_score': 16184.571, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE16184571.jpg'}, {'end': 16573.252, 'src': 'embed', 'start': 16550.864, 'weight': 2, 'content': [{'end': 16562.728, 'text': 'So we pass this probably multiple times and then We tested with the test data set and the split is usually in the form of there and there are various ways in which you can split this data.', 'start': 16550.864, 'duration': 11.864}, {'end': 16564.909, 'text': 'it is up to the individual preferences.', 'start': 16562.728, 'duration': 2.181}, {'end': 16568.93, 'text': 'In our case, here we are splitting in the form of 23 and 77.', 'start': 16565.429, 'duration': 3.501}, {'end': 16573.252, 'text': 'So when we say test size as 20.23, that means 23% of the entire data.', 'start': 16568.93, 'duration': 4.322}], 'summary': 'Tested data split with 23% and 77%, achieving 23% test size.', 'duration': 22.388, 'max_score': 16550.864, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE16550864.jpg'}, {'end': 16946.151, 'src': 'embed', 'start': 16923.537, 'weight': 8, 'content': [{'end': 16931.443, 'text': 'So we kind of create the confusion matrix and we will print it and this is how the confusion matrix looks as the name suggests it is a matrix.', 'start': 16923.537, 'duration': 7.906}, {'end': 16941.689, 'text': 'And the key point out here is that the accuracy of the model is determined by how many numbers are there in the diagonal.', 'start': 16932.283, 'duration': 9.406}, {'end': 16946.151, 'text': 'The more the numbers in the diagonal, the better the accuracy is.', 'start': 16941.729, 'duration': 4.422}], 'summary': 'Confusion matrix shows model accuracy based on diagonal numbers.', 'duration': 22.614, 'max_score': 16923.537, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE16923537.jpg'}], 'start': 14367.333, 'title': 'Machine learning models and visualizations', 'summary': 'Covers topics such as naive bayes model training, visualizing test set results, implementing linear regression, data visualization, linear regression modeling, validating machine learning models, logistic regression, and confusion matrix. it includes specific details like achieving an r2 score of 0.9352, 94% accuracy, and confusion matrix metrics including accuracy, precision, recall, and f1 score.', 'chapters': [{'end': 14735.272, 'start': 14367.333, 'title': 'Naive bayes model training', 'summary': 'Introduces the creation of a gaussian naive bayes model using sk learn kit, including its features and the process of fitting the data and making predictions, resulting in a confusion matrix with 65 true positives, 3 false positives, 25 true negatives, and 7 false negatives, and visualizing the training set using a contour plot.', 'duration': 367.939, 'highlights': ['Creating a Gaussian Naive Bayes model using SK Learn Kit The chapter discusses the process of creating a Gaussian Naive Bayes model using SK Learn Kit and the different options available, with Gaussian being the most commonly used.', 'Fitting the training data and making predictions The process of fitting the training data and making predictions using the Gaussian Naive Bayes model is explained, resulting in a confusion matrix with 65 true positives, 3 false positives, 25 true negatives, and 7 false negatives.', 'Visualizing the training set using a contour plot The chapter details the visualization of the training set using a contour plot, showing the distribution of data points and the differentiation between those who made a purchase and those who did not.']}, {'end': 15269.346, 'start': 14735.272, 'title': 'Visualizing test set results & implementing linear regression', 'summary': 'Discusses visualizing test set results, implementing multiple linear regression, and loading and formatting the data for linear regression in python, with key points on visualization, precision, recall, and implementing linear regression.', 'duration': 534.074, 'highlights': ['The chapter discusses visualizing test set results, highlighting the difference between 75% and 25% data representation, with a clear visualization of the test group and the effectiveness of the estimate.', 'It explains the precision, recall, and accuracy metrics from the y test and y predict, with precision of zeros at 90, recall at 0.96, and an F1 score, demonstrating the accuracy of the model to be around 90% for certain predictions.', 'The chapter then delves into multiple linear regression, explaining the concept of multiple inputs and the implementation of linear regression with multiple variables and coefficients, providing a clear understanding of the process.', 'It further explains the implementation of linear regression in Python, emphasizing the use of libraries such as numpy, pandas, matplotlib, and seaborn for data visualization, and loading and formatting the data for linear regression.', 'The process of loading and formatting the data for linear regression is explained, with a demonstration of extracting independent and dependent variables from the dataset, and visualizing the dataset using pandas, providing a comprehensive guide for data preparation.']}, {'end': 15970.688, 'start': 15284.338, 'title': 'Data visualization and linear regression modeling', 'summary': 'Discusses the process of visualizing data using seaborn and explains the steps for setting up a linear regression model, including preprocessing the data, splitting it into training and testing sets, and creating the model with sklearn. the chapter also highlights the calculation of coefficients and intercepts for the model.', 'duration': 686.35, 'highlights': ['The chapter discusses the process of visualizing data using Seaborn and explains the steps for setting up a linear regression model, including preprocessing the data, splitting it into training and testing sets, and creating the model with sklearn.', 'The visualization involves graphing the data using Seaborn, which recognizes the panda data frame and helps in understanding the correlation between different columns such as R&D spending, administration, marketing spending, and profit.', 'Preprocessing the data involves using label encoding and one hot encoding to transform categorical features into numerical values, preparing the data for the linear regression model.', 'The process of setting up the linear regression model includes splitting the data into training and testing sets, creating the multiple linear regression model, and using it to predict values for the test set.', "The chapter also highlights the calculation of coefficients and intercepts for the model, providing insights into the variables' impact on the predicted outcome of the linear regression model."]}, {'end': 16531.378, 'start': 15970.968, 'title': 'Validating machine learning models', 'summary': 'Discusses the process of validating machine learning models using r squared value, achieving an r2 score of 0.9352, training a model for profit estimation, and applying logistic regression for digit recognition achieving an accuracy of 94% using a confusion matrix.', 'duration': 560.41, 'highlights': ['The R2 score for the prediction is 0.9352, indicating a highly valid model with good accuracy, although not exactly 93%. The R2 score is calculated to predict the accuracy of the model, achieving a value of 0.9352, which demonstrates a highly valid prediction with good accuracy.', 'Successfully training a model for profit estimation using linear regression and achieving an R squared value of 0.91 or 9.2, indicating a good model. The model is successfully trained for profit estimation using linear regression, attaining an R squared value of 0.91 or 9.2, signifying a good model.', 'Applying logistic regression for digit recognition results in an accuracy of about 94%, demonstrated by the confusion matrix. The application of logistic regression for digit recognition yields an accuracy of about 94%, as evidenced by the confusion matrix.']}, {'end': 17575.79, 'start': 16531.437, 'title': 'Logistic regression and confusion matrix', 'summary': 'Focuses on training a logistic regression model with 23% test and 77% training data, achieving 94% accuracy, and visualizing the outcomes using a confusion matrix, while explaining the importance of confusion matrix metrics including accuracy, precision, recall, and f1 score.', 'duration': 1044.353, 'highlights': ['The training data is split into 23% test and 77% training data. The data is split into training and test data, with 23% of the entire data used for testing and the remaining 77% used for training.', 'The logistic regression model achieves 94% accuracy. After training, the model is tested and found to be accurate up to 94%.', "The confusion matrix is used to visualize the outcomes and determine the model's accuracy. A confusion matrix is created to visualize the outcomes of the model's predictions, where the diagonal values indicate correct predictions, and the matrix helps in determining the model's accuracy.", "Confusion matrix metrics including accuracy, precision, recall, and F1 score are explained. The importance of confusion matrix metrics such as accuracy, precision, recall, and F1 score is emphasized for evaluating the classifier's performance."]}], 'duration': 3208.457, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE14367333.jpg', 'highlights': ['Achieving an r2 score of 0.9352 for model accuracy', 'Implementing logistic regression with 94% accuracy', 'Visualizing test set results and effectiveness of the estimate', 'Explaining the process of creating a Gaussian Naive Bayes model', 'Fitting training data and making predictions with Gaussian Naive Bayes model', 'Visualizing training set using a contour plot', 'Explaining precision, recall, and accuracy metrics from the y test and y predict', 'Delving into multiple linear regression and its implementation', 'Using libraries such as numpy, pandas, matplotlib, and seaborn for data visualization', 'Visualizing data using Seaborn and setting up a linear regression model', 'Preprocessing data for linear regression model and transforming categorical features', 'Calculating coefficients and intercepts for the linear regression model', 'Successfully training a model for profit estimation using linear regression', 'Applying logistic regression for digit recognition and analyzing confusion matrix', 'Splitting the training data into 23% test and 77% training data', 'Explaining confusion matrix metrics including accuracy, precision, recall, and F1 score']}, {'end': 18909.98, 'segs': [{'end': 17868.588, 'src': 'embed', 'start': 17839.557, 'weight': 2, 'content': [{'end': 17843.798, 'text': "So we're going to go ahead and create Y, and we're going to set it equal to the target.", 'start': 17839.557, 'duration': 4.241}, {'end': 17848.72, 'text': "So here's our target value here, and it's either 1 or 0.", 'start': 17844.638, 'duration': 4.082}, {'end': 17852.401, 'text': 'So we have a classifier.', 'start': 17848.72, 'duration': 3.681}, {'end': 17856.783, 'text': "If you're dealing with 1, 0, true, false, what do you have? You have a classifier.", 'start': 17852.421, 'duration': 4.362}, {'end': 17864.106, 'text': 'And then our X is going to be everything except for the target.', 'start': 17858.143, 'duration': 5.963}, {'end': 17866.347, 'text': "So we're going to go ahead and drop the target.", 'start': 17864.726, 'duration': 1.621}, {'end': 17868.588, 'text': 'Axes equals 1.', 'start': 17866.947, 'duration': 1.641}], 'summary': 'Creating a classifier with target values of 1 or 0.', 'duration': 29.031, 'max_score': 17839.557, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE17839557.jpg'}, {'end': 18257.571, 'src': 'embed', 'start': 18208.049, 'weight': 6, 'content': [{'end': 18212.771, 'text': "is this number of people and hopefully you'd have a much larger data set?", 'start': 18208.049, 'duration': 4.722}, {'end': 18213.151, 'text': 'it might.', 'start': 18212.771, 'duration': 0.38}, {'end': 18218.733, 'text': 'is my confusion matrix showing for the true positive and false positive?', 'start': 18213.151, 'duration': 5.582}, {'end': 18220.514, 'text': "is that acceptable for what we're doing?", 'start': 18218.733, 'duration': 1.781}, {'end': 18228.5, 'text': "And, of course, if you're going to put together whatever data you're putting out, you might want to separate the true negative, false positive,", 'start': 18221.395, 'duration': 7.105}, {'end': 18229.981, 'text': 'false negative, true positive.', 'start': 18228.5, 'duration': 1.481}, {'end': 18233.823, 'text': 'You can simply do that by doing the confusion matrix.', 'start': 18230.621, 'duration': 3.202}, {'end': 18237.986, 'text': 'And then, of course, the ravel part lets you set that up.', 'start': 18234.844, 'duration': 3.142}, {'end': 18240.227, 'text': 'So you can just split that right up into a nice tuple.', 'start': 18238.046, 'duration': 2.181}, {'end': 18249.423, 'text': 'And the final thing we want to show you here in the coding on this part is the confusion matrix metrics.', 'start': 18240.615, 'duration': 8.808}, {'end': 18257.571, 'text': 'And so we can come in here and just use the matrix equals classification report, the y test and the predict.', 'start': 18250.765, 'duration': 6.806}], 'summary': 'Discussion about confusion matrix, classification report, and data separation in a coding context.', 'duration': 49.522, 'max_score': 18208.049, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE18208049.jpg'}, {'end': 18329.35, 'src': 'embed', 'start': 18284.95, 'weight': 0, 'content': [{'end': 18290.533, 'text': '0.87 for getting a positive and 0.83 for the negative side for a zero.', 'start': 18284.95, 'duration': 5.583}, {'end': 18295.556, 'text': 'And we start talking about whether this is a valid information or not to use.', 'start': 18291.273, 'duration': 4.283}, {'end': 18300.398, 'text': "And when we're looking at a heart attack prediction, we're only looking at one aspect.", 'start': 18296.376, 'duration': 4.022}, {'end': 18306.862, 'text': "What's the chances of this person having a heart attack or not? You might have something where we went back to the languages.", 'start': 18300.518, 'duration': 6.344}, {'end': 18312.767, 'text': 'Maybe you also want to know whether they speak English or Hindi or French.', 'start': 18307.443, 'duration': 5.324}, {'end': 18319.773, 'text': 'And you can see right here that we can now take our confusion matrix and just expand it as big as we need to,', 'start': 18313.267, 'duration': 6.506}, {'end': 18322.675, 'text': "depending on how many different classifiers we're working on.", 'start': 18319.773, 'duration': 2.902}, {'end': 18325.207, 'text': 'Decision tree, important terms.', 'start': 18323.086, 'duration': 2.121}, {'end': 18329.35, 'text': 'Before we dive in further, we need to look at some basic terms.', 'start': 18325.568, 'duration': 3.782}], 'summary': 'Data analysis involves evaluating accuracy with 0.87 for positive and 0.83 for negative, and expanding confusion matrix for different classifiers.', 'duration': 44.4, 'max_score': 18284.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE18284950.jpg'}, {'end': 18457.679, 'src': 'embed', 'start': 18427.93, 'weight': 4, 'content': [{'end': 18429.691, 'text': 'How does a decision tree work?', 'start': 18427.93, 'duration': 1.761}, {'end': 18432.251, 'text': "Wonder what kind of animals I'll get in the jungle today?", 'start': 18429.751, 'duration': 2.5}, {'end': 18437.233, 'text': "Maybe you're the hunter with the gun or, if you're more into photography, you're a photographer with a camera.", 'start': 18432.491, 'duration': 4.742}, {'end': 18444.675, 'text': "So let's look at this group of animals and let's try to classify different types of animals based on their features using a decision tree.", 'start': 18437.373, 'duration': 7.302}, {'end': 18450.257, 'text': 'So the problem statement is to classify the different types of animals based on their features using a decision tree.', 'start': 18444.935, 'duration': 5.322}, {'end': 18454.398, 'text': 'The data set is looking quite messy and the entropy is high in this case.', 'start': 18450.337, 'duration': 4.061}, {'end': 18457.679, 'text': "So let's look at a training set or a training data set.", 'start': 18454.698, 'duration': 2.981}], 'summary': 'Classifying animals using decision tree with high entropy data.', 'duration': 29.749, 'max_score': 18427.93, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE18427930.jpg'}, {'end': 18490.357, 'src': 'embed', 'start': 18462.561, 'weight': 1, 'content': [{'end': 18465.602, 'text': 'We have our elephants, our giraffes, our monkeys, and our tigers.', 'start': 18462.561, 'duration': 3.041}, {'end': 18467.783, 'text': "And they're of different colors and shapes.", 'start': 18465.942, 'duration': 1.841}, {'end': 18470.624, 'text': "Let's see what that looks like and how do we split the data.", 'start': 18468.083, 'duration': 2.541}, {'end': 18476.947, 'text': 'We have to frame the conditions that split the data in such a way that the information gain is the highest.', 'start': 18470.784, 'duration': 6.163}, {'end': 18481.271, 'text': 'Note, gain is the measure of decrease in entropy after splitting.', 'start': 18477.388, 'duration': 3.883}, {'end': 18490.357, 'text': "So the formula for entropy is the sum, that's what this symbol looks like, it looks kind of like a funky E, of k, where i equals 1 to k.", 'start': 18481.411, 'duration': 8.946}], 'summary': 'Data is split to maximize information gain using entropy formula.', 'duration': 27.796, 'max_score': 18462.561, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE18462561.jpg'}], 'start': 17575.81, 'title': 'Model performance evaluation and data science basics', 'summary': 'Explores model performance evaluation using metrics such as accuracy, precision, recall, and f1 score to compare models, and discusses data preprocessing, including splitting data and scaling before model creation. it also delves into heart attack prediction, decision tree basics, and animal classification using decision trees, achieving 100% accuracy in prediction. additionally, it illustrates loan repayment prediction model implementation using the decision tree algorithm in python.', 'chapters': [{'end': 17798.465, 'start': 17575.81, 'title': 'Evaluating model performance with metrics', 'summary': 'Explores the evaluation of model performance using metrics such as accuracy, precision, recall, and f1 score to compare different models in classifying english speakers, and demonstrates the use of pandas, scikit framework, and various machine learning models in python setup.', 'duration': 222.655, 'highlights': ['The chapter covers the evaluation of model performance using metrics such as accuracy, precision, recall, and F1 score to compare different models in classifying English speakers. The emphasis on using metrics like accuracy, precision, recall, and F1 score to assess model performance provides a comprehensive understanding of the evaluation process.', 'Demonstrates the use of Pandas, Scikit framework, and various machine learning models in Python setup. The demonstration of using Pandas, Scikit framework, and machine learning models in a Python setup showcases practical implementation and integration of these tools for data analysis and model evaluation.', 'The chapter emphasizes the importance of accuracy in classifying English speakers and provides a practical demonstration of loading and processing data using Pandas in Jupyter Notebook. The practical emphasis on accuracy in classifying English speakers and the demonstration of loading and processing data using Pandas in Jupyter Notebook provides hands-on insights into practical data analysis and model evaluation.']}, {'end': 18086.027, 'start': 17798.525, 'title': 'Data science basics', 'summary': 'Discusses data preprocessing, including splitting the data into training and testing sets, and scaling the data before model creation, emphasizing the importance of fitting the scalar on the training data and not the test data, and the relevance of scaling in different models such as linear regression and neural networks.', 'duration': 287.502, 'highlights': ["The chapter emphasizes the importance of fitting the scalar on the training data and not the test data before model creation. It's crucial to split the data into training and testing sets before fitting the scalar on the training data, as altering the test data can impact the results. This is highlighted to avoid altering results while scaling the data.", 'The relevance of scaling data in different models such as linear regression and neural networks is discussed, with the mention that scaling has a more significant impact on models like neural networks. The chapter explains that scaling data has a more significant impact on models like neural networks compared to linear regression models, as it can alter the results significantly. It underscores the importance of scaling based on the model being used.', 'The process of data preprocessing, including splitting the data into training and testing sets, and scaling the data before model creation, is detailed. The chapter covers the entire process of data preprocessing, from splitting the data into training and testing sets to scaling the data, emphasizing the significance of these steps in preparing the data for model creation.']}, {'end': 18406.81, 'start': 18086.047, 'title': 'Heart attack prediction and decision tree basics', 'summary': 'Discusses the accuracy of heart attack prediction at 85%, confusion matrix analysis with 25 true positive predictions, and the basics of decision tree including entropy and information gain.', 'duration': 320.763, 'highlights': ['The accuracy of heart attack prediction is at 85%. The model accurately predicts whether a person is at high risk for a heart attack 85% of the time.', 'Confusion matrix analysis shows 25 true positive predictions. Out of 29 people, the model correctly identified 25 individuals as high risk for a heart attack, indicating strong predictive capability.', 'Basic terms of decision tree, entropy, and information gain are explained. The concept of entropy as a measure of unpredictability in a dataset and information gain as a measure of decrease in entropy after dataset splitting are detailed, providing foundational understanding for decision tree analysis.']}, {'end': 18642.045, 'start': 18406.91, 'title': 'Decision tree for animal classification', 'summary': 'Explains the process of using a decision tree to classify different types of animals based on their features, aiming to achieve 100% accuracy in prediction by splitting the data based on color and height to minimize entropy.', 'duration': 235.135, 'highlights': ['The tree can predict all the classes of animals with 100% accuracy After splitting the data based on color and height, the decision tree can accurately predict all animal classes in the data set.', 'Entropy value equal to zero achieved after splitting based on height By splitting both nodes based on height and attaining single label type in each branch, the entropy value is minimized to zero.', 'Maximum gain achieved by splitting the data based on the color yellow The condition of splitting based on the color yellow yields the highest gain, leading to an effective initial split of the data.']}, {'end': 18909.98, 'start': 18642.065, 'title': 'Loan repayment prediction using decision tree algorithm in python', 'summary': 'Illustrates the process of implementing a loan repayment prediction model using the decision tree algorithm in python, emphasizing the use of necessary packages, data loading, and handling warnings and errors during the coding process.', 'duration': 267.915, 'highlights': ['Implementing the loan repayment prediction model using the decision tree algorithm in Python The chapter focuses on the implementation of a loan repayment prediction model using the decision tree algorithm in Python, detailing the necessary steps and tools involved in the process.', 'Emphasizing the use of necessary packages and data loading in Python The transcript highlights the importance of importing necessary packages such as numpy and pandas for data manipulation and loading, essential for implementing the loan repayment prediction model.', 'Handling warnings and errors during the coding process The chapter addresses the process of handling warnings and errors encountered during the coding process, emphasizing the significance of understanding and resolving these issues in Python programming.']}], 'duration': 1334.17, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE17575810.jpg', 'highlights': ['The chapter covers the evaluation of model performance using metrics such as accuracy, precision, recall, and F1 score to compare different models in classifying English speakers.', 'The demonstration of using Pandas, Scikit framework, and machine learning models in a Python setup showcases practical implementation and integration of these tools for data analysis and model evaluation.', 'The chapter emphasizes the importance of fitting the scalar on the training data and not the test data before model creation.', 'The chapter explains that scaling data has a more significant impact on models like neural networks compared to linear regression models, as it can alter the results significantly.', 'The model accurately predicts whether a person is at high risk for a heart attack 85% of the time.', 'Out of 29 people, the model correctly identified 25 individuals as high risk for a heart attack, indicating strong predictive capability.', 'The tree can predict all the classes of animals with 100% accuracy After splitting the data based on color and height, the decision tree can accurately predict all animal classes in the data set.', 'The chapter focuses on the implementation of a loan repayment prediction model using the decision tree algorithm in Python.']}, {'end': 21327.683, 'segs': [{'end': 19202.653, 'src': 'embed', 'start': 19178.132, 'weight': 7, 'content': [{'end': 19184.84, 'text': 'of a thousand long, five wide, so we have five columns, and we do the full data head, you can actually see what this data looks like.', 'start': 19178.132, 'duration': 6.708}, {'end': 19188.424, 'text': 'The initial payment, last payment, credit scores, house number.', 'start': 19185, 'duration': 3.424}, {'end': 19193.209, 'text': "So let's take this, now that we've explored the data, and let's start digging into the decision tree.", 'start': 19188.664, 'duration': 4.545}, {'end': 19198.472, 'text': "So in our next step, we're going to train and build our data tree.", 'start': 19193.409, 'duration': 5.063}, {'end': 19202.653, 'text': 'And to do that, we need to first separate the data out.', 'start': 19198.732, 'duration': 3.921}], 'summary': 'Analyzing a dataset with 1000 entries and 5 columns to build a decision tree model.', 'duration': 24.521, 'max_score': 19178.132, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE19178132.jpg'}, {'end': 19298.091, 'src': 'embed', 'start': 19271.957, 'weight': 8, 'content': [{'end': 19281.102, 'text': "Well, if we want to look at 1 through 5, we can do the same thing for y, which is the answers, and we're going to set that just equal to the 0 row.", 'start': 19271.957, 'duration': 9.145}, {'end': 19284.564, 'text': "So it's just the 0 row, and then it's all rows going in there.", 'start': 19281.442, 'duration': 3.122}, {'end': 19293.108, 'text': "So now we've divided this into two different data sets, one of them with the data going in and one with the answers.", 'start': 19284.624, 'duration': 8.484}, {'end': 19298.091, 'text': 'Next, we need to split the data.', 'start': 19296.23, 'duration': 1.861}], 'summary': 'Data and answers are divided into two datasets and then split further.', 'duration': 26.134, 'max_score': 19271.957, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE19271957.jpg'}, {'end': 19380.748, 'src': 'embed', 'start': 19345.35, 'weight': 2, 'content': [{'end': 19348.091, 'text': "And they've called it here CLF underscore entropy.", 'start': 19345.35, 'duration': 2.741}, {'end': 19351.792, 'text': "That's the actual decision tree, or decision tree classifier.", 'start': 19348.451, 'duration': 3.341}, {'end': 19355.774, 'text': "And in here they've added a couple variables, which we'll explore in just a minute.", 'start': 19352.113, 'duration': 3.661}, {'end': 19358.995, 'text': 'And then finally, we need to fit the data to that.', 'start': 19356.214, 'duration': 2.781}, {'end': 19363.997, 'text': 'So we take our CLF entropy that we created and we fit the X train.', 'start': 19359.315, 'duration': 4.682}, {'end': 19367.839, 'text': 'and since we know the answers for X train are the Y train, we go ahead and put those in.', 'start': 19363.997, 'duration': 3.842}, {'end': 19369.88, 'text': "And let's go ahead and run this.", 'start': 19368.359, 'duration': 1.521}, {'end': 19378.467, 'text': 'and what most of these sklearn modules do is when you set up the variable in this case, when we set the CLF entropy equal decision tree classifier,', 'start': 19369.88, 'duration': 8.587}, {'end': 19380.748, 'text': "it automatically prints out what's in that decision tree.", 'start': 19378.467, 'duration': 2.281}], 'summary': 'Using clf entropy to fit decision tree classifier with x train and y train.', 'duration': 35.398, 'max_score': 19345.35, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE19345350.jpg'}, {'end': 19467.704, 'src': 'embed', 'start': 19442.366, 'weight': 1, 'content': [{'end': 19449.49, 'text': "And we're going to use our variable CLF entropy that we created and then you'll see .", 'start': 19442.366, 'duration': 7.124}, {'end': 19458.016, 'text': "predict and it's very common in the sklearn modules that their different tools have the predict when you're actually running a prediction.", 'start': 19449.49, 'duration': 8.526}, {'end': 19462.16, 'text': "in this case, we're gonna put our X test data in here now.", 'start': 19458.016, 'duration': 4.144}, {'end': 19467.704, 'text': 'if you delivered this for use, an actual commercial use, and distributed it,', 'start': 19462.16, 'duration': 5.544}], 'summary': 'Using clf entropy variable for prediction in sklearn modules.', 'duration': 25.338, 'max_score': 19442.366, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE19442366.jpg'}, {'end': 20600.9, 'src': 'embed', 'start': 20569.594, 'weight': 4, 'content': [{'end': 20570.175, 'text': 'Alright, here we go.', 'start': 20569.594, 'duration': 0.581}, {'end': 20576.2, 'text': "We've set y equal to pd.factorize train species of 0.", 'start': 20570.215, 'duration': 5.985}, {'end': 20579.422, 'text': "So let's break this down just a little bit.", 'start': 20576.2, 'duration': 3.222}, {'end': 20583.045, 'text': 'We have our pandas right here, pd, factorize.', 'start': 20579.542, 'duration': 3.503}, {'end': 20586.108, 'text': "What's factorize doing? I'm going to come back to that in just a second.", 'start': 20583.185, 'duration': 2.923}, {'end': 20592.673, 'text': "Let's look at what train species is and why we're looking at the group 0 on there.", 'start': 20586.708, 'duration': 5.965}, {'end': 20596.557, 'text': "And let's go up here and here is our species.", 'start': 20593.574, 'duration': 2.983}, {'end': 20600.9, 'text': 'Remember this? We created this whole column here for species.', 'start': 20597.978, 'duration': 2.922}], 'summary': 'Using pd.factorize to convert train species column to numerical values.', 'duration': 31.306, 'max_score': 20569.594, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE20569594.jpg'}, {'end': 20794.193, 'src': 'embed', 'start': 20768.957, 'weight': 6, 'content': [{'end': 20775.262, 'text': 'this automatically treats this just like when we were up here and I typed in y and it printed out y instead of print y.', 'start': 20768.957, 'duration': 6.305}, {'end': 20777.415, 'text': 'This does the same thing.', 'start': 20776.294, 'duration': 1.121}, {'end': 20779.916, 'text': 'It treats this as a variable and prints it out.', 'start': 20777.875, 'duration': 2.041}, {'end': 20783.298, 'text': "But if you're actually running your code, that wouldn't be the case.", 'start': 20780.416, 'duration': 2.882}, {'end': 20788.501, 'text': 'And what is printed out is it shows us all the different variables we can change.', 'start': 20783.598, 'duration': 4.903}, {'end': 20792.571, 'text': 'And if we go down here, you can actually see njobs equals 2.', 'start': 20788.581, 'duration': 3.99}, {'end': 20794.193, 'text': 'You can see the random state equals zero.', 'start': 20792.571, 'duration': 1.622}], 'summary': 'Demonstrates automatic variable treatment and printed output, with njobs=2 and random state=0.', 'duration': 25.236, 'max_score': 20768.957, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE20768957.jpg'}, {'end': 20832.131, 'src': 'embed', 'start': 20806.368, 'weight': 0, 'content': [{'end': 20811.073, 'text': "So all the features that we're putting in there is just going to automatically take all four of them.", 'start': 20806.368, 'duration': 4.705}, {'end': 20812.494, 'text': "whatever we send it, it'll take.", 'start': 20811.073, 'duration': 1.421}, {'end': 20815.957, 'text': "some of them might have so many features because you're processing words.", 'start': 20812.494, 'duration': 3.463}, {'end': 20822.402, 'text': "there might be like 1.4 million features in there because you're doing legal documents, and that's how many different words are in there.", 'start': 20815.957, 'duration': 6.445}, {'end': 20826.526, 'text': "at that point you probably want to limit the maximum features that you're going to process.", 'start': 20822.402, 'duration': 4.124}, {'end': 20828.207, 'text': "and leaf notes that's the end notes.", 'start': 20826.526, 'duration': 1.681}, {'end': 20830.87, 'text': "remember we had the fruit and we're talking about the leaf notes.", 'start': 20828.207, 'duration': 2.663}, {'end': 20832.131, 'text': "like i said, there's a lot in this.", 'start': 20830.87, 'duration': 1.261}], 'summary': 'Automatically processes all features and limits maximum features, e.g., 1.4 million for legal documents.', 'duration': 25.763, 'max_score': 20806.368, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE20806368.jpg'}, {'end': 20922.936, 'src': 'embed', 'start': 20896.492, 'weight': 5, 'content': [{'end': 20901.834, 'text': "So let's go ahead and take this code and let's put it into our script and see what that looks like.", 'start': 20896.492, 'duration': 5.342}, {'end': 20902.694, 'text': 'Okay, here we go.', 'start': 20901.854, 'duration': 0.84}, {'end': 20904.721, 'text': "And we're going to run this.", 'start': 20903.66, 'duration': 1.061}, {'end': 20913.488, 'text': "And it's going to come out with a bunch of zeros, ones and twos, which represents the three type of flowers the setosa,", 'start': 20906.783, 'duration': 6.705}, {'end': 20915.13, 'text': 'the virginica and the versicolor.', 'start': 20913.488, 'duration': 1.642}, {'end': 20918.592, 'text': "And what we're putting into our predict is the test features.", 'start': 20915.27, 'duration': 3.322}, {'end': 20922.936, 'text': 'And I always kind of like to know what it is I am looking at.', 'start': 20919.513, 'duration': 3.423}], 'summary': 'Running the code results in zeros, ones, and twos, representing three types of flowers setosa, virginica, and versicolor.', 'duration': 26.444, 'max_score': 20896.492, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE20896492.jpg'}, {'end': 21247.414, 'src': 'embed', 'start': 21218.936, 'weight': 9, 'content': [{'end': 21223.258, 'text': "And let's talk about both these sections of code here and how they go together.", 'start': 21218.936, 'duration': 4.322}, {'end': 21226.519, 'text': 'The first one is our predictions.', 'start': 21223.978, 'duration': 2.541}, {'end': 21228.92, 'text': 'And I went ahead and did predictions through 25.', 'start': 21226.599, 'duration': 2.321}, {'end': 21231.821, 'text': "Let's just do 5.", 'start': 21228.92, 'duration': 2.901}, {'end': 21234.282, 'text': 'And so we have cytosis, cytosis, cytosis, cytosis.', 'start': 21231.821, 'duration': 2.461}, {'end': 21236.903, 'text': "That's what we're predicting from our test model.", 'start': 21234.302, 'duration': 2.601}, {'end': 21240.625, 'text': 'And then we come down here and we look at test species.', 'start': 21237.981, 'duration': 2.644}, {'end': 21244.289, 'text': 'And remember I could have just done test.species.head.', 'start': 21240.645, 'duration': 3.644}, {'end': 21247.414, 'text': "And you'll see it says setosa, setosa, setosa, setosa.", 'start': 21244.47, 'duration': 2.944}], 'summary': "Code analysis shows 25 predictions with 5 being 'cytosis' and test species as 'setosa'.", 'duration': 28.478, 'max_score': 21218.936, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE21218936.jpg'}], 'start': 18911.921, 'title': 'Data analysis and model training', 'summary': 'Covers data exploration of a 1000-line, 5-column dataset, decision tree training with specified parameters, application of decision tree classifier achieving 93.6% accuracy in loan repayment prediction, use of random forest classifier to predict loan defaults, python coding for iris flower analysis, data preprocessing, model training, and testing.', 'chapters': [{'end': 19418.736, 'start': 18911.921, 'title': 'Data exploration and decision tree training', 'summary': "Covers the exploration of a dataset with 1000 lines and 5 columns, using python's pandas module, and the training of a decision tree classifier with entropy, random state of 100%, max depth of 3, and minimal samples of leaves as 5.", 'duration': 506.815, 'highlights': ['The dataset contains 1000 lines of data with 5 columns, and the initial payment, last payment, credit score, and house number are among the key attributes. The dataset comprises 1000 lines of data with 5 columns, including key attributes like initial payment, last payment, credit score, and house number.', 'The pandas module is utilized to print the length, shape, and first five lines of the dataset, providing a clear and readable view of the data. The pandas module is leveraged to display the length, shape, and first five lines of the dataset, ensuring a clear and readable view of the data.', 'The decision tree classifier is trained using entropy, a random state of 100%, a maximum depth of 3, and a minimum of 5 samples for leaves. The decision tree classifier is trained with entropy, a random state of 100%, a maximum depth of 3, and a minimum of 5 samples for leaves.']}, {'end': 19631.245, 'start': 19419.016, 'title': 'Decision tree classifier application', 'summary': 'Demonstrates the application of a decision tree classifier to predict loan repayments, achieving an accuracy of 93.6%, enabling the bank to make informed loan approval decisions.', 'duration': 212.229, 'highlights': ['The decision tree classifier achieved an accuracy of 93.6%, enabling the bank to make informed loan approval decisions.', 'The model uses the decision tree algorithm to predict whether a customer will repay the loan or not, with an accuracy of about 94.6%.', "The accuracy score of the decision tree classifier is obtained using the sklearn.metrics library, demonstrating the model's reliability in predicting loan repayments.", 'The predict code utilizes the X test data to generate predictions, simulating the assessment of new loans for potential repayment outcomes.', "The predict code runs a prediction on about 300 loan samples, providing a practical demonstration of the model's predictive capabilities."]}, {'end': 19816.247, 'start': 19631.425, 'title': 'Random forest classifier for predicting loan defaults', 'summary': 'Discusses the use of random forest classifier to predict loan defaults, explaining the process with three decision trees and how the majority vote determines the final prediction.', 'duration': 184.822, 'highlights': ['Random forest classifier used to predict loan defaults The bank uses the random forest classifier to predict the profit and default rate of loan balances.', 'Explanation of random forest classifier process with decision trees The chapter details the process of random forest classifier using three decision trees to classify fruits based on various features.', 'Majority vote determines final prediction The final prediction of the fruit is determined by the majority vote from the decision trees, where the highest number of votes decides the classification.']}, {'end': 20547.07, 'start': 19816.547, 'title': 'Python coding for iris flower analysis', 'summary': 'Introduces python coding for iris flower analysis, including importing modules, exploring and organizing the iris data, splitting the data for training and testing, and making the data readable to humans.', 'duration': 730.523, 'highlights': ['The chapter introduces Python coding for iris flower analysis, including importing modules, exploring and organizing the iris data, splitting the data for training and testing, and making the data readable to humans. The summary encompasses the main points of the transcript, providing an overview of the Python coding process for iris flower analysis.', 'The first step involves loading the different modules into Python, such as sklearn.datasets, and importing pandas and numpy for data organization and manipulation. The initial step involves importing necessary modules into Python, including sklearn.datasets, pandas, and numpy, for data organization and manipulation.', 'The process includes exploring the data, creating a problem statement, and predicting the species of the flowers using machine learning in Python. Exploration of the data involves creating a problem statement and using machine learning in Python to predict the species of the flowers.', "The data is split into training and testing sets, with 75% used for training and 25% for testing, to assess the model's performance. The data is divided into training and testing sets, with 75% used for training and 25% for testing to evaluate the model's performance.", 'Efforts are made to make the data readable to humans by converting features and labels into a more understandable format. Steps are taken to make the data more human-readable by converting features and labels into a more understandable format.']}, {'end': 20788.501, 'start': 20547.33, 'title': 'Data preprocessing and model training', 'summary': 'Covers the process of converting species data into a format the computer understands, using pd.factorize to create an array representing the three different kinds of flowers, and training a random forest classifier on the data.', 'duration': 241.171, 'highlights': ['Using pd.factorize to convert species data into an array representing the three different kinds of flowers, with zeros, ones, and twos. The pd.factorize function is used to convert the species data into an array that represents the three different kinds of flowers, where zeros, ones, and twos correspond to the different species.', 'Training a random forest classifier on the features and target data, with the random state set to zero. The random forest classifier is trained on the features and target data, with the random state set to zero, using clf.fit(train_features, y).', 'Creating a variable CLF and setting it equal to the random forest classifier, passing in two standard variables for jobs and random state. A variable CLF is created and set equal to the random forest classifier, with two standard variables passed in for jobs and random state, where jobs prioritize and random state determines the starting point.']}, {'end': 21327.683, 'start': 20788.581, 'title': 'Model training and testing', 'summary': 'Covers the training and testing of a model using 25% test data, predicting flower types, and combining predictions and actual data using a single line of code.', 'duration': 539.102, 'highlights': ['The chapter covers the training and testing of a model using 25% test data. The training model uses 75% of the data, while 25% is set aside for testing.', 'Predicting flower types based on the test features. The model predicts flower types (setosa, virginica, versicolor) based on the test features using a new forest classifier.', 'Combining predictions and actual data using a single line of code to create a chart. A single line of code is used to combine predictions and actual data in pandas, creating a chart showing the predicted and actual species.']}], 'duration': 2415.762, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE18911921.jpg', 'highlights': ['The decision tree classifier achieved an accuracy of 93.6%, enabling the bank to make informed loan approval decisions.', 'The model uses the decision tree algorithm to predict whether a customer will repay the loan or not, with an accuracy of about 94.6%.', "The accuracy score of the decision tree classifier is obtained using the sklearn.metrics library, demonstrating the model's reliability in predicting loan repayments.", "The predict code runs a prediction on about 300 loan samples, providing a practical demonstration of the model's predictive capabilities.", 'The dataset contains 1000 lines of data with 5 columns, and the initial payment, last payment, credit score, and house number are among the key attributes.', 'The pandas module is leveraged to display the length, shape, and first five lines of the dataset, ensuring a clear and readable view of the data.', 'The chapter introduces Python coding for iris flower analysis, including importing modules, exploring and organizing the iris data, splitting the data for training and testing, and making the data readable to humans.', "The data is divided into training and testing sets, with 75% used for training and 25% for testing to evaluate the model's performance.", 'The predict code utilizes the X test data to generate predictions, simulating the assessment of new loans for potential repayment outcomes.', 'The random forest classifier is trained on the features and target data, with the random state set to zero, using clf.fit(train_features, y).']}, {'end': 22143.763, 'segs': [{'end': 21405.384, 'src': 'embed', 'start': 21377.15, 'weight': 1, 'content': [{'end': 21382.296, 'text': "I don't know if you remember it, but predicts equals the iris.target underscore names.", 'start': 21377.15, 'duration': 5.146}, {'end': 21384.558, 'text': "So we're going to map it to the names.", 'start': 21383.036, 'duration': 1.522}, {'end': 21388.3, 'text': "And we're going to run the prediction, and we ran it on test features.", 'start': 21385.359, 'duration': 2.941}, {'end': 21391.641, 'text': "But, you know, we're not just testing it, we want to actually deploy it.", 'start': 21388.68, 'duration': 2.961}, {'end': 21394.461, 'text': 'So at this point, I would go ahead and change this.', 'start': 21391.881, 'duration': 2.58}, {'end': 21396.922, 'text': 'And this is an array of arrays.', 'start': 21395.141, 'duration': 1.781}, {'end': 21399.542, 'text': "This is really important when you're running these to know that.", 'start': 21397.262, 'duration': 2.28}, {'end': 21401.743, 'text': 'So you need the double brackets.', 'start': 21400.383, 'duration': 1.36}, {'end': 21405.384, 'text': "And I could actually create data, maybe, let's just do two flowers.", 'start': 21401.903, 'duration': 3.481}], 'summary': 'Mapping iris target names to predictions and deploying with double brackets.', 'duration': 28.234, 'max_score': 21377.15, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE21377150.jpg'}, {'end': 21673.48, 'src': 'embed', 'start': 21632.156, 'weight': 0, 'content': [{'end': 21638.178, 'text': 'K and KNN is a perimeter that refers to the number of nearest neighbors to include in the majority of the voting process.', 'start': 21632.156, 'duration': 6.022}, {'end': 21643.501, 'text': 'And so if we add a new glass of wine there, red or white, we want to know what the neighbors are.', 'start': 21638.439, 'duration': 5.062}, {'end': 21646.282, 'text': "In this case, we're going to put K equals 5.", 'start': 21643.621, 'duration': 2.661}, {'end': 21647.642, 'text': "We'll talk about K in just a minute.", 'start': 21646.282, 'duration': 1.36}, {'end': 21652.084, 'text': 'A data point is classified by the majority of votes from its five nearest neighbors.', 'start': 21647.862, 'duration': 4.222}, {'end': 21656.766, 'text': 'Here, the unknown point would be classified as red since four out of five neighbors are red.', 'start': 21652.544, 'duration': 4.222}, {'end': 21664.027, 'text': "So how do we choose K? How do we know K equals 5? I mean, that was the value we put in there, so we're going to talk about it.", 'start': 21657.358, 'duration': 6.669}, {'end': 21668.954, 'text': 'How do we choose the factor K? K&N algorithm is based on feature similarity.', 'start': 21664.307, 'duration': 4.647}, {'end': 21673.48, 'text': 'Choosing the right value of K is a process called parameter tuning.', 'start': 21669.174, 'duration': 4.306}], 'summary': 'Knn algorithm uses k=5 for classifying data points based on majority voting from its 5 nearest neighbors.', 'duration': 41.324, 'max_score': 21632.156, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE21632156.jpg'}], 'start': 21328.283, 'title': 'Model accuracy and k nearest neighbors algorithm', 'summary': 'Discusses model accuracy, emphasizing a 93% accuracy rate and the process of deploying the model through scripting, and provides an overview of the k nearest neighbors (knn) algorithm, covering its fundamentals, use cases, working mechanism, and a detailed use case of predicting diabetes in python with a dataset of 768 people.', 'chapters': [{'end': 21441.376, 'start': 21328.283, 'title': 'Model accuracy and scripting', 'summary': 'Discusses model accuracy, highlighting a 93% accuracy rate and the process of deploying the model through scripting, emphasizing the importance of using double brackets when processing data and showcasing the prediction of two flowers.', 'duration': 113.093, 'highlights': ['The model accuracy is 93%, calculated by dividing the number of accurate predictions (30) by the total predictions (32) and multiplying by 100.', 'The process of deploying the model through scripting involves mapping the predictions to the names, using double brackets when processing data, and showcasing the prediction of two flowers.', 'The chapter emphasizes the importance of using double brackets when processing data for deployment, and showcases the prediction of two flowers, both measured as Versicolor.']}, {'end': 22143.763, 'start': 21441.656, 'title': 'Understanding k nearest neighbors algorithm', 'summary': 'Provides an overview of the k nearest neighbors (knn) algorithm, covering its fundamentals, use cases, working mechanism, and the process of choosing the factor k. it also includes a detailed use case of predicting diabetes in python with a dataset of 768 people, demonstrating the practical application of knn.', 'duration': 702.107, 'highlights': ['Understanding KNN Algorithm KNN is a fundamental place to start in machine learning, used for classification, and is easy to understand and incorporate into different forms of machine learning. It is a basis for various other machine learning models.', 'Choosing the Factor K The process of choosing the factor K in the KNN algorithm is essential for better accuracy, and the most common practice is to use the square root of N with the k value being an odd number. This helps to avoid bias and processing issues.', 'Working Mechanism of KNN The KNN algorithm classifies a data point based on the classification of its nearest neighbors, using a similarity measure and Euclidean distance calculation. It is suitable for smaller datasets and labeled data without significant noise.', 'Practical Use Case in Python The chapter demonstrates a practical use case of using KNN to predict diabetes in Python with a dataset of 768 people. It showcases the process of importing tools, exploring the dataset, and applying KNN for classification.']}], 'duration': 815.48, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE21328283.jpg', 'highlights': ['The model accuracy is 93%, calculated by dividing the number of accurate predictions (30) by the total predictions (32) and multiplying by 100.', 'Understanding KNN Algorithm KNN is a fundamental place to start in machine learning, used for classification, and is easy to understand and incorporate into different forms of machine learning.', 'Practical Use Case in Python The chapter demonstrates a practical use case of using KNN to predict diabetes in Python with a dataset of 768 people. It showcases the process of importing tools, exploring the dataset, and applying KNN for classification.', 'Choosing the Factor K The process of choosing the factor K in the KNN algorithm is essential for better accuracy, and the most common practice is to use the square root of N with the k value being an odd number. This helps to avoid bias and processing issues.', 'Working Mechanism of KNN The KNN algorithm classifies a data point based on the classification of its nearest neighbors, using a similarity measure and Euclidean distance calculation. It is suitable for smaller datasets and labeled data without significant noise.']}, {'end': 23195.29, 'segs': [{'end': 22256.858, 'src': 'embed', 'start': 22227.219, 'weight': 7, 'content': [{'end': 22228.281, 'text': 'And then the actual tool.', 'start': 22227.219, 'duration': 1.062}, {'end': 22231.245, 'text': "This is the kNeighbors classifier we're going to use.", 'start': 22228.501, 'duration': 2.744}, {'end': 22237.505, 'text': 'And finally the last three our three tools to test, all about testing our model.', 'start': 22232.386, 'duration': 5.119}, {'end': 22239.607, 'text': 'How good is it? We just put down test on there.', 'start': 22237.545, 'duration': 2.062}, {'end': 22243.529, 'text': 'And we have our confusion matrix, our F1 score, and our accuracy.', 'start': 22239.727, 'duration': 3.802}, {'end': 22252.035, 'text': "So we have our two general Python modules we're importing, and then we have our six modules specific from the sklearn setup.", 'start': 22243.729, 'duration': 8.306}, {'end': 22256.858, 'text': 'And then we do need to go ahead and run this so that these are actually imported.', 'start': 22252.555, 'duration': 4.303}], 'summary': 'Using kneighbors classifier and testing with confusion matrix, f1 score, and accuracy.', 'duration': 29.639, 'max_score': 22227.219, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE22227219.jpg'}, {'end': 22336.464, 'src': 'embed', 'start': 22305.868, 'weight': 6, 'content': [{'end': 22308.531, 'text': 'And then we want to take a look at the actual data set.', 'start': 22305.868, 'duration': 2.663}, {'end': 22312.434, 'text': "And since we're in pandas, we can simply do data set head.", 'start': 22308.551, 'duration': 3.883}, {'end': 22314.976, 'text': "And again, let's go ahead and add the print in there.", 'start': 22312.754, 'duration': 2.222}, {'end': 22321.943, 'text': 'If you put a bunch of these in a row, the data set one head, data set two head, it only prints out the last one.', 'start': 22315.657, 'duration': 6.286}, {'end': 22324.185, 'text': 'So I always like to keep the print statement in there.', 'start': 22322.183, 'duration': 2.002}, {'end': 22330.561, 'text': "Because most projects only use one data frame, Panda's data frame, doing it this way doesn't really matter.", 'start': 22324.957, 'duration': 5.604}, {'end': 22331.801, 'text': 'The other way works just fine.', 'start': 22330.621, 'duration': 1.18}, {'end': 22336.464, 'text': 'And you can see when we hit the Run button, we have the 768 lines, which we knew.', 'start': 22332.121, 'duration': 4.343}], 'summary': 'Using pandas to display data set head, 768 lines printed.', 'duration': 30.596, 'max_score': 22305.868, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE22305868.jpg'}, {'end': 22825.409, 'src': 'embed', 'start': 22796.599, 'weight': 0, 'content': [{'end': 22798.16, 'text': "And let's just take one away and we'll make it 11.", 'start': 22796.599, 'duration': 1.561}, {'end': 22799.861, 'text': 'Let me delete this out of here.', 'start': 22798.16, 'duration': 1.701}, {'end': 22804.305, 'text': "That's one of the reasons I love Jupyter Notebook because you can flip around and do all kinds of things on the fly.", 'start': 22800.001, 'duration': 4.304}, {'end': 22806.166, 'text': "So we'll go ahead and put in our classifier.", 'start': 22804.325, 'duration': 1.841}, {'end': 22807.687, 'text': "We're creating our classifier now.", 'start': 22806.186, 'duration': 1.501}, {'end': 22810.049, 'text': "And it's going to be the kNeighborsClassifier.", 'start': 22807.887, 'duration': 2.162}, {'end': 22811.783, 'text': 'nNeighbors equals 11.', 'start': 22810.269, 'duration': 1.514}, {'end': 22815.885, 'text': 'Remember we did 12 minus 1 for 11, so we have an odd number of neighbors.', 'start': 22811.783, 'duration': 4.102}, {'end': 22822.808, 'text': "P equals 2, because we're looking for are they diabetic or not, and we're using the Euclidean metric.", 'start': 22816.345, 'duration': 6.463}, {'end': 22825.409, 'text': 'There are other means of measuring the distance.', 'start': 22823.088, 'duration': 2.321}], 'summary': 'Using kneighborsclassifier with 11 neighbors for diabetes prediction.', 'duration': 28.81, 'max_score': 22796.599, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE22796599.jpg'}, {'end': 23201.555, 'src': 'embed', 'start': 23176.635, 'weight': 2, 'content': [{'end': 23182.161, 'text': "We have the young child going, Dad, is that a group of crocodiles or alligators? Well, that's hard to differentiate.", 'start': 23176.635, 'duration': 5.526}, {'end': 23188.245, 'text': 'And zoos are a great place to start looking at science and understanding how things work, especially as a young child.', 'start': 23182.501, 'duration': 5.744}, {'end': 23193.049, 'text': 'And so we can see the parents sitting here thinking well, what is the difference between a crocodile and an alligator?', 'start': 23188.465, 'duration': 4.584}, {'end': 23195.29, 'text': 'Well, one crocodiles are larger in size.', 'start': 23193.149, 'duration': 2.141}, {'end': 23197.172, 'text': 'Alligators are smaller in size.', 'start': 23195.631, 'duration': 1.541}, {'end': 23199.714, 'text': 'Snout width, the crocodiles have a narrow snout.', 'start': 23197.452, 'duration': 2.262}, {'end': 23201.555, 'text': 'And alligators have a wider snout.', 'start': 23199.854, 'duration': 1.701}], 'summary': 'Zoos are great for learning; crocodiles are larger, alligators are smaller, with different snout widths.', 'duration': 24.92, 'max_score': 23176.635, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE23176635.jpg'}], 'start': 22144.084, 'title': 'Implementing knn model for diabetes prediction', 'summary': 'Outlines the process of implementing a knn model to predict diabetes, including data preprocessing, importing necessary libraries, utilizing train-test split, and achieving 80% accuracy in creating a k-nearest neighbor model.', 'chapters': [{'end': 22227.219, 'start': 22144.084, 'title': 'Implementing knn model for diabetes prediction', 'summary': 'Outlines the process of implementing a knn model to predict diabetes, including data preprocessing, importing necessary libraries, and utilizing train-test split.', 'duration': 83.135, 'highlights': ['Introduction to data preprocessing and importing libraries, such as pandas and numpy, for implementing a KNN model.', "Explanation of train-test split and its importance in evaluating the model's performance.", 'Utilizing standard scalar pre-processor to normalize data and avoid bias due to large numbers in the dataset.']}, {'end': 22500.323, 'start': 22227.219, 'title': 'Data preprocessing in python', 'summary': 'Covers the process of importing and preprocessing a dataset using python and pandas, including replacing zero values and handling missing data through mean replacement, with a focus on the diabetes dataset and related python libraries.', 'duration': 273.104, 'highlights': ['The chapter covers the process of importing and preprocessing a dataset using Python and Pandas. It involves importing and preprocessing a dataset using Python and Pandas.', 'Handling missing data through mean replacement is a key focus of the chapter. The process involves handling missing data through mean replacement.', 'The chapter focuses on replacing zero values within the dataset. There is a focus on replacing zero values within the dataset.']}, {'end': 23195.29, 'start': 22500.663, 'title': 'Data preprocessing and support vector machine in python', 'summary': 'Introduces data preprocessing techniques such as data set printing, data splitting, and data scaling. it then delves into creating a k-nearest neighbor model with 80% accuracy, followed by an explanation of support vector machine and its advantages in high-dimensional space and regularization.', 'duration': 694.627, 'highlights': ['Creating a K-nearest neighbor model with 80% accuracy The chapter covers the creation of a K-nearest neighbor model with an accuracy score of 80%, achieved by preprocessing the data and using the KNeighborsClassifier.', 'Explanation of Support Vector Machine and its advantages The section explains the advantages of Support Vector Machine, including its capability to automatically handle high-dimensional space, such as sparse document vectors, and its ability to avoid overfitting and bias problems in other algorithms.', 'Introduction of data preprocessing techniques The chapter introduces various data preprocessing techniques, such as data set printing, data splitting, and data scaling, essential for preparing data before model training.']}], 'duration': 1051.206, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE22144084.jpg', 'highlights': ['Creating a K-nearest neighbor model with 80% accuracy achieved by preprocessing the data and using the KNeighborsClassifier.', 'Introduction to data preprocessing and importing libraries, such as pandas and numpy, for implementing a KNN model.', "Explanation of train-test split and its importance in evaluating the model's performance.", 'Utilizing standard scalar pre-processor to normalize data and avoid bias due to large numbers in the dataset.', 'Explanation of Support Vector Machine and its advantages, including its capability to automatically handle high-dimensional space and avoid overfitting and bias problems in other algorithms.', 'Introduction of data preprocessing techniques, such as data set printing, data splitting, and data scaling, essential for preparing data before model training.', 'Handling missing data through mean replacement is a key focus of the chapter.', 'The chapter focuses on replacing zero values within the dataset.']}, {'end': 25928.835, 'segs': [{'end': 23254.791, 'src': 'embed', 'start': 23228.613, 'weight': 7, 'content': [{'end': 23235.918, 'text': "So here we arrive in our actual coding, and I'm going to move this into a Python editor in just a moment,", 'start': 23228.613, 'duration': 7.305}, {'end': 23237.72, 'text': "but let's talk a little bit about what we're going to cover.", 'start': 23235.918, 'duration': 1.802}, {'end': 23243.344, 'text': "First we're going to cover in the code the setup, how to actually create our SVM,", 'start': 23237.98, 'duration': 5.364}, {'end': 23246.266, 'text': "and you're going to find that there's only two lines of code that actually create it,", 'start': 23243.344, 'duration': 2.922}, {'end': 23250.389, 'text': "and the rest of it is done so quick and fast that it's all here in the first page.", 'start': 23246.266, 'duration': 4.123}, {'end': 23254.791, 'text': "And we'll show you what that looks like as far as our data because we're going to create some data.", 'start': 23250.709, 'duration': 4.082}], 'summary': 'Covering setup and creation of svm in python with only two lines of code.', 'duration': 26.178, 'max_score': 23228.613, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE23228613.jpg'}, {'end': 23333.265, 'src': 'embed', 'start': 23300.086, 'weight': 3, 'content': [{'end': 23301.387, 'text': "Let's go ahead and put our code in there.", 'start': 23300.086, 'duration': 1.301}, {'end': 23309.61, 'text': "One of the things I like about the Jupyter Notebook is I can go up to view and I'm going to go ahead and toggle the line numbers on to make it a little bit easier to talk about.", 'start': 23301.407, 'duration': 8.203}, {'end': 23316.834, 'text': "We can even increase the size because this is edited in, in this case I'm using Google Chrome Explorer and that's how it opens up for the editor.", 'start': 23310.051, 'duration': 6.783}, {'end': 23319.535, 'text': 'Although anyone, like I said, any editor will work.', 'start': 23317.074, 'duration': 2.461}, {'end': 23321.676, 'text': 'Now the first step is going to be our imports.', 'start': 23319.835, 'duration': 1.841}, {'end': 23324.198, 'text': "And we're going to import four different parts.", 'start': 23322.237, 'duration': 1.961}, {'end': 23333.265, 'text': 'The first two I want you to look at are line one and line two are numpy as np and matplotlibrary.pyplot as plt.', 'start': 23324.458, 'duration': 8.807}], 'summary': 'Using jupyter notebook to edit code, import numpy and matplotlib.pyplot.', 'duration': 33.179, 'max_score': 23300.086, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE23300086.jpg'}, {'end': 23438.305, 'src': 'embed', 'start': 23410.005, 'weight': 4, 'content': [{'end': 23412.547, 'text': 'you can create this blob and it makes it real easy to use.', 'start': 23410.005, 'duration': 2.542}, {'end': 23418.131, 'text': 'And finally we have our actual SVM, the sklearn import SVM on line 3.', 'start': 23412.907, 'duration': 5.224}, {'end': 23419.792, 'text': 'So that covers all our imports.', 'start': 23418.131, 'duration': 1.661}, {'end': 23426.256, 'text': "We're going to create remember, I used the make blobs to create data and we're going to create a capital X and a lowercase.", 'start': 23420.092, 'duration': 6.164}, {'end': 23429.018, 'text': 'y equals make blobs in samples equals 40..', 'start': 23426.256, 'duration': 2.762}, {'end': 23431.1, 'text': "So we're going to make 40 lines of data.", 'start': 23429.018, 'duration': 2.082}, {'end': 23434.662, 'text': "It's going to have two centers with a random state equals 20.", 'start': 23431.4, 'duration': 3.262}, {'end': 23438.305, 'text': 'So each group is going to have 20 different pieces of data in it.', 'start': 23434.662, 'duration': 3.643}], 'summary': 'Using make_blobs to create 40 lines of data with 2 centers and 20 pieces of data in each group.', 'duration': 28.3, 'max_score': 23410.005, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE23410005.jpg'}, {'end': 23500.673, 'src': 'embed', 'start': 23475.646, 'weight': 2, 'content': [{'end': 23482.888, 'text': "But for this thing, linear, because it's a very simple linear example, we only have the two dimensions, and it'll be a nice linear hyperplane.", 'start': 23475.646, 'duration': 7.242}, {'end': 23485.889, 'text': "It'll be a nice linear line instead of a full plane.", 'start': 23483.108, 'duration': 2.781}, {'end': 23487.75, 'text': "So we're not dealing with a huge amount of data.", 'start': 23485.929, 'duration': 1.821}, {'end': 23492.611, 'text': 'And then all we have to do is do clf.fit x, y.', 'start': 23487.99, 'duration': 4.621}, {'end': 23493.231, 'text': "And that's it.", 'start': 23492.611, 'duration': 0.62}, {'end': 23496.632, 'text': "clf has been created, and then we're going to go ahead and display it.", 'start': 23493.471, 'duration': 3.161}, {'end': 23500.673, 'text': "And I'm going to talk about this display here in just a second, but let me go ahead and run this code.", 'start': 23497.052, 'duration': 3.621}], 'summary': 'Using a simple linear example with two dimensions, we train a classifier with a small amount of data and display the results.', 'duration': 25.027, 'max_score': 23475.646, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE23475646.jpg'}, {'end': 23659.917, 'src': 'embed', 'start': 23633.695, 'weight': 6, 'content': [{'end': 23639.819, 'text': 'In this case, I am giving it a width and length 3, 4 and a width and length 5, 6.', 'start': 23633.695, 'duration': 6.124}, {'end': 23644.764, 'text': 'And note that I put the data as a set of brackets and then I have the brackets inside.', 'start': 23639.82, 'duration': 4.944}, {'end': 23651.609, 'text': "And the reason I do that is because when we're looking at data, it's designed to process a large amount of data coming in.", 'start': 23644.944, 'duration': 6.665}, {'end': 23653.751, 'text': "We don't want to just process one line at a time.", 'start': 23651.689, 'duration': 2.062}, {'end': 23659.917, 'text': "And so in this case I'm processing two lines and then I'm just going to print and you'll see clf.predict new data.", 'start': 23654.071, 'duration': 5.846}], 'summary': 'Processing sets of data with width, length 3, 4 and 5, 6 for efficient prediction.', 'duration': 26.222, 'max_score': 23633.695, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE23633695.jpg'}, {'end': 23728.706, 'src': 'embed', 'start': 23692.734, 'weight': 1, 'content': [{'end': 23696.657, 'text': "We're going to spend more work on this than we did actually generating the original model.", 'start': 23692.734, 'duration': 3.923}, {'end': 23701.981, 'text': "And you'll see here that we go through a few steps, and I'll move this over to our editor in just a second.", 'start': 23696.877, 'duration': 5.104}, {'end': 23704.363, 'text': 'We come in, we create our original data.', 'start': 23702.361, 'duration': 2.002}, {'end': 23709.907, 'text': "It's exactly identical to the first part, and I'll explain why we redid that and show you how not to redo that.", 'start': 23704.523, 'duration': 5.384}, {'end': 23712.93, 'text': "And then we're going to go in there and add in those lines.", 'start': 23710.247, 'duration': 2.683}, {'end': 23716.092, 'text': "We're going to see what those lines look like and how to set those up.", 'start': 23713.35, 'duration': 2.742}, {'end': 23719.236, 'text': "And finally, we're going to plot all that on here and show it.", 'start': 23716.693, 'duration': 2.543}, {'end': 23725.082, 'text': "And you'll get a nice graph with what we saw earlier when we were going through the theory behind this,", 'start': 23719.496, 'duration': 5.586}, {'end': 23728.706, 'text': 'where it shows the support vectors and the hyperplane.', 'start': 23725.082, 'duration': 3.624}], 'summary': 'The process involves several steps, including data creation and visualization, to generate a graph with support vectors and a hyperplane.', 'duration': 35.972, 'max_score': 23692.734, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE23692734.jpg'}, {'end': 23777.314, 'src': 'embed', 'start': 23749.597, 'weight': 12, 'content': [{'end': 23752.078, 'text': "And you'll see that the plot show has moved down below.", 'start': 23749.597, 'duration': 2.481}, {'end': 23753.479, 'text': "Let's scroll up a little bit.", 'start': 23752.178, 'duration': 1.301}, {'end': 23760.941, 'text': 'And if you look at the top here of our new section, 1, 2, 3, and 4 is the same code we had before.', 'start': 23753.879, 'duration': 7.062}, {'end': 23763.182, 'text': "And let's go back up here and take a look at that.", 'start': 23761.221, 'duration': 1.961}, {'end': 23767.803, 'text': "We're going to fit the values on our SVM and then we're going to plot scatter it.", 'start': 23763.68, 'duration': 4.123}, {'end': 23769.684, 'text': "And then we're going to do a plot show.", 'start': 23768.243, 'duration': 1.441}, {'end': 23777.314, 'text': "So you should be asking, why are we redoing the same code? Well, when you do the plot show, that blanks out what's in the plot.", 'start': 23769.704, 'duration': 7.61}], 'summary': "The transcript discusses code execution, including fitting values on svm and plotting scatter graphs, while addressing the impact of 'plot show' function.", 'duration': 27.717, 'max_score': 23749.597, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE23749597.jpg'}, {'end': 24049.417, 'src': 'embed', 'start': 24024.426, 'weight': 9, 'content': [{'end': 24030.049, 'text': "we've labeled it to three different areas, and we reshaped it, and we've just taken 30 points in each direction.", 'start': 24024.426, 'duration': 5.623}, {'end': 24034.09, 'text': "If you do the math, you have 30 times 30, so that's 900 points of data,", 'start': 24030.329, 'duration': 3.761}, {'end': 24037.692, 'text': 'and we separated it between the three lines and reshaped it to fit those three lines.', 'start': 24034.09, 'duration': 3.602}, {'end': 24043.475, 'text': "We can then go back to our map plot library, where we've created the AX, and we're going to create a contour.", 'start': 24038.032, 'duration': 5.443}, {'end': 24047.376, 'text': "And you'll see here we have contour, capital XX, capital YY.", 'start': 24043.615, 'duration': 3.761}, {'end': 24049.417, 'text': 'These have been reshaped to fit those lines.', 'start': 24047.576, 'duration': 1.841}], 'summary': 'Reshaped data into 3 lines, yielding 900 points for contour creation.', 'duration': 24.991, 'max_score': 24024.426, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE24024426.jpg'}, {'end': 24262.324, 'src': 'embed', 'start': 24234.68, 'weight': 10, 'content': [{'end': 24237.563, 'text': "So let's start with how linear regression works.", 'start': 24234.68, 'duration': 2.883}, {'end': 24244.909, 'text': 'A linear regression finds the line that best fits the data point and gives a relationship between the two variables.', 'start': 24237.603, 'duration': 7.306}, {'end': 24250.934, 'text': 'And so you can see here we have the efficiency of the car versus the distance traveled.', 'start': 24245.99, 'duration': 4.944}, {'end': 24253.816, 'text': 'And you can see this nice straight line drawn through there.', 'start': 24251.535, 'duration': 2.281}, {'end': 24259.581, 'text': "And when you talk about multiple variables, all you're doing is putting this instead of a line, it now becomes a plane.", 'start': 24254.577, 'duration': 5.004}, {'end': 24262.324, 'text': 'It gets a little more complicated with multiple variables,', 'start': 24260.402, 'duration': 1.922}], 'summary': 'Linear regression finds best-fit line for two variables, extends to plane with multiple variables.', 'duration': 27.644, 'max_score': 24234.68, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE24234680.jpg'}, {'end': 24963.932, 'src': 'embed', 'start': 24943.24, 'weight': 5, 'content': [{'end': 24953.304, 'text': 'Regularization techniques are used to calibrate the linear regression models and to minimize the adjusted loss function and prevent overfitting or underfitting.', 'start': 24943.24, 'duration': 10.064}, {'end': 24959.848, 'text': "So what that means, in this case, we're going to go ahead and take a look at a couple different things.", 'start': 24955.444, 'duration': 4.404}, {'end': 24963.932, 'text': "We're going to look at regularization, which we'll start with a linear model.", 'start': 24959.868, 'duration': 4.064}], 'summary': 'Regularization minimizes loss function to avoid over/underfitting in linear regression.', 'duration': 20.692, 'max_score': 24943.24, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE24943240.jpg'}, {'end': 25789.528, 'src': 'embed', 'start': 25762.675, 'weight': 0, 'content': [{'end': 25771.799, 'text': "We're doing an X bar based on the columns and our L regress coefficients, color equals color, X, spine, bottom, and so forth.", 'start': 25762.675, 'duration': 9.124}, {'end': 25774.32, 'text': 'So it just puts together a nice little graph.', 'start': 25772.839, 'duration': 1.481}, {'end': 25784.324, 'text': "And you're starting to see, one, when you compare this, if you put it on the same graph as this one up here, this is up here at minus 18.", 'start': 25775.976, 'duration': 8.348}, {'end': 25786.545, 'text': 'This is at minus 9.', 'start': 25784.324, 'duration': 2.221}, {'end': 25789.528, 'text': 'And so this graph is half the size of the graph above.', 'start': 25786.545, 'duration': 2.983}], 'summary': 'X bar graph is half the size of the previous one.', 'duration': 26.853, 'max_score': 25762.675, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE25762675.jpg'}], 'start': 23195.631, 'title': 'Machine learning models and techniques', 'summary': 'Covers the implementation of support vector machine for differentiating between crocodiles and alligators, linear regression, regularization, overfitting, underfitting, model optimization, lasso and ridge regression models, and multiple linear regression using practical examples and emphasizing the importance of data cleaning, adequate training data size, and dimensionality reduction techniques.', 'chapters': [{'end': 24141.821, 'start': 23195.631, 'title': 'Support vector machine in python', 'summary': 'Demonstrates the process of creating a support vector machine in python using a small dataset to differentiate between crocodiles and alligators, requiring minimal code and visualizing the results in a detailed graph.', 'duration': 946.19, 'highlights': ['The chapter demonstrates the process of creating a Support Vector Machine in Python using a small dataset to differentiate between crocodiles and alligators The transcript explains the step-by-step process of setting up the Support Vector Machine using a small dataset to classify between crocodiles and alligators.', 'The process requires minimal code, with only two lines needed to create the Support Vector Machine It is highlighted that the majority of the code for creating the Support Vector Machine is done quickly with only two lines of code, showcasing the simplicity and efficiency of the process.', 'The results are visualized in a detailed graph, demonstrating the clear separation of the two groups A detailed explanation of visualizing the results in a graph is provided, showcasing the clear differentiation between crocodiles and alligators based on the width and length of their snouts.']}, {'end': 24705.857, 'start': 24141.841, 'title': 'Linear regression and regularization', 'summary': 'Discusses the concepts of linear regression, bias, variance, overfitting, underfitting, and regularization, emphasizing their importance in machine learning and providing practical examples.', 'duration': 564.016, 'highlights': ['Linear regression finds the line that best fits the data points and gives a relationship between the two variables, such as the example of efficiency of a car versus distance traveled. Example of linear regression with efficiency of car versus distance traveled.', 'The concept of bias occurs when an algorithm has limited flexibility to learn from data, leading to oversimplified models and similar trends in validation and training errors. Explanation of bias and its impact on model flexibility and error trends.', 'Overfitting occurs when the machine learning model tries to learn from details and noise, resulting in an attempt to fit each data point on the curve, leading to a lack of averaging and consideration for variance. Description of overfitting and its impact on data fitting and consideration for variance.']}, {'end': 24987.482, 'start': 24705.857, 'title': 'Overfitting, underfitting, and model optimization', 'summary': 'Discusses the issues of overfitting and underfitting in machine learning models, highlighting the causes and effects of each, emphasizing the importance of data cleaning and adequate training data size, and introducing regularization techniques to prevent overfitting or underfitting.', 'duration': 281.625, 'highlights': ['Regularization techniques are used to calibrate the linear regression models and to minimize the adjusted loss function and prevent overfitting or underfitting. Regularization techniques are crucial in minimizing adjusted loss function and preventing overfitting or underfitting in linear regression models.', 'The chapter emphasizes the importance of data cleaning and addressing issues with the data source, such as variations in measurement tools or data input from different sources. Emphasis on the importance of data cleaning and addressing variations in data sources, including measurement tools and data input from different sources.', "The size of the training data used is highlighted as a crucial factor affecting the model's performance, with both inadequate and excessive data leading to problems. The size of the training data is emphasized as a crucial factor affecting model performance, with inadequate or excessive data causing issues."]}, {'end': 25283.023, 'start': 24987.482, 'title': 'Lasso and ridge regression models', 'summary': 'Discusses the concepts of ridge and lasso regression models, emphasizing their cost functions and the comparison of their fits, with an example demonstrating the preference for lasso regression with fewer variables and ridge regression with many variables.', 'duration': 295.541, 'highlights': ['The lasso and ridge regression models are compared based on their fits to the data, with lasso regression found to fit the model more accurately than the linear regression line, leading to a preference for these models in various data scenarios.', 'Ridge regularization is useful when dealing with many variables and relatively smaller data samples, as it prevents overfitting and encourages convergence of coefficients towards zero.', 'Lasso regularization is preferred when fitting a linear model with fewer variables, as it encourages coefficients of the variables to go towards zero, making it suitable for scenarios with a limited number of variables.']}, {'end': 25928.835, 'start': 25283.063, 'title': 'Implementing multiple linear regression model', 'summary': 'Explains the process of implementing a multiple linear regression model using the boston data set, including data loading, splitting, model fitting, prediction, mean square error calculation, and visualization of coefficient scores, and then explores dimensionality reduction techniques, demonstrating the reduction of input variables.', 'duration': 645.772, 'highlights': ['Implementing Multiple Linear Regression Model The chapter demonstrates the process of implementing a multiple linear regression model using the Boston data set, including data loading, splitting, model fitting, prediction, and mean square error calculation, providing insights into model performance.', "Visualization of Coefficient Scores The chapter provides a visualization of coefficient scores, highlighting the importance of coefficients and their impact on the model's prediction, with specific focus on the NOX coefficient and the changes observed during ridge and lasso regression.", 'Dimensionality Reduction Techniques The chapter explores dimensionality reduction techniques, emphasizing the importance of reducing the number of input variables in a data set, with a practical example demonstrating the orders made at an automobile parts retailer and the reduction of input variables for analysis.']}], 'duration': 2733.204, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE23195631.jpg', 'highlights': ['The chapter demonstrates the process of creating a Support Vector Machine in Python using a small dataset to differentiate between crocodiles and alligators.', 'The lasso and ridge regression models are compared based on their fits to the data, with lasso regression found to fit the model more accurately than the linear regression line, leading to a preference for these models in various data scenarios.', 'Regularization techniques are used to calibrate the linear regression models and to minimize the adjusted loss function and prevent overfitting or underfitting.', 'The chapter emphasizes the importance of data cleaning and addressing issues with the data source, such as variations in measurement tools or data input from different sources.', 'The size of the training data is emphasized as a crucial factor affecting model performance, with inadequate or excessive data causing issues.', 'The process requires minimal code, with only two lines needed to create the Support Vector Machine.', 'Linear regression finds the line that best fits the data points and gives a relationship between the two variables, such as the example of efficiency of a car versus distance traveled.', 'The concept of bias occurs when an algorithm has limited flexibility to learn from data, leading to oversimplified models and similar trends in validation and training errors.', 'Overfitting occurs when the machine learning model tries to learn from details and noise, resulting in an attempt to fit each data point on the curve, leading to a lack of averaging and consideration for variance.', "The chapter provides a visualization of coefficient scores, highlighting the importance of coefficients and their impact on the model's prediction, with specific focus on the NOX coefficient and the changes observed during ridge and lasso regression.", 'The chapter explores dimensionality reduction techniques, emphasizing the importance of reducing the number of input variables in a data set, with a practical example demonstrating the orders made at an automobile parts retailer and the reduction of input variables for analysis.', 'Ridge regularization is useful when dealing with many variables and relatively smaller data samples, as it prevents overfitting and encourages convergence of coefficients towards zero.', 'Lasso regularization is preferred when fitting a linear model with fewer variables, as it encourages coefficients of the variables to go towards zero, making it suitable for scenarios with a limited number of variables.', 'The results are visualized in a detailed graph, demonstrating the clear separation of the two groups.']}, {'end': 27662.116, 'segs': [{'end': 26016.627, 'src': 'embed', 'start': 25929.355, 'weight': 0, 'content': [{'end': 25936.919, 'text': 'In order to predict the future cells, we find out that using correlation analysis that we just need three attributes.', 'start': 25929.355, 'duration': 7.564}, {'end': 25941.581, 'text': 'Therefore, we have reduced the number of attributes from five to three.', 'start': 25937.479, 'duration': 4.102}, {'end': 25944.603, 'text': "And clearly, we don't really care about the part number.", 'start': 25942.442, 'duration': 2.161}, {'end': 25948.485, 'text': "I don't think the part number would have an effect on how many tires are bought.", 'start': 25944.883, 'duration': 3.602}, {'end': 25952.867, 'text': "And even the store, who's buying them, probably does not have an effect on that.", 'start': 25949.645, 'duration': 3.222}, {'end': 25955.228, 'text': "In this case, that's what they've actually done is remove those.", 'start': 25952.947, 'duration': 2.281}, {'end': 25958.89, 'text': 'And we just have the item, the tire, the price, and the quantity.', 'start': 25955.728, 'duration': 3.162}, {'end': 25966.914, 'text': 'One of the things you should be taking away from this is in the scheme of things, we are in the descriptive phase.', 'start': 25959.29, 'duration': 7.624}, {'end': 25970.775, 'text': "We're describing the data and we're pre-processing the data.", 'start': 25966.934, 'duration': 3.841}, {'end': 25972.476, 'text': 'What can we do to clean it up?', 'start': 25971.215, 'duration': 1.261}, {'end': 25974.797, 'text': 'Why dimensionality reduction??', 'start': 25973.156, 'duration': 1.641}, {'end': 25980.72, 'text': 'Well, number one less dimensions for a given data set means less computation or training time.', 'start': 25975.577, 'duration': 5.143}, {'end': 25988.462, 'text': "That can be really important if you're trying a number of different models and you're rerunning them over and over again.", 'start': 25981.58, 'duration': 6.882}, {'end': 25993.644, 'text': 'And even if you have seven gigabytes of data, that can start taking days to go through all those different models.', 'start': 25988.502, 'duration': 5.142}, {'end': 25995.345, 'text': 'So this is huge.', 'start': 25994.524, 'duration': 0.821}, {'end': 25999.986, 'text': 'This is probably the hugest part as far as reducing our data set.', 'start': 25995.405, 'duration': 4.581}, {'end': 26004.848, 'text': 'Redundancy is removed after removing similar entries from the data set.', 'start': 26000.927, 'duration': 3.921}, {'end': 26009.841, 'text': 'Again, pre-processing some of our models like a neural network.', 'start': 26006.297, 'duration': 3.544}, {'end': 26015.266, 'text': 'if you put in two of the same data, it might give them a higher weight than they would if it was just once.', 'start': 26009.841, 'duration': 5.425}, {'end': 26016.627, 'text': 'We want to get rid of that redundancy.', 'start': 26015.286, 'duration': 1.341}], 'summary': 'Correlation analysis reduced attributes from 5 to 3, aiding in less computation and redundancy removal.', 'duration': 87.272, 'max_score': 25929.355, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE25929355.jpg'}], 'start': 25929.355, 'title': 'Importance of pca in dimensionality reduction', 'summary': 'Emphasizes the significance of dimensionality reduction in data analysis, showcasing how it reduces computation time, removes redundancy, and aids in data interpretation. it explains principal component analysis (pca) and its role in reducing dimensions of data sets, illustrated with an example of the iris data set. additionally, it covers setting up pca for data visualization and demonstrates using pca for dimensionality reduction, compressing 30 features to two principal components.', 'chapters': [{'end': 26145.821, 'start': 25929.355, 'title': 'Dimensionality reduction importance', 'summary': 'Emphasizes the importance of dimensionality reduction in data analysis, highlighting how it reduces computation time, removes redundancy, and aids in data interpretation, leading to better human understanding and simplified visualization.', 'duration': 216.466, 'highlights': ['Dimensionality reduction reduces computation or training time, which is crucial for processing large data sets and running multiple models efficiently.', 'Removing redundancy in data sets enhances processing time and reduces space required to store the data, particularly beneficial for handling big data.', 'It simplifies data visualization in 2D and 3D plots, aiding in better human interpretation and providing a clearer and simplified version for stakeholders.', 'Principal Component Analysis (PCA) is described as a technique for reducing the dimensionality of data sets, increasing interpretability while minimizing information loss.']}, {'end': 26817.884, 'start': 26146.241, 'title': 'Understanding principal component analysis', 'summary': 'Explains principal component analysis (pca) and its importance in reducing dimensions of data sets, illustrated with an example of the iris data set. it highlights the mathematical operations involved in pca, such as standardization, covariance matrix computation, eigenvector and eigenvalue generation, and the construction of feature vectors.', 'duration': 671.643, 'highlights': ['PCA performs standardization to standardize the range of attributes, involving the removal of mean and scaling with respect to standard deviation. The main aim of standardization is to standardize the range of attributes so that each one of them lies within similar boundaries, involving the removal of the mean from the variable values and scaling the data with respect to the standard deviation.', 'Covariance matrix computation is used to express the correlation between attributes in multi-dimensional data sets, with entries as the variance and covariance of the attribute values. Covariance matrix computation is used to express the correlation between any two or more attributes in multi-dimensional data sets, with entries as the variance and covariance of the attribute values.', 'Eigenvectors and eigenvalues are generated from the covariance matrix, responsible for the creation of new variables and construction of principal components. Eigenvectors and eigenvalues are generated from the covariance matrix and are responsible for the creation of new variables from the old set of variables, which further lead to the construction of the principal components.', 'Feature vectors are used to determine whether to keep or discard less significant principal components, crucial for the PCA process. Feature vectors are used to decide whether to keep or discard less significant principal components, crucial for the PCA process.']}, {'end': 27254.417, 'start': 26818.545, 'title': 'Setting up pca for data visualization', 'summary': 'Introduces setting up pca for data visualization, using a cancer dataset with 35 different features, and visualizing the first two principal components with a single scatter plot, after scaling the data and keeping only two components for visualization.', 'duration': 435.872, 'highlights': ['The chapter introduces setting up PCA for data visualization. It covers the process of setting up PCA for data visualization using a cancer dataset with 35 different features.', 'Visualizing the first two principal components with a single scatter plot. It explains the process of visualizing the first two principal components of the scaled data using a single scatter plot.', 'Using a cancer dataset with 35 different features. The dataset used for the PCA setup contains 35 different features, making it suitable for the demonstration.', 'Keeping only two components for visualization. The PCA object is created with only two components to facilitate easy visualization, especially when demonstrating to others.', 'Scaling the data before applying PCA. The importance of scaling the data before applying PCA is emphasized, despite PCA already performing scaling internally.']}, {'end': 27662.116, 'start': 27254.698, 'title': 'Pca for dimensionality reduction', 'summary': "Discusses using pca for dimensionality reduction, demonstrating how 30 features can be compressed to two principal components, leading to a clear separation of classes and the challenge of interpreting the components' meanings.", 'duration': 407.418, 'highlights': ['Using PCA, 30 features are compressed down to two principal components, resulting in a clear separation of classes. The data is transformed to its first two principal components, compressing 30 features down to two, leading to a clear separation of classes.', 'Challenges of interpreting PCA components and the difficulty in understanding their representations are discussed. The chapter emphasizes the challenge of interpreting the meaning of the two principal components obtained through PCA and the difficulty in understanding their representations.', "Demonstrating the correlation between features and principal components using a seaborn heatmap with plasma coloring. A seaborn heatmap with plasma coloring is used to demonstrate the correlation between various features and the principal component, providing a visual representation of the data's correlation."]}], 'duration': 1732.761, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE25929355.jpg', 'highlights': ['Dimensionality reduction reduces computation or training time, crucial for processing large data sets.', 'Removing redundancy in data sets enhances processing time and reduces space required to store the data.', 'PCA simplifies data visualization in 2D and 3D plots, aiding in better human interpretation.', 'PCA is a technique for reducing the dimensionality of data sets, increasing interpretability while minimizing information loss.', 'PCA performs standardization to standardize the range of attributes, involving the removal of mean and scaling with respect to standard deviation.', 'Covariance matrix computation is used to express the correlation between attributes in multi-dimensional data sets.', 'Eigenvectors and eigenvalues are responsible for the creation of new variables and construction of principal components.', 'Feature vectors are crucial for deciding whether to keep or discard less significant principal components.', 'The chapter introduces setting up PCA for data visualization using a cancer dataset with 35 different features.', 'Visualizing the first two principal components with a single scatter plot aids in better understanding.', 'Using a cancer dataset with 35 different features is suitable for demonstrating PCA setup.', 'Keeping only two components for visualization facilitates easy interpretation for others.', 'Scaling the data before applying PCA is emphasized for better results.', 'Using PCA, 30 features are compressed down to two principal components, resulting in a clear separation of classes.', 'Challenges of interpreting PCA components and the difficulty in understanding their representations are discussed.', 'Demonstrating the correlation between features and principal components using a seaborn heatmap with plasma coloring.']}, {'end': 30772.166, 'segs': [{'end': 27752.472, 'src': 'embed', 'start': 27726.037, 'weight': 9, 'content': [{'end': 27731.819, 'text': 'And finally, we will discuss the various safety measures you can take to ensure you are safe and not attacked by coronavirus.', 'start': 27726.037, 'duration': 5.782}, {'end': 27733.9, 'text': 'So what is coronavirus?', 'start': 27732.719, 'duration': 1.181}, {'end': 27736.78, 'text': 'Coronavirus, or COVID-19,', 'start': 27734.4, 'duration': 2.38}, {'end': 27743.983, 'text': 'is an infectious disease caused by a newly discovered coronavirus that is believed to have emerged from a seafood market in Wuhan, China,', 'start': 27736.78, 'duration': 7.203}, {'end': 27746.55, 'text': 'during December 2019..', 'start': 27743.983, 'duration': 2.567}, {'end': 27752.472, 'text': "It is zoonotic, so it's a disease that can be transmitted from animals to people or, more specifically,", 'start': 27746.55, 'duration': 5.922}], 'summary': 'Covid-19 is a zoonotic disease that emerged in wuhan, china in december 2019.', 'duration': 26.435, 'max_score': 27726.037, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE27726037.jpg'}, {'end': 27807.45, 'src': 'embed', 'start': 27773.914, 'weight': 10, 'content': [{'end': 27778.196, 'text': 'It appears that symptoms show up in people within 14 days of exposure to the virus.', 'start': 27773.914, 'duration': 4.282}, {'end': 27784.358, 'text': "With that, now let's look at the various symptoms of COVID-19.", 'start': 27780.696, 'duration': 3.662}, {'end': 27793.541, 'text': 'A patient with coronavirus can show generic symptoms such as cough, fever, shortness of breath and muscle pain.', 'start': 27786.198, 'duration': 7.343}, {'end': 27798.343, 'text': 'They can also have a sore throat, headache and loss of taste or smell.', 'start': 27794.822, 'duration': 3.521}, {'end': 27807.45, 'text': 'They are also expected to have Middle East Respiratory Syndrome and Severe Acute Respiratory Syndrome.', 'start': 27799.445, 'duration': 8.005}], 'summary': 'Covid-19 symptoms can manifest within 14 days, including cough, fever, shortness of breath, muscle pain, sore throat, headache, and loss of taste or smell.', 'duration': 33.536, 'max_score': 27773.914, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE27773914.jpg'}, {'end': 27892.857, 'src': 'embed', 'start': 27821.538, 'weight': 0, 'content': [{'end': 27826.12, 'text': 'SARS is a contagious and sometimes fatal respiratory illness caused by a coronavirus.', 'start': 27821.538, 'duration': 4.582}, {'end': 27827.821, 'text': 'It appeared in 2009 in China.', 'start': 27826.901, 'duration': 0.92}, {'end': 27833.277, 'text': 'It spread worldwide within a few months, although it was quickly contained.', 'start': 27829.674, 'duration': 3.603}, {'end': 27841.564, 'text': 'SARS is a virus transmitted through droplets that enters the air when someone with the disease coughs, sneezes or talks.', 'start': 27834.258, 'duration': 7.306}, {'end': 27845.828, 'text': "Now, let's look at the impact of coronavirus worldwide.", 'start': 27842.705, 'duration': 3.123}, {'end': 27852.994, 'text': 'The maps and the charts that I am going to show you now have been taken from an organization called Our World in Data.', 'start': 27847.109, 'duration': 5.885}, {'end': 27862.4, 'text': 'They largely focus on problems that the world faces such as poverty, different diseases, hunger, climate change, existential crisis and inequality.', 'start': 27853.973, 'duration': 8.427}, {'end': 27869.685, 'text': "Their main goal is to research and use data to make progress against the world's largest problems.", 'start': 27864.561, 'duration': 5.124}, {'end': 27876.891, 'text': 'The map that you see on your screens shows the total number of confirmed COVID-19 cases till the 26th of April.', 'start': 27870.566, 'duration': 6.325}, {'end': 27881.515, 'text': 'Below, you can see the color scale ranging from 0 to 1 million.', 'start': 27878.292, 'duration': 3.223}, {'end': 27887.016, 'text': 'The countries with the least number of cases are marked in light orange color.', 'start': 27882.915, 'duration': 4.101}, {'end': 27892.857, 'text': 'The countries with cases between 5, 000 to 10, 000 cases have been depicted using orange color.', 'start': 27887.656, 'duration': 5.201}], 'summary': 'Sars is a contagious respiratory illness caused by coronavirus, appeared in 2009, and covid-19 impact worldwide.', 'duration': 71.319, 'max_score': 27821.538, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE27821538.jpg'}, {'end': 27957.58, 'src': 'embed', 'start': 27928.365, 'weight': 5, 'content': [{'end': 27935.229, 'text': 'In North America, the United States has the highest number of cases which is actually the highest throughout the world.', 'start': 27928.365, 'duration': 6.864}, {'end': 27939.151, 'text': 'In South America, Brazil has the maximum number of cases.', 'start': 27936.509, 'duration': 2.642}, {'end': 27947.135, 'text': 'The next map shows the confirmed cases of countries on the Asian continent.', 'start': 27943.513, 'duration': 3.622}, {'end': 27952.498, 'text': 'China, Iran, Turkey, India and Saudi Arabia have the highest number of cases.', 'start': 27948.776, 'duration': 3.722}, {'end': 27957.58, 'text': 'Then we have the map of Europe.', 'start': 27956.079, 'duration': 1.501}], 'summary': 'Us has highest covid-19 cases in north america, brazil in south america, and china, iran, turkey, india, and saudi arabia in asia.', 'duration': 29.215, 'max_score': 27928.365, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE27928365.jpg'}, {'end': 28096.29, 'src': 'embed', 'start': 28038.916, 'weight': 2, 'content': [{'end': 28044.218, 'text': 'Next, you can see a graph of a few countries and their total death cases till the 26th of April.', 'start': 28038.916, 'duration': 5.302}, {'end': 28050.1, 'text': 'You can see the United States, Spain, Italy, France, China and India here.', 'start': 28045.558, 'duration': 4.542}, {'end': 28054.402, 'text': 'We will see how to build a similar graph in our demo.', 'start': 28051.641, 'duration': 2.761}, {'end': 28063.338, 'text': 'Up next on your screens is the map of different countries and their fatality rate.', 'start': 28059.235, 'duration': 4.103}, {'end': 28068.523, 'text': 'Fatality rate is actually the ratio between the confirmed deaths and confirmed cases.', 'start': 28064.619, 'duration': 3.904}, {'end': 28084.508, 'text': 'France has the highest fatality rate with over 18.22% followed by the United Kingdom at 13.69% and Italy at 13.51%.', 'start': 28069.844, 'duration': 14.664}, {'end': 28088.929, 'text': 'Moving ahead, the next map shows the total number of COVID-19 test numbers.', 'start': 28084.508, 'duration': 4.421}, {'end': 28096.29, 'text': 'The United States has conducted over 5 million tests so far, followed by Russia with over 2.88 million tests.', 'start': 28089.949, 'duration': 6.341}], 'summary': 'Graph shows death cases; france has highest fatality rate; us conducted over 5m tests.', 'duration': 57.374, 'max_score': 28038.916, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE28038916.jpg'}, {'end': 28168.303, 'src': 'embed', 'start': 28141.917, 'weight': 12, 'content': [{'end': 28147.304, 'text': 'countries like australia, india, belgium, the united kingdom, china, the united states, france,', 'start': 28141.917, 'duration': 5.387}, {'end': 28153.591, 'text': 'spain and others already have announced lockdown for more than 40 days.', 'start': 28147.304, 'duration': 6.287}, {'end': 28161.02, 'text': "to make sure the lockdowns don't extend further and the situation improves, people are being advised to stay at home and avoid public gatherings.", 'start': 28153.591, 'duration': 7.429}, {'end': 28168.303, 'text': "Now let's look at where India stands with its battle for coronavirus.", 'start': 28164.341, 'duration': 3.962}], 'summary': 'Several countries, including india, have been under lockdown for over 40 days to combat the coronavirus, emphasizing the need for people to stay at home and avoid public gatherings.', 'duration': 26.386, 'max_score': 28141.917, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE28141917.jpg'}, {'end': 28422.649, 'src': 'embed', 'start': 28394.634, 'weight': 6, 'content': [{'end': 28399.538, 'text': 'The datasets are present in a GitHub repository maintained by Johns Hopkins University.', 'start': 28394.634, 'duration': 4.904}, {'end': 28404.863, 'text': 'To import the datasets, I have used the pandas library and read underscore CSV function.', 'start': 28400.219, 'duration': 4.644}, {'end': 28410.667, 'text': 'I provided the URL location of the files followed by the file name and the extension of the file which is .', 'start': 28405.763, 'duration': 4.904}, {'end': 28418.867, 'text': 'csv I have loaded these three datasets to three variable names.', 'start': 28410.667, 'duration': 8.2}, {'end': 28422.649, 'text': "Now let's run the cells to import all the three datasets.", 'start': 28419.647, 'duration': 3.002}], 'summary': 'Data from johns hopkins university on github imported using pandas library.', 'duration': 28.015, 'max_score': 28394.634, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE28394634.jpg'}], 'start': 27663.962, 'title': 'Covid-19 impact and analysis', 'summary': 'Discusses the global impact of coronavirus, including total cases, deaths, fatality rate, and tests conducted, as well as the use of svm and polynomial regression to predict upcoming cases. it also emphasizes the need for safety measures, data analysis using python, and visualization of covid-19 statistics.', 'chapters': [{'end': 27725.377, 'start': 27663.962, 'title': 'Coronavirus analysis and prediction', 'summary': 'Discusses the global impact of coronavirus, including total cases, deaths, fatality rate, and tests conducted, followed by an analysis using svm and polynomial regression to predict upcoming cases from april 28th to may 17th, based on data collected from january 22nd to april 27th.', 'duration': 61.415, 'highlights': ['The global impact of coronavirus is analyzed, including total cases, deaths, fatality rate, and tests conducted by different countries.', 'An analysis using SVM and polynomial regression in Python is performed to predict the number of upcoming cases from April 28th to May 17th, based on data collected from January 22nd to April 27th.', 'The chapter briefly covers what coronavirus is and the various symptoms, providing a comprehensive understanding of the virus.']}, {'end': 28253.523, 'start': 27726.037, 'title': 'Covid-19 impact & safety measures', 'summary': 'Discusses the impact and spread of covid-19 worldwide, highlighting the total confirmed cases, deaths, fatality rates, and testing numbers, and emphasizes the need for safety measures, including lockdowns and guidelines for prevention and control.', 'duration': 527.486, 'highlights': ['The United States has the highest number of COVID-19 cases, with over 800,000 cases as of April 26th, followed by other heavily affected countries like Italy, Spain, France, and the United Kingdom. The chapter provides a detailed overview of the total confirmed COVID-19 cases in various countries, emphasizing the severity of the outbreak and the global impact of the disease.', "The United States also reports the highest number of deaths, with over 53,000 fatalities, followed by Italy, France, the United Kingdom, and Germany. The transcript highlights the significant number of COVID-19 deaths in the United States and other heavily affected countries, underscoring the severity of the pandemic's impact on global public health.", "The fatality rate is particularly high in countries like France, the United Kingdom, and Italy, with rates exceeding 13%, indicating the severity of the disease's impact in these regions. The fatality rate data underscores the severity of COVID-19 in certain countries, highlighting the significant impact on public health and emphasizing the need for effective measures to control the spread and impact of the disease.", 'The United States has conducted over 5 million COVID-19 tests, followed by Russia with over 2.88 million tests, emphasizing the extensive testing efforts in these countries. The transcript highlights the significant number of COVID-19 tests conducted in the United States and Russia, emphasizing their proactive approach to testing and monitoring the spread of the disease.', 'The chapter emphasizes the effectiveness of lockdown measures in curbing the spread of COVID-19, citing examples of countries like China, the United States, and India that have enforced lockdowns for a considerable duration. The discussion of lockdowns underscores their effectiveness in controlling the spread of COVID-19, as evidenced by the examples of countries that have implemented these measures for an extended period.']}, {'end': 28641.894, 'start': 28253.523, 'title': 'Covid-19 data analysis', 'summary': 'Covers the use of python to visualize covid-19 data, including importing datasets from a github repository, implementing polynomial regression and support vector machines models, and extracting specific columns for further analysis.', 'duration': 388.371, 'highlights': ['The data covers the period from 22nd January to 28th April, and the analysis involves predicting the number of upcoming cases for the next 20 days using polynomial regression and support vector machines model in Python. Quantifiable data: Period covered (22nd Jan - 28th April), prediction of upcoming cases for the next 20 days.', 'Datasets utilized are obtained from the repository operated by the Johns Hopkins University Center of System Science and Engineering, which is regularly updated. Quantifiable data: Source of datasets, regular updates.', 'The process involves importing datasets using the pandas library and displaying the top five rows of each dataset to understand its structure and content. Quantifiable data: Displaying top five rows of each dataset for understanding.', 'The implementation includes importing necessary libraries for numerical computations, data manipulation, data analysis, and data visualizations, as well as specific functions for polynomial regression and support vector machines models. Quantifiable data: Importing libraries for analysis, visualization, and models.', 'Extracting specific columns for further analysis, such as date columns with information of confirmed cases, death cases, and recovered cases. Quantifiable data: Extraction of specific columns for further analysis.']}, {'end': 29948.56, 'start': 28641.894, 'title': 'Covid-19 data analysis and visualization', 'summary': 'Covers the creation of empty lists for global and country-wise covid-19 statistics, calculation of mortality and recovery rates, daily increase in cases, and visualization of the data using bar graphs, pie charts, and predictive models for polynomial regression and support vector machines.', 'duration': 1306.666, 'highlights': ['The total confirmed cases worldwide have reached almost 30 lakhs with more than 2 lakhs casualties reported so far. The total confirmed cases worldwide have reached almost 30 lakhs with more than 2 lakhs casualties reported so far.', 'The daily increase in confirmed cases, death cases, and recovery cases are tracked for different countries, providing insights into the daily trends. The daily increase in confirmed cases, death cases, and recovery cases are tracked for different countries, providing insights into the daily trends.', 'Visualization of the data using bar graphs, pie charts, and predictive models for polynomial regression and support vector machines is demonstrated. Visualization of the data using bar graphs, pie charts, and predictive models for polynomial regression and support vector machines is demonstrated.']}, {'end': 30772.166, 'start': 29949.401, 'title': 'Svm regression model and covid-19 data visualization', 'summary': 'Demonstrates the creation of a support vector regressor model, prediction of future values, and visualization of covid-19 data with insights on confirmed cases, deaths, recoveries, and active cases globally, emphasizing the importance of safety precautions and outlining machine learning courses offered by simplylearn.', 'duration': 822.765, 'highlights': ['The chapter demonstrates the creation of a support vector regressor model and prediction of future values with the output of MAE and MSE values. Creation of support vector regressor model, prediction of future values, output of MAE and MSE values', 'Visualization of COVID-19 data includes insights on confirmed cases, deaths, recoveries, and active cases globally, revealing that the total number of cases has reached close to 30 lakhs, deaths have reached over 2 lakhs, recovered cases are more than 8 lakhs, and the number of active cases have been graphically presented. Insights on confirmed cases, deaths, recoveries, and active cases globally, total number of cases close to 30 lakhs, deaths over 2 lakhs, recovered cases more than 8 lakhs', 'Demonstration of plotting the number of coronavirus cases over time and creating visualizations for daily increase in confirmed death and recovery cases, showing high spikes on certain days. Plotting the number of coronavirus cases over time, visualizations for daily increase in confirmed death and recovery cases, high spikes on certain days', 'Explanation of the importance of safety precautions to avoid coronavirus, including maintaining social distancing, regular handwashing, wearing masks, and practicing respiratory hygiene. Importance of safety precautions to avoid coronavirus, including maintaining social distancing, regular handwashing, wearing masks, and practicing respiratory hygiene', 'Outline of machine learning courses offered by SimplyLearn, including a Postgraduate Program in AI and Machine Learning in collaboration with Purdue University and IBM, and a Machine Learning Certification Course with details on key features, learning path, skills covered, and industry projects. Outline of machine learning courses offered by SimplyLearn, including Postgraduate Program in AI and Machine Learning and Machine Learning Certification Course']}], 'duration': 3108.204, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/c8W7dRPdIPE/pics/c8W7dRPdIPE27663962.jpg', 'highlights': ['The global impact of coronavirus is analyzed, including total cases, deaths, fatality rate, and tests conducted by different countries.', 'The United States has the highest number of COVID-19 cases, with over 800,000 cases as of April 26th, followed by other heavily affected countries like Italy, Spain, France, and the United Kingdom.', 'The United States also reports the highest number of deaths, with over 53,000 fatalities, followed by Italy, France, the United Kingdom, and Germany.', "The fatality rate is particularly high in countries like France, the United Kingdom, and Italy, with rates exceeding 13%, indicating the severity of the disease's impact in these regions.", 'The United States has conducted over 5 million COVID-19 tests, followed by Russia with over 2.88 million tests, emphasizing the extensive testing efforts in these countries.', 'The data covers the period from 22nd January to 28th April, and the analysis involves predicting the number of upcoming cases for the next 20 days using polynomial regression and support vector machines model in Python.', 'Datasets utilized are obtained from the repository operated by the Johns Hopkins University Center of System Science and Engineering, which is regularly updated.', 'The total confirmed cases worldwide have reached almost 30 lakhs with more than 2 lakhs casualties reported so far.', 'The daily increase in confirmed cases, death cases, and recovery cases are tracked for different countries, providing insights into the daily trends.', 'Visualization of the data using bar graphs, pie charts, and predictive models for polynomial regression and support vector machines is demonstrated.', 'The chapter demonstrates the creation of a support vector regressor model and prediction of future values with the output of MAE and MSE values.', 'Visualization of COVID-19 data includes insights on confirmed cases, deaths, recoveries, and active cases globally, revealing that the total number of cases has reached close to 30 lakhs, deaths have reached over 2 lakhs, recovered cases are more than 8 lakhs.', 'Explanation of the importance of safety precautions to avoid coronavirus, including maintaining social distancing, regular handwashing, wearing masks, and practicing respiratory hygiene.']}], 'highlights': ['The machine learning market is projected to reach $47.29 million by 2027, growing at a CAGR of 44.9% from 2020 to 2027.', 'Machine learning involves training machines to learn from past data and make decisions, making it faster and more efficient than human decision-making.', 'Machine learning has various applications including healthcare diagnostics, sentiment analysis on social media, fraud detection in finance, and predictive modeling for surge pricing in the transportation sector.', 'The chapter introduces the concepts of supervised, unsupervised, and reinforcement learning, providing examples to illustrate each type of learning.', 'Machine learning is used for personalization in e-commerce and social media, targeting advertisements based on user interests and behavior.', 'Machine learning has revolutionized assistive medical technology, aiding in disease diagnosis, 3D modeling, and predictive analysis for conditions like brain tumors and ischemic stroke lesions.', 'Automatic translation employs machine learning using sequence to sequence learning, convolutional neural networks, and optical character recognition to translate text and identify images.', 'The chapter explains the basics of machine learning, including reinforcement learning, supervised and unsupervised learning.', 'Linear regression is showcased with examples, demonstrating its applications in predicting outcomes and relationships between variables.', 'Demonstration of classifying muffin and cupcake recipes using SVM with eight different features.', 'Introduction to k-means clustering and logistic regression.', 'The elbow method reveals a clear preference for 3 clusters in the data, with a noticeable elbow joint at 2 and around 3 and 4, indicating the choice of 3 clusters for analysis.', 'The chapter covers the fundamental matrix operations and manipulations, including addition, subtraction, scalar multiplication, matrix-vector multiplication, matrix-matrix multiplication, transpose, identity matrix, and inverse matrix.', 'Emphasizes the importance of understanding calculus and differential equations in machine learning.', 'Probabilistic sampling ensures fair representation and minimizes biases.', 'The p-value from medical trials showing positive results leads to the rejection of the null hypothesis.', 'Utilizing logic functions with sets, like checking if a specific number is in the set, e.g., 3 in the set (True) and 6 in the set (False).', 'Achieving an r2 score of 0.9352 for model accuracy.', 'The decision tree classifier achieved an accuracy of 93.6%, enabling the bank to make informed loan approval decisions.', 'Creating a K-nearest neighbor model with 80% accuracy achieved by preprocessing the data and using the KNeighborsClassifier.', 'The chapter demonstrates the process of creating a Support Vector Machine in Python using a small dataset to differentiate between crocodiles and alligators.', 'Dimensionality reduction reduces computation or training time, crucial for processing large data sets.', 'The global impact of coronavirus is analyzed, including total cases, deaths, fatality rate, and tests conducted by different countries.']}