title

Data Science Full Course - Learn Data Science in 10 Hours | Data Science For Beginners | Edureka

description

π₯ Data Science Course (Use Code "πππππππππ"): https://www.edureka.co/masters-program/data-scientist-certification
This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms. Below are the topics covered in this Data Science for Beginners course:
00:00 Data Science Full Course Agenda
2:44 Introduction to Data Science
9:55 Data Analysis at Walmart
13:20 What is Data Science?
14:39 Who is a Data Scientist?
16:50 Data Science Skill Set
21:51 Data Science Job Roles
26:58 Data Life Cycle
30:25 Statistics & Probability
34:31 Categories of Data
34:50 Qualitative Data
36:09 Quantitative Data
39:11 What is Statistics?
41:32 Basic Terminologies in Statistics
42:50 Sampling Techniques
45:31 Random Sampling
46:20 Systematic Sampling
46:50 Stratified Sampling
47:54 Types of Statistics
50:38 Descriptive Statistics
55:52 Measures of Spread
55:56 Range
56:44 Inter Quartile Range
58:58 Variance
59:36 Standard Deviation
1:14:25 Confusion Matrix
1:19:16 Probability
1:24:14 What is Probability?
1:27:13 Types of Events
1:27:58 Probability Distribution
1:28:15 Probability Density Function
1:30:02 Normal Distribution
1:30:51 Standard Deviation & Curve
1:31:19 Central Limit Theorem
1:33:12 Types of Probability
1:33:34 Marginal Probability
1:34:06 Joint Probability
1:34:58 Conditional Probability
1:35:56 Use-Case
1:39:46 Bayes Theorem
1:45:44 Inferential Statistics
1:56:40 Hypothesis Testing
2:00:34 Basics of Machine Learning
2:01:41 Need for Machine Learning
2:07:03 What is Machine Learning?
2:09:21 Machine Learning Definitions
2:11:48 Machine Learning Process
2:18:31 Supervised Learning Algorithm
2:19:54 What is Regression?
2:21:23 Linear vs Logistic Regression
2:33:51 Linear Regression
2:25:27 Where is Linear Regression used?
2:27:11 Understanding Linear Regression
2:37:00 What is R-Square?
2:46:35 Logistic Regression
2:51:22 Logistic Regression Curve
2:53:02 Logistic Regression Equation
2:56:21 Logistic Regression Use-Cases
2:58:23 Demo
3:00:57 Implement Logistic Regression
3:02:33 Import Libraries
3:05:28 Analyzing Data
3:11:52 Data Wrangling
3:23:54 Train & Test Data
3:20:44 Implement Logistic Regression
3:31:04 SUV Data Analysis
3:38:44 Decision Trees
3:39:50 What is Classification?
3:42:27 Types of Classification
3:42:27 Decision Tree
3:43:51 Random Forest
3:45:06 Naive Bayes
3:47:12 KNN
3:49:02 What is Decision Tree?
3:55:15 Decision Tree Terminologies
3:56:51 CART Algorithm
3:58:50 Entropy
4:00:15 What is Entropy?
4:23:52 Random Forest
4:27:29 Types of Classifier
4:31:17 Why Random Forest?
4:39:14 What is Random Forest?
4:51:26 How Random Forest Works?
4:51:36 Random Forest Algorithm
5:04:23 K Nearest Neighbour
5:05:33 What is KNN Algorithm?
5:08:50 KNN Algorithm Working
5:24:30 What is Naive Bayes?
5:25:13 Bayes Theorem
5:27:48 Bayes Theorem Proof
5:29:43 Naive Bayes Working
5:39:06 Types of Naive Bayes
5:53:37 Support Vector Machine
5:57:40 What is SVM?
5:59:46 How does SVM work?
6:03:00 Introduction to Non-Linear SVM
6:04:48 SVM Example
6:06:12 Unsupervised Learning Algorithms - KMeans
6:06:18 What is Unsupervised Learning?
6:06:45 Unsupervised Learning: Process Flow
6:07:17 What is Clustering?
6:09:15 Types of Clustering
6:10:15 K-Means Clustering
6:10:40 K-Means Algorithm Working
6:16:17 K-Means Algorithm
6:19:16 Fuzzy C-Means Clustering
6:21:22 Hierarchical Clustering
6:22:53 Association Clustering
6:24:57 Association Rule Mining
6:30:35 Apriori Algorithm
6:37:45 Apriori Demo
6:40:49 What is Reinforcement Learning?
6:42:48 Reinforcement Learning Process
6:51:10 Markov Decision Process
6:54:53 Understanding Q - Learning
7:13:12 Q-Learning Demo
7:25:34 The Bellman Equation
7:48:39 What is Deep Learning?
7:52:53 Why we need Artificial Neuron?
7:54:33 Perceptron Learning Algorithm
7:57:57 Activation Function
8:03:14 Single Layer Perceptron
8:04:04 What is Tensorflow?
8:07:25 Demo
8:21:03 What is a Computational Graph?
8:49:18 Limitations of Single Layer Perceptron
8:50:08 Multi-Layer Perceptron
8:51:24 What is Backpropagation?
8:52:26 Backpropagation Learning Algorithm
8:59:31 Multi-layer Perceptron Demo
9:01:23 Data Science Interview Questions
Edureka Data Science Training & Certifications
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detail

{'title': 'Data Science Full Course - Learn Data Science in 10 Hours | Data Science For Beginners | Edureka', 'heatmap': [{'end': 3747.061, 'start': 2614.179, 'weight': 0.801}], 'summary': "Covers a comprehensive data science course, including statistics, machine learning, and algorithms, emphasizing the increasing demand for data science due to exponential data growth. it discusses walmart's use of data science to gain customer insights, highlights essential skills and job roles in data science, explores various statistical techniques and analysis, and provides practical implementations and applications of machine learning algorithms, achieving specific accuracies and model performances.", 'chapters': [{'end': 714.828, 'segs': [{'end': 53.112, 'src': 'embed', 'start': 28.559, 'weight': 0, 'content': [{'end': 34.582, 'text': 'the first module is an introduction to data science that covers all the basic fundamentals of data science.', 'start': 28.559, 'duration': 6.023}, {'end': 37.944, 'text': 'followed by this, we have statistics and probability module,', 'start': 34.582, 'duration': 3.362}, {'end': 43.447, 'text': "where you'll understand the statistics and the math behind data science and machine learning algorithms.", 'start': 37.944, 'duration': 5.503}, {'end': 50.871, 'text': "the next module is the basics of machine learning, where we'll understand what exactly machine learning is, the different types of machine learning,", 'start': 43.447, 'duration': 7.424}, {'end': 53.112, 'text': 'the different machine learning algorithms, and so on.', 'start': 50.871, 'duration': 2.241}], 'summary': 'Introduction to data science, statistics, probability, and basics of machine learning covered in the course.', 'duration': 24.553, 'max_score': 28.559, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF028559.jpg'}, {'end': 86.065, 'src': 'embed', 'start': 62.495, 'weight': 1, 'content': [{'end': 69.798, 'text': 'The next module is the logistic regression module where we will see how logistic regression can be used to solve classification problems.', 'start': 62.495, 'duration': 7.303}, {'end': 77.341, 'text': 'After this we will discuss about decision trees and we will see how decision trees can be used to solve complex data driven problems.', 'start': 70.598, 'duration': 6.743}, {'end': 79.982, 'text': 'The next module is random forest.', 'start': 78.002, 'duration': 1.98}, {'end': 86.065, 'text': 'Here we will understand how random forest can be used to solve classification problems and regression problems,', 'start': 80.322, 'duration': 5.743}], 'summary': 'Modules cover logistic regression, decision trees, and random forest for classification and regression problems.', 'duration': 23.57, 'max_score': 62.495, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF062495.jpg'}, {'end': 273.526, 'src': 'embed', 'start': 246.596, 'weight': 4, 'content': [{'end': 253.079, 'text': "where we'll use the k-means algorithm to cluster movies based on their popularity on social media platforms like Facebook.", 'start': 246.596, 'duration': 6.483}, {'end': 259.422, 'text': "At the end of today's session we'll also discuss about what a data science certification is and why you should take it up.", 'start': 253.579, 'duration': 5.843}, {'end': 261.663, 'text': "So guys there's a lot to cover in today's session.", 'start': 259.742, 'duration': 1.921}, {'end': 263.264, 'text': "Let's jump into the first topic.", 'start': 261.863, 'duration': 1.401}, {'end': 270.005, 'text': 'Do you guys remember the times when we had telephones and we had to go to PCO booths in order to make a phone call?', 'start': 264.102, 'duration': 5.903}, {'end': 273.526, 'text': "Now, those times were very simple, because we didn't generate a lot of data.", 'start': 270.385, 'duration': 3.141}], 'summary': 'Using k-means to cluster movies by social media popularity, and discussing data science certification benefits.', 'duration': 26.93, 'max_score': 246.596, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF0246596.jpg'}, {'end': 660.372, 'src': 'embed', 'start': 619.263, 'weight': 5, 'content': [{'end': 622.864, 'text': 'They know that if a customer buys Pop-Tarts, they might also buy cookies.', 'start': 619.263, 'duration': 3.601}, {'end': 624.325, 'text': 'How do they know all of this?', 'start': 623.345, 'duration': 0.98}, {'end': 626.546, 'text': 'Like, how do they generate information like this?', 'start': 624.405, 'duration': 2.141}, {'end': 633.768, 'text': 'Now, they use the data that they get from their customers and they analyze it to see what a particular customer is looking for.', 'start': 626.966, 'duration': 6.802}, {'end': 639.913, 'text': "Now let's look at a few cases where Walmart actually analyzed the data and they figured out the customer needs.", 'start': 634.228, 'duration': 5.685}, {'end': 643.236, 'text': "So let's consider the Halloween and the cookie sales example.", 'start': 640.214, 'duration': 3.022}, {'end': 647.841, 'text': 'Now during Halloween sales analyst at Walmart took a look at the data.', 'start': 643.597, 'duration': 4.244}, {'end': 653.065, 'text': 'Okay, and he found out that a specific cookie was popular across all Walmart stores.', 'start': 648.141, 'duration': 4.924}, {'end': 660.372, 'text': 'So every Walmart store was selling these cookies very well, but he found out that there were two stores which are not selling them at all.', 'start': 653.466, 'duration': 6.906}], 'summary': 'Walmart analyzes customer data to identify popular products and sales patterns.', 'duration': 41.109, 'max_score': 619.263, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF0619263.jpg'}], 'start': 7.06, 'title': 'The evolution and demand for data science', 'summary': "Covers a comprehensive data science course, including statistics, machine learning, and algorithms, emphasizing the increasing demand for data science due to exponential data growth. it also discusses the evolution of data, highlighting walmart's use of data science to gain customer insights and boost sales.", 'chapters': [{'end': 263.264, 'start': 7.06, 'title': 'Data science full course', 'summary': 'Covers a comprehensive data science course including statistics, machine learning, algorithms like linear regression, logistic regression, decision trees, random forest, knn, naive bias, support vector machines, reinforcement learning, deep learning, and data science job roles, with a focus on the increasing demand for data science due to the exponential growth of data.', 'duration': 256.204, 'highlights': ['The demand for data science is driven by the exponential growth of data, with a need to process and derive insights from the vast amount of generated data.', 'The course covers various modules including statistics, machine learning, and algorithms like linear regression, logistic regression, decision trees, random forest, KNN, naive bias, support vector machines, reinforcement learning, and deep learning.', 'The session provides an in-depth understanding of data science, its importance, and its application in real-world scenarios, such as the use of insightful patterns by Walmart to enhance business potential.']}, {'end': 714.828, 'start': 264.102, 'title': 'Evolution of data and the need for data science', 'summary': "Discusses the evolution of data from simple telephone contacts to the massive data generated by smartphones, iot, and social media, emphasizing the need for data science to analyze and extract useful insights from this data. it also highlights walmart's use of data science to gain customer insights and boost sales.", 'duration': 450.726, 'highlights': ['Walmart using data science to analyze customer data and boost sales', 'Massive data generation from smartphones, IoT, and social media', 'Impact of social media on data generation', 'Evolution of data from simple telephone contacts to complex smartphone data']}], 'duration': 707.768, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF07060.jpg', 'highlights': ['The course covers statistics, machine learning, and algorithms like linear regression, logistic regression, decision trees, random forest, KNN, naive bias, support vector machines, reinforcement learning, and deep learning.', 'The demand for data science is driven by the exponential growth of data, with a need to process and derive insights from the vast amount of generated data.', 'The session provides an in-depth understanding of data science, its importance, and its application in real-world scenarios, such as the use of insightful patterns by Walmart to enhance business potential.', 'Walmart using data science to analyze customer data and boost sales', 'Massive data generation from smartphones, IoT, and social media', 'Evolution of data from simple telephone contacts to complex smartphone data', 'Impact of social media on data generation']}, {'end': 1598.296, 'segs': [{'end': 1329.705, 'src': 'embed', 'start': 1301.497, 'weight': 0, 'content': [{'end': 1306.962, 'text': "So now that we know the skills that are needed to become a data scientist, let's look at the different job roles.", 'start': 1301.497, 'duration': 5.465}, {'end': 1312.216, 'text': 'Since data science is a very vast field, there are many job roles under data science.', 'start': 1307.634, 'duration': 4.582}, {'end': 1314.117, 'text': "So let's take a look at each role.", 'start': 1312.436, 'duration': 1.681}, {'end': 1317.138, 'text': "Let's start off with data scientists.", 'start': 1314.677, 'duration': 2.461}, {'end': 1325.562, 'text': 'So, as data scientists have to understand the challenges of a business and they have to offer the best solution using data analysis and data processing.', 'start': 1317.378, 'duration': 8.184}, {'end': 1329.705, 'text': 'So, for instance, if they are expected to perform predictive analysis,', 'start': 1325.842, 'duration': 3.863}], 'summary': 'Data science has various job roles including data scientists who offer solutions using data analysis.', 'duration': 28.208, 'max_score': 1301.497, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF01301497.jpg'}, {'end': 1384.527, 'src': 'embed', 'start': 1358.827, 'weight': 2, 'content': [{'end': 1364.131, 'text': 'They have to also perform queries on databases, so they should be aware of the different querying languages.', 'start': 1358.827, 'duration': 5.304}, {'end': 1368.875, 'text': 'And guys, one of the most important skills of a data analyst is optimization.', 'start': 1364.632, 'duration': 4.243}, {'end': 1378.362, 'text': 'This is because they have to create and modify algorithms that can be used to pull information from some of the biggest databases without corrupting the data.', 'start': 1369.435, 'duration': 8.927}, {'end': 1384.527, 'text': 'So to become a data analyst, you must know technologies such as SQL, R, SAS, and Python.', 'start': 1378.783, 'duration': 5.744}], 'summary': 'Data analysts must be proficient in sql, r, sas, and python for querying databases and optimizing algorithms.', 'duration': 25.7, 'max_score': 1358.827, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF01358827.jpg'}], 'start': 715.489, 'title': "Walmart's data analysis success and data science skills", 'summary': "Highlights walmart's utilization of social media data for analyzing trending products, investing in data analysis and processing, and leveraging insights to attract customers. it also discusses the essential skills and job roles in data science, including programming languages like r or python, sql, machine learning, big data processing frameworks like hadoop and spark, and various roles such as data scientist, data analyst, and data engineer.", 'chapters': [{'end': 1042.339, 'start': 715.489, 'title': "Walmart's data analysis success", 'summary': 'Highlights how walmart utilizes social media data to analyze trending products, invests in data analysis and processing, and leverages insights to attract customers and enhance business, while also exploring the role of data science and the skill sets required for data scientists.', 'duration': 326.85, 'highlights': ['Walmart uses social media data to analyze trending products and make strategic business decisions, like introducing cake pops based on Facebook user interests.', 'Walmart invests significant resources in data analysis and processing to uncover hidden patterns and associations, subsequently offering promotions or discounts based on these insights.', "Data science involves uncovering hidden insights from data to facilitate smart business decisions, illustrated by Netflix's analysis of user viewing patterns to determine content preferences.", 'Data scientists need a quantitative lens, proficient math skills, technological expertise, and business acumen, emphasizing the importance of statistics, mathematics, technology, and business understanding.']}, {'end': 1598.296, 'start': 1042.339, 'title': 'Skills and job roles in data science', 'summary': 'Discusses the essential skills needed to become a data scientist, including the importance of programming languages like r or python and sql, data extraction and processing, data wrangling, machine learning, big data processing frameworks like hadoop and spark, and data visualization. it also outlines the various job roles in data science, such as data scientist, data analyst, data architect, data engineer, statistician, database administrator, business analyst, and data and analytics manager, along with the specific skills required for each role.', 'duration': 555.957, 'highlights': ['The importance of programming languages like R or Python and SQL is emphasized, as they are essential for data analysis and processing, with predefined packages containing most algorithms.', 'Data wrangling and exploration are crucial tasks in data science, involving the cleaning of data, analyzing patterns, trends, outliers, and unexpected results.', 'Machine learning methods are essential for processing large amounts of data, and familiarity with algorithms like KNN, random forest, k-means, and support vector machines is important for data scientists.', 'Knowledge of big data processing frameworks like Hadoop and Spark is crucial for handling structured and unstructured data.', 'Data visualization is a critical skill for presenting data in an understandable and visually appealing format, and proficiency in tools like Tableau and Power BI is essential for data scientists.']}], 'duration': 882.807, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF0715489.jpg', 'highlights': ['Walmart uses social media data to analyze trending products and make strategic business decisions, like introducing cake pops based on Facebook user interests.', 'Walmart invests significant resources in data analysis and processing to uncover hidden patterns and associations, subsequently offering promotions or discounts based on these insights.', 'The importance of programming languages like R or Python and SQL is emphasized, as they are essential for data analysis and processing, with predefined packages containing most algorithms.', 'Data wrangling and exploration are crucial tasks in data science, involving the cleaning of data, analyzing patterns, trends, outliers, and unexpected results.', 'Machine learning methods are essential for processing large amounts of data, and familiarity with algorithms like KNN, random forest, k-means, and support vector machines is important for data scientists.']}, {'end': 2635.032, 'segs': [{'end': 1685.872, 'src': 'embed', 'start': 1657.045, 'weight': 4, 'content': [{'end': 1663.946, 'text': 'At this stage, some of the questions you can ask yourself is what data do I need for my project? Where does it live?', 'start': 1657.045, 'duration': 6.901}, {'end': 1668.967, 'text': 'How can I obtain it? And what is the most efficient way to store and access all of it?', 'start': 1664.046, 'duration': 4.921}, {'end': 1671.048, 'text': 'Next up, there is data processing.', 'start': 1669.468, 'duration': 1.58}, {'end': 1674.869, 'text': 'Now usually all the data that you collected is a huge mess.', 'start': 1671.588, 'duration': 3.281}, {'end': 1678.65, 'text': "It's not formatted, it's not structured, it's not cleaned.", 'start': 1675.369, 'duration': 3.281}, {'end': 1685.872, 'text': "So if you find any data set that is cleaned and it's packaged well for you, then you've actually won the lottery,", 'start': 1679.17, 'duration': 6.702}], 'summary': 'Key questions for data project: data needs, source, processing, storage. clean data is like winning the lottery.', 'duration': 28.827, 'max_score': 1657.045, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF01657045.jpg'}, {'end': 2154.264, 'src': 'embed', 'start': 2103.442, 'weight': 0, 'content': [{'end': 2109.467, 'text': "so nominal data is any sort of data that doesn't have any order or ranking.", 'start': 2103.442, 'duration': 6.025}, {'end': 2112.95, 'text': 'okay, an example of nominal data is gender.', 'start': 2109.467, 'duration': 3.483}, {'end': 2115.072, 'text': 'now there is no ranking in gender.', 'start': 2112.95, 'duration': 2.122}, {'end': 2118.055, 'text': "there's only male, female or other right.", 'start': 2115.072, 'duration': 2.983}, {'end': 2122.239, 'text': 'there is no one, two, three, four or any sort of ordering in gender.', 'start': 2118.055, 'duration': 4.184}, {'end': 2125.141, 'text': 'race is another example of nominal data.', 'start': 2122.239, 'duration': 2.902}, {'end': 2129.345, 'text': 'now, ordinal data is basically an ordered series of information.', 'start': 2125.141, 'duration': 4.204}, {'end': 2132.52, 'text': "Okay, let's say that you went to a restaurant.", 'start': 2130.419, 'duration': 2.101}, {'end': 2136.282, 'text': 'Okay, your information is stored in the form of customer ID.', 'start': 2132.82, 'duration': 3.462}, {'end': 2139.524, 'text': 'Alright, so basically you are represented with a customer ID.', 'start': 2136.662, 'duration': 2.862}, {'end': 2145.18, 'text': 'Now you would have rated their service as either good or average.', 'start': 2140.197, 'duration': 4.983}, {'end': 2147.321, 'text': "That's how ordinal data is.", 'start': 2145.54, 'duration': 1.781}, {'end': 2154.264, 'text': "And similarly, they'll have a record of other customers who visit the restaurant along with their ratings.", 'start': 2148.081, 'duration': 6.183}], 'summary': 'Nominal data has no ranking, while ordinal data is ordered series of information.', 'duration': 50.822, 'max_score': 2103.442, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF02103442.jpg'}, {'end': 2474.116, 'src': 'embed', 'start': 2441.376, 'weight': 1, 'content': [{'end': 2444.258, 'text': 'All right, this is another problem that comes under statistics.', 'start': 2441.376, 'duration': 2.882}, {'end': 2446.579, 'text': "Let's look at another example.", 'start': 2445.078, 'duration': 1.501}, {'end': 2455.585, 'text': 'The latest sales data has just come in and your boss wants you to prepare a report for management on places where the company could improve its business.', 'start': 2447.36, 'duration': 8.225}, {'end': 2462.695, 'text': 'What should you look for and what should you not look for? Now this problem involves a lot of data analysis.', 'start': 2456.419, 'duration': 6.276}, {'end': 2467.375, 'text': "You'll have to look at the different variables that are causing your business to go down,", 'start': 2463.314, 'duration': 4.061}, {'end': 2474.116, 'text': 'or you have to look at a few variables that are increasing the performance of your models and thus growing your business.', 'start': 2467.375, 'duration': 6.741}], 'summary': 'Analyze sales data to identify areas for business improvement.', 'duration': 32.74, 'max_score': 2441.376, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF02441376.jpg'}, {'end': 2585.7, 'src': 'embed', 'start': 2552.539, 'weight': 2, 'content': [{'end': 2554.4, 'text': "That's how your sample should be chosen.", 'start': 2552.539, 'duration': 1.861}, {'end': 2560.543, 'text': 'So a well-chosen sample will contain most of the information about a particular population parameter.', 'start': 2555.06, 'duration': 5.483}, {'end': 2566.527, 'text': 'Now you must be wondering how can one choose a sample that best represents the entire population?', 'start': 2561.024, 'duration': 5.503}, {'end': 2573.773, 'text': 'Now, sampling is a statistical method that deals with the selection of individual observations within a population.', 'start': 2567.21, 'duration': 6.563}, {'end': 2579.837, 'text': 'So sampling is performed in order to infer statistical knowledge about a population.', 'start': 2574.354, 'duration': 5.483}, {'end': 2585.7, 'text': 'If you want to understand the different statistics of a population, like the mean, the median,', 'start': 2580.437, 'duration': 5.263}], 'summary': 'Sampling is a statistical method to select observations for inferring population statistics.', 'duration': 33.161, 'max_score': 2552.539, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF02552539.jpg'}], 'start': 1598.756, 'title': 'Understanding data and statistics', 'summary': 'Covers different job roles in data science and the six steps in the data lifecycle. it also provides a comprehensive overview of statistics, probability, and their real-life applications, emphasizing the importance of basic statistical terminologies like population and sample.', 'chapters': [{'end': 1656.525, 'start': 1598.756, 'title': 'Data science job roles & data lifecycle', 'summary': 'Covers different job roles in data science, including the need for understanding technologies like python, sas, r, java, and the six steps in the data lifecycle: business requirement, data acquisition, data processing, data exploration, modeling, and deployment.', 'duration': 57.769, 'highlights': ['The chapter covers different job roles in data science, emphasizing the need for a good understanding of technologies like Python, SAS, R, Java.', 'The six steps in the data lifecycle are outlined as: business requirement, data acquisition, data processing, data exploration, modeling, and deployment.', 'Data acquisition is highlighted as a critical step, emphasizing the importance of gathering data from different sources for a data science project.']}, {'end': 2016.586, 'start': 1657.045, 'title': 'Data life cycle & statistics', 'summary': 'Covers the data life cycle, including data processing, exploration, modeling, and deployment, along with a comprehensive overview of statistics and probability, covering descriptive and inferential statistics, probability distributions, and bayes theorem.', 'duration': 359.541, 'highlights': ['The process of model training involves splitting the input data into the training and testing data sets, building a model using the training data set, and evaluating the model using machine learning algorithms.', 'The data exploration stage involves understanding the patterns in data through techniques like histograms and exploring different models that can be applied to the data.', 'The hypothesis testing is an essential part of inferential statistics and is covered in the session with a use case that illustrates how hypothesis testing works.', 'The chapter provides a comprehensive overview of statistics and probability, covering descriptive and inferential statistics, probability distributions, and Bayes Theorem.']}, {'end': 2400.71, 'start': 2017.23, 'title': 'Understanding data and statistics', 'summary': 'Discusses the definition of data, its significance in providing insights for analysis and decision-making, the two major subcategories of data (qualitative and quantitative), and the types of qualitative and quantitative data. it also touches on the types of variables and provides an overview of statistics, including its role in data collection, analysis, interpretation, and visualization.', 'duration': 383.48, 'highlights': ['Data is everything around us and provides insights for analysis and decision-making; each phone click generates substantial data.', 'Data is divided into qualitative (dealing with characteristics and descriptors) and quantitative (dealing with numbers) categories, further subcategorized into nominal, ordinal, discrete, and continuous data.', 'Qualitative data includes nominal (no order or ranking) and ordinal (ordered) data, while quantitative data comprises discrete (finite possible values) and continuous (infinite possible values) data.', 'Statistics encompasses data collection, analysis, interpretation, and presentation, going beyond mere analysis; it is concerned with understanding how data can be used to solve complex problems.']}, {'end': 2635.032, 'start': 2401.374, 'title': 'Statistics in real life scenarios', 'summary': 'Focuses on real-life applications of statistics, including testing drug effectiveness, probability assessment in sports betting, and data analysis for business improvement, while emphasizing the importance of basic statistical terminologies like population and sample.', 'duration': 233.658, 'highlights': ['The importance of basic statistical terminologies like population and sample is emphasized, with a sample being a subset of the population that should represent the entire population to contain most of the information about a particular population parameter.', 'The chapter discusses real-life scenarios where statistics play a crucial role, such as testing drug effectiveness, probability assessment in sports betting, and data analysis for business improvement.', 'The need for statistical methods, particularly sampling, is highlighted for inferring statistical knowledge about a population and understanding population statistics like mean, median, mode, standard deviation, and variance.']}], 'duration': 1036.276, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF01598756.jpg', 'highlights': ['The six steps in the data lifecycle are outlined as: business requirement, data acquisition, data processing, data exploration, modeling, and deployment.', 'The chapter covers different job roles in data science, emphasizing the need for a good understanding of technologies like Python, SAS, R, Java.', 'The chapter provides a comprehensive overview of statistics and probability, covering descriptive and inferential statistics, probability distributions, and Bayes Theorem.', 'The importance of basic statistical terminologies like population and sample is emphasized, with a sample being a subset of the population that should represent the entire population to contain most of the information about a particular population parameter.', 'The process of model training involves splitting the input data into the training and testing data sets, building a model using the training data set, and evaluating the model using machine learning algorithms.', 'Data acquisition is highlighted as a critical step, emphasizing the importance of gathering data from different sources for a data science project.', 'The hypothesis testing is an essential part of inferential statistics and is covered in the session with a use case that illustrates how hypothesis testing works.', 'The need for statistical methods, particularly sampling, is highlighted for inferring statistical knowledge about a population and understanding population statistics like mean, median, mode, standard deviation, and variance.']}, {'end': 6022.49, 'segs': [{'end': 3479.264, 'src': 'embed', 'start': 3450.155, 'weight': 2, 'content': [{'end': 3454.398, 'text': "Alright, so if you look at this, I've highlighted the 25th and the 26th observation.", 'start': 3450.155, 'duration': 4.243}, {'end': 3460.541, 'text': 'So how you can calculate Q1, or first quartile, is by taking the average of these two values.', 'start': 3454.818, 'duration': 5.723}, {'end': 3467.125, 'text': "Alright, since both the values are 45, when you add them up and divide them by two, you'll still get 45.", 'start': 3461.021, 'duration': 6.104}, {'end': 3472.368, 'text': 'Now the second quartile, or Q2, is between the 50th and the 51st observation.', 'start': 3467.125, 'duration': 5.243}, {'end': 3479.264, 'text': "So you're going to take the average of 58 and 59 and you'll get a value of 58.5.", 'start': 3472.97, 'duration': 6.294}], 'summary': 'Calculating q1 and q2 using observations 25th, 26th, 50th, and 51st.', 'duration': 29.109, 'max_score': 3450.155, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF03450155.jpg'}, {'end': 3747.061, 'src': 'embed', 'start': 3723.245, 'weight': 0, 'content': [{'end': 3729.973, 'text': 'All you have to do is you have to substitute the values in the formula, all right? I hope this was clear to all of you.', 'start': 3723.245, 'duration': 6.728}, {'end': 3736.396, 'text': "Now let's move on and discuss the next topic which is information gain and entropy.", 'start': 3731.534, 'duration': 4.862}, {'end': 3739.077, 'text': 'Now this is one of my favorite topics in statistics.', 'start': 3736.896, 'duration': 2.181}, {'end': 3747.061, 'text': "It's very interesting and this topic is mainly involved in machine learning algorithms like decision trees and random forest.", 'start': 3739.197, 'duration': 7.864}], 'summary': 'Substitute values in formula, discuss information gain and entropy, key topic in machine learning.', 'duration': 23.816, 'max_score': 3723.245, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF03723245.jpg'}, {'end': 3821.632, 'src': 'embed', 'start': 3786.723, 'weight': 1, 'content': [{'end': 3789.485, 'text': 'So it can be measured by using this formula.', 'start': 3786.723, 'duration': 2.762}, {'end': 3796.309, 'text': 'So here S is the set of all instances in the data set or all the data items in the data set.', 'start': 3789.845, 'duration': 6.464}, {'end': 3799.59, 'text': 'N is the different type of classes in your data set.', 'start': 3796.749, 'duration': 2.841}, {'end': 3801.952, 'text': 'PI is the event probability.', 'start': 3800.191, 'duration': 1.761}, {'end': 3809.499, 'text': "Now, this might seem a little confusing to you all, but when we go through the use case, you'll understand all of these terms even better.", 'start': 3802.492, 'duration': 7.007}, {'end': 3809.8, 'text': 'all right?', 'start': 3809.499, 'duration': 0.301}, {'end': 3813.503, 'text': 'Coming to information gain, as the word suggests,', 'start': 3810.22, 'duration': 3.283}, {'end': 3821.632, 'text': 'information gain indicates how much information a particular feature or a particular variable gives us about the final outcome.', 'start': 3813.503, 'duration': 8.129}], 'summary': 'Measuring information gain in data set with formula and terms explained.', 'duration': 34.909, 'max_score': 3786.723, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF03786723.jpg'}, {'end': 5546.537, 'src': 'embed', 'start': 5522.789, 'weight': 4, 'content': [{'end': 5529.111, 'text': 'You can see each sample here and the mean of each sample is almost along the same line.', 'start': 5522.789, 'duration': 6.322}, {'end': 5533.332, 'text': 'So this is exactly what the central limit theorem states.', 'start': 5529.131, 'duration': 4.201}, {'end': 5539.454, 'text': 'Now the accuracy or the resemblance to the normal distribution depends on two main factors.', 'start': 5533.873, 'duration': 5.581}, {'end': 5542.776, 'text': 'So the first is the number of sample points that you consider.', 'start': 5540.135, 'duration': 2.641}, {'end': 5546.537, 'text': 'And the second is the shape of the underlying population.', 'start': 5543.716, 'duration': 2.821}], 'summary': 'Central limit theorem: mean of samples align, accuracy depends on sample size and population shape.', 'duration': 23.748, 'max_score': 5522.789, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF05522789.jpg'}], 'start': 2635.092, 'title': 'Statistical techniques and analysis', 'summary': "Discusses sampling techniques and types of statistics in statistics, measures of spread in data analysis, and information gain and entropy in decision trees. it covers a practical demo in r on descriptive statistics, probability, and probability distributions, and explores the use of bayes' theorem in naive bias algorithm, emphasizing key statistical concepts and machine learning applications.", 'chapters': [{'end': 2853.731, 'start': 2635.092, 'title': 'Sampling techniques in statistics', 'summary': 'Discusses the concept of sampling in statistics, emphasizing that a sample of the population is studied to draw inference about the entire population, and it explores the three main types of probability sampling: random sampling, systematic sampling, and stratified sampling.', 'duration': 218.639, 'highlights': ['Sampling is a method wherein a sample of the population is studied in order to draw inference about the entire population.', 'The chapter focuses on probability sampling techniques, specifically random sampling, systematic sampling, and stratified sampling.', 'Random sampling ensures that each member of the population has an equal chance of being selected in the sample.', 'Systematic sampling involves choosing every nth record from the population to be a part of the sample.', 'Stratified sampling uses strata, which are subsets of the population that share at least one common characteristic, to form samples from a large population.']}, {'end': 3333.974, 'start': 2854.171, 'title': 'Types of statistics & descriptive statistics', 'summary': 'Covers the two major types of statistics, descriptive statistics and inferential statistics, with an explanation of their features and differences, along with detailed examples and calculations of measures of central tendency (mean, median, mode) and measures of variability.', 'duration': 479.803, 'highlights': ['Descriptive statistics vs inferential statistics explained with detailed examples and calculations of measures of central tendency (mean, median, mode) and measures of variability', 'Detailed explanation of descriptive statistics and inferential statistics', 'Example and calculation of median as a measure of central tendency in a sample data set', 'Explanation and example of mode as a measure of central tendency in a sample data set', 'Calculation and example of mean as a measure of central tendency in a sample data set']}, {'end': 3723.125, 'start': 3334.555, 'title': 'Measures of spread in data analysis', 'summary': 'Discusses the measures of spread including range, interquartile range, variance, and standard deviation, with examples and calculations, aiming to understand and apply these measures in data analysis.', 'duration': 388.57, 'highlights': ['The mode is six since six is more recurrent than four', 'Calculating the range by subtracting the maximum value from the minimum value', 'Defining quartiles and calculating interquartile range by subtracting Q1 from Q3', 'Explaining the calculation of variance and deviation in data sets', 'Demonstrating the calculation of standard deviation with a practical example']}, {'end': 4744.396, 'start': 3723.245, 'title': 'Information gain and entropy in decision trees', 'summary': 'Discusses the importance of information gain and entropy in building machine learning models, explaining the formulas and demonstrating their use in a decision tree use case, concluding with an explanation of confusion matrix for classifier model evaluation.', 'duration': 1021.151, 'highlights': ['Decision trees and random forest are mainly involved in machine learning algorithms and information gain and entropy are essential in building machine learning models.', 'Entropy is the measure of uncertainty in the data, and information gain indicates how much information a feature gives about the final outcome.', 'Demonstrates the process of using information gain and entropy to select the most significant variable for the root node in a decision tree use case, showcasing the practical application of these statistical measures.', 'Provides an in-depth explanation of confusion matrix for evaluating the performance of a classifier model by comparing actual and predicted results, offering a practical understanding of model evaluation in classification tasks.']}, {'end': 6022.49, 'start': 4744.917, 'title': 'Descriptive statistics, probability, and probability distributions', 'summary': "Covers descriptive statistics, including a practical demo in r on mean, median, mode, variance, and standard deviation calculation, followed by a detailed discussion on probability, including the relationship between statistics and probability, types of events, and probability distributions, with an example on conditional probability. it also explores the use of bayes' theorem in naive bias algorithm.", 'duration': 1277.573, 'highlights': ['A practical demo in R on mean, median, mode, variance, and standard deviation calculation', 'The relationship between statistics and probability', 'Explanation of probability as the measure of how likely an event will occur, with a specific example of rolling a dice', 'Detailed explanation of types of probability distributions: probability density function, normal distribution, and central limit theorem', 'Illustration of different types of probability: marginal, joint, and conditional probability using a practical use case', "Explanation of the significance of Bayes' theorem in naive bias algorithm and its application in Gmail spam filtering"]}], 'duration': 3387.398, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF02635092.jpg', 'highlights': ['The chapter focuses on probability sampling techniques, specifically random sampling, systematic sampling, and stratified sampling.', 'Demonstrates the process of using information gain and entropy to select the most significant variable for the root node in a decision tree use case, showcasing the practical application of these statistical measures.', 'A practical demo in R on mean, median, mode, variance, and standard deviation calculation', 'Explanation of probability as the measure of how likely an event will occur, with a specific example of rolling a dice', "Explanation of the significance of Bayes' theorem in naive bias algorithm and its application in Gmail spam filtering"]}, {'end': 7174.74, 'segs': [{'end': 6619.967, 'src': 'embed', 'start': 6588.474, 'weight': 0, 'content': [{'end': 6594.001, 'text': 'Instead, you have estimated an interval within which your value might occur right?', 'start': 6588.474, 'duration': 5.527}, {'end': 6599.914, 'text': 'Okay, now, this image clearly shows how point estimate and interval estimate are different.', 'start': 6594.79, 'duration': 5.124}, {'end': 6603.216, 'text': 'So, guys, interval estimate is obviously more accurate,', 'start': 6600.374, 'duration': 2.842}, {'end': 6611.361, 'text': "because you're not just focusing on a particular value or a particular point in order to predict the probability.", 'start': 6603.216, 'duration': 8.145}, {'end': 6619.967, 'text': "Instead, you're seeing that the value might be within this range between the lower confidence limit and the upper confidence limit.", 'start': 6611.501, 'duration': 8.466}], 'summary': 'Interval estimate is more accurate, showing a range between lower and upper confidence limits.', 'duration': 31.493, 'max_score': 6588.474, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF06588474.jpg'}, {'end': 6828.732, 'src': 'embed', 'start': 6803.632, 'weight': 1, 'content': [{'end': 6810.375, 'text': 'So the level of confidence is the probability that the interval estimate contains the population parameter.', 'start': 6803.632, 'duration': 6.743}, {'end': 6816.41, 'text': 'So this interval between minus ZC and ZC, although area beneath this curve,', 'start': 6811.027, 'duration': 5.383}, {'end': 6821.514, 'text': 'is nothing but the probability that the interval estimate contains a population parameter.', 'start': 6816.41, 'duration': 5.104}, {'end': 6825.036, 'text': 'Alright, it should basically contain the value that you are predicting.', 'start': 6821.954, 'duration': 3.082}, {'end': 6828.732, 'text': 'Now these are known as critical values.', 'start': 6826.611, 'duration': 2.121}], 'summary': 'Confidence level indicates probability of interval estimate containing population parameter.', 'duration': 25.1, 'max_score': 6803.632, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF06803632.jpg'}, {'end': 6902.029, 'src': 'embed', 'start': 6876.039, 'weight': 7, 'content': [{'end': 6882.081, 'text': 'Now these Z-scores are calculated from the table as I mentioned before.', 'start': 6876.039, 'duration': 6.042}, {'end': 6884.882, 'text': '1.645 is calculated from the standard normal table.', 'start': 6882.101, 'duration': 2.781}, {'end': 6888.383, 'text': 'So guys, this is how you estimate the level of confidence.', 'start': 6884.902, 'duration': 3.481}, {'end': 6894.046, 'text': 'So to sum it up, let me tell you the steps that are involved in constructing a confidence interval.', 'start': 6889.184, 'duration': 4.862}, {'end': 6897.567, 'text': "First you'll start by identifying a sample statistic.", 'start': 6894.446, 'duration': 3.121}, {'end': 6902.029, 'text': 'Okay, this is the statistic that you will use to estimate a population parameter.', 'start': 6897.887, 'duration': 4.142}], 'summary': 'Z-scores estimate confidence levels using sample statistics.', 'duration': 25.99, 'max_score': 6876.039, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF06876039.jpg'}, {'end': 7053.288, 'src': 'embed', 'start': 7030.657, 'weight': 5, 'content': [{'end': 7038.439, 'text': 'We test whether or not the identified conclusion represents the population accurately and finally we interpret their results.', 'start': 7030.657, 'duration': 7.782}, {'end': 7044.421, 'text': 'Now whether or not to accept the hypothesis depends upon the percentage value that we get from the hypothesis.', 'start': 7038.819, 'duration': 5.602}, {'end': 7048.642, 'text': "So to better understand this, let's look at a small example.", 'start': 7045.241, 'duration': 3.401}, {'end': 7053.288, 'text': 'Before that, there are a few steps that are followed in hypothesis testing.', 'start': 7049.665, 'duration': 3.623}], 'summary': 'Testing conclusion accuracy, interpreting results, and evaluating hypothesis with percentage value in hypothesis testing.', 'duration': 22.631, 'max_score': 7030.657, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF07030657.jpg'}, {'end': 7174.74, 'src': 'embed', 'start': 7144.918, 'weight': 3, 'content': [{'end': 7154.641, 'text': 'Now 75% is fairly high, so if John is not picked for three days in a row, the probability will drop down to approximately 42%.', 'start': 7144.918, 'duration': 9.723}, {'end': 7161.083, 'text': 'Okay, so three days in a row meaning that is the probability drops down to 42%.', 'start': 7154.641, 'duration': 6.442}, {'end': 7166.324, 'text': "Now let's consider a situation where John is not picked for 12 days in a row.", 'start': 7161.083, 'duration': 5.241}, {'end': 7169.507, 'text': 'the probability drops down to 3.2.', 'start': 7166.96, 'duration': 2.547}, {'end': 7174.74, 'text': "okay, that's the probability of john cheating becomes fairly high, right?", 'start': 7169.507, 'duration': 5.233}], 'summary': 'If john is not picked for 12 days, probability of cheating becomes 3.2%.', 'duration': 29.822, 'max_score': 7144.918, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF07144918.jpg'}], 'start': 6023.27, 'title': 'Probability and statistical inference', 'summary': 'Covers bayes theorem and its applications, drawing blue balls probability, inferential statistics including point and interval estimation, and confidence intervals with hypothesis testing. it includes methods, significance, and practical examples.', 'chapters': [{'end': 6095.593, 'start': 6023.27, 'title': 'Bayes theorem and its application', 'summary': 'Introduces the bayes theorem as a method to show the relation between conditional probabilities, presenting its mathematical representation and key components, and highlights the importance of prior knowledge in determining the probability of an event occurring.', 'duration': 72.323, 'highlights': ['The Bayes Theorem is a method to show the relation between conditional probabilities, emphasizing the significance of prior knowledge in determining the probability of an event occurring.', 'The mathematical representation of the Bayes Theorem includes terms such as likelihood ratio, posterior, and prior, which are essential in calculating the probability of occurrence of events.', 'The chapter explains the significance of the likelihood ratio in measuring the probability of event occurrence given another event, providing a clear understanding of its role in the Bayes Theorem.']}, {'end': 6332.296, 'start': 6095.593, 'title': 'Probability of drawing blue balls', 'summary': 'Discusses the probability of drawing a blue ball from bowl a given that exactly two blue balls are drawn, using conditional probability and a formula, considering three different ways of picking exactly two blue balls.', 'duration': 236.703, 'highlights': ['The chapter discusses conditional probability and the formula for finding the probability of drawing a blue ball from bowl A given that exactly two blue balls are drawn, representing the events as A for picking a blue ball from bowl A and X for picking exactly two blue balls.', 'The chapter explains the three possible ways of picking exactly two blue balls, involving picking one blue ball from bowl A and one from bowl B, one from A and another blue ball from C, and a blue ball from bowl B and a blue ball from bowl C, emphasizing the need to find the probability of each of these ways.', 'The chapter emphasizes the importance of calculating the probability of picking a blue ball from bowl A given that exactly two blue balls are drawn, and the probability of picking exactly two blue balls as the two key probabilities to calculate.']}, {'end': 6739.151, 'start': 6332.316, 'title': 'Inferential statistics: point estimation and interval estimation', 'summary': 'Discusses inferential statistics, focusing on point estimation and interval estimation, including methods such as method of moments, maximum likelihood, base estimator, and best unbiased estimators, and the significance of confidence interval and margin of error in estimating population parameters with examples.', 'duration': 406.835, 'highlights': ['The chapter introduces inferential statistics, covering point estimation and interval estimation methods, along with the significance of confidence interval and margin of error in estimating population parameters.', 'The methods of point estimation, including method of moments, maximum likelihood, base estimator, and best unbiased estimators, are explained with the example of estimating population parameters.', 'The significance of confidence interval in measuring uncertainty and the example of a survey with a 99% confidence level and a confidence interval of 100 to 200 cans of cat food is provided.']}, {'end': 7174.74, 'start': 6739.151, 'title': 'Confidence intervals & hypothesis testing', 'summary': 'Explains confidence intervals, including the calculation of margin of error and how to estimate confidence intervals, as well as hypothesis testing, outlining the steps involved and providing a practical example.', 'duration': 435.589, 'highlights': ['The margin of error can be calculated using a formula involving the critical value, standard deviation, and sample size, with an example yielding a margin of error of approximately 8.12 for a sample of 32 textbooks.', 'Explanation of hypothesis testing, including the steps involved such as stating null and alternative hypotheses, formulating an analysis plan, analyzing sample data, and interpreting results, followed by a practical example involving probability calculations.', 'Calculation of the probability of an event using hypothesis testing, demonstrating how the probability changes based on the number of occurrences, with the probability dropping to approximately 42% when the event happens three days in a row.']}], 'duration': 1151.47, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF06023270.jpg', 'highlights': ['The significance of the likelihood ratio in measuring the probability of event occurrence given another event', 'The mathematical representation of the Bayes Theorem includes terms such as likelihood ratio, posterior, and prior', 'The Bayes Theorem is a method to show the relation between conditional probabilities, emphasizing the significance of prior knowledge', 'The chapter discusses conditional probability and the formula for finding the probability of drawing a blue ball from bowl A given that exactly two blue balls are drawn', 'The chapter emphasizes the importance of calculating the probability of picking a blue ball from bowl A given that exactly two blue balls are drawn', 'The chapter introduces inferential statistics, covering point estimation and interval estimation methods', 'The margin of error can be calculated using a formula involving the critical value, standard deviation, and sample size', 'Explanation of hypothesis testing, including the steps involved such as stating null and alternative hypotheses, formulating an analysis plan, analyzing sample data, and interpreting results', 'Calculation of the probability of an event using hypothesis testing, demonstrating how the probability changes based on the number of occurrences']}, {'end': 10897.657, 'segs': [{'end': 7796.262, 'src': 'embed', 'start': 7767.05, 'weight': 0, 'content': [{'end': 7768.991, 'text': 'Now this is quite self-explanatory.', 'start': 7767.05, 'duration': 1.941}, {'end': 7775.875, 'text': 'Basically algorithm is a set of rules or statistical techniques which are used to learn patterns from data.', 'start': 7769.111, 'duration': 6.764}, {'end': 7780.272, 'text': 'Now an algorithm is the logic behind a machine learning model.', 'start': 7776.69, 'duration': 3.582}, {'end': 7784.675, 'text': 'An example of a machine learning algorithm is linear regression.', 'start': 7781.313, 'duration': 3.362}, {'end': 7787.477, 'text': "I'm not sure how many of you have heard of linear regression.", 'start': 7784.935, 'duration': 2.542}, {'end': 7790.679, 'text': "It's the most simple and basic machine learning algorithm.", 'start': 7787.957, 'duration': 2.722}, {'end': 7793.08, 'text': 'Next we have model.', 'start': 7791.719, 'duration': 1.361}, {'end': 7796.262, 'text': 'Now model is the main component of machine learning.', 'start': 7793.48, 'duration': 2.782}], 'summary': 'A machine learning algorithm is a set of rules used to learn patterns from data, with linear regression being a basic example.', 'duration': 29.212, 'max_score': 7767.05, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF07767050.jpg'}, {'end': 8293.777, 'src': 'embed', 'start': 8268.522, 'weight': 2, 'content': [{'end': 8274.245, 'text': 'So methods like parameter tuning and cross validation can be used to improve the performance of the model.', 'start': 8268.522, 'duration': 5.723}, {'end': 8277.468, 'text': 'This is followed by the last stage which is predictions.', 'start': 8274.626, 'duration': 2.842}, {'end': 8282.931, 'text': 'So once the model is evaluated and improved, it is finally used to make predictions.', 'start': 8278.067, 'duration': 4.864}, {'end': 8288.332, 'text': 'The final output can be a categorical variable or it can be a continuous quantity.', 'start': 8283.507, 'duration': 4.825}, {'end': 8293.777, 'text': 'In our case, for predicting the occurrence of rainfall, the output will be a categorical variable.', 'start': 8288.772, 'duration': 5.005}], 'summary': 'Parameter tuning and cross validation improve model performance for predicting rainfall occurrence.', 'duration': 25.255, 'max_score': 8268.522, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF08268522.jpg'}, {'end': 8961.459, 'src': 'embed', 'start': 8926.145, 'weight': 4, 'content': [{'end': 8927.906, 'text': 'In other words, we have to minimize the error.', 'start': 8926.145, 'duration': 1.761}, {'end': 8932.428, 'text': 'This was a brief understanding of linear regression algorithm soon.', 'start': 8928.646, 'duration': 3.782}, {'end': 8934.589, 'text': "We'll jump to its mathematical implementation.", 'start': 8932.448, 'duration': 2.141}, {'end': 8943.767, 'text': 'All right, but for then, let me tell you this Suppose you draw a graph with speed on the x-axis and distance covered on the y-axis,', 'start': 8934.95, 'duration': 8.817}, {'end': 8945.148, 'text': 'with the time remaining constant.', 'start': 8943.767, 'duration': 1.381}, {'end': 8951.092, 'text': 'If you plot a graph between the speed traveled by the vehicle and the distance traveled in a fixed unit of time,', 'start': 8945.848, 'duration': 5.244}, {'end': 8952.613, 'text': 'then you will get a positive relationship.', 'start': 8951.092, 'duration': 1.521}, {'end': 8953.193, 'text': 'All right.', 'start': 8952.973, 'duration': 0.22}, {'end': 8961.459, 'text': 'So suppose the equation of line is y equal MX plus C, then in this case, why is the distance traveled in a fixed duration of time?', 'start': 8953.733, 'duration': 7.726}], 'summary': 'Linear regression minimizes error, shows positive relationship between speed and distance.', 'duration': 35.314, 'max_score': 8926.145, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF08926145.jpg'}, {'end': 10697.7, 'src': 'embed', 'start': 10659.587, 'weight': 1, 'content': [{'end': 10666.129, 'text': 'So what doctor will do it will perform various tests on the patient and will check whether the patient is actually ill or not.', 'start': 10659.587, 'duration': 6.542}, {'end': 10668.15, 'text': 'So what will be the features?', 'start': 10666.509, 'duration': 1.641}, {'end': 10671.371, 'text': 'so doctor can check the sugar level, the blood pressure?', 'start': 10668.15, 'duration': 3.221}, {'end': 10673.251, 'text': 'then what is the age of the patient?', 'start': 10671.371, 'duration': 1.88}, {'end': 10675.692, 'text': 'is it very small or is it an old person?', 'start': 10673.251, 'duration': 2.441}, {'end': 10678.113, 'text': 'then what is the previous medical history of that patient?', 'start': 10675.692, 'duration': 2.421}, {'end': 10681.214, 'text': 'and all of these features will be recorded by the doctor.', 'start': 10678.533, 'duration': 2.681}, {'end': 10686.596, 'text': 'and finally, doctor checks the patient data and determines the outcome of an illness and the severity of illness.', 'start': 10681.214, 'duration': 5.382}, {'end': 10691.718, 'text': 'So using all the data a doctor can identify whether a patient is ill or not.', 'start': 10687.056, 'duration': 4.662}, {'end': 10695.279, 'text': 'So these are the various use cases in which you can use logistic regression.', 'start': 10692.158, 'duration': 3.121}, {'end': 10697.7, 'text': 'Now, I guess enough of theory part.', 'start': 10695.939, 'duration': 1.761}], 'summary': "Doctor uses tests to check patient's health features, then records and analyzes data to determine illness outcome and severity.", 'duration': 38.113, 'max_score': 10659.587, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF010659587.jpg'}], 'start': 7175.6, 'title': 'Machine learning processes', 'summary': 'Explores hypothesis testing, probability, machine learning importance, applications, demand for skills, machine learning process, linear regression, logistic regression, and their practical implementations, emphasizing the exponential data generation rate, impact on businesses, and achieving an r square value of 0.63 in linear regression using python.', 'chapters': [{'end': 7217.707, 'start': 7175.6, 'title': 'Hypothesis testing and probability', 'summary': 'Explores the concept of hypothesis testing and probability, including the use of threshold values and the significance of null and alternate hypotheses in statistical analysis.', 'duration': 42.107, 'highlights': ['Statisticians define a threshold value to determine probability, such as 5%, to differentiate between cheating and luck in a given situation.', 'The chapter emphasizes the significance of null and alternate hypotheses in hypothesis testing, where the former approves the assumption and the latter disapproves it.']}, {'end': 7863.649, 'start': 7218.527, 'title': 'Importance of machine learning', 'summary': 'Discusses the importance and applications of machine learning, emphasizing the exponential data generation rate, its impact on businesses, and its role in solving complex problems. it highlights the demand for machine learning skills and provides examples of its applications in companies like netflix, facebook, and amazon.', 'duration': 645.122, 'highlights': ['Machine learning is in high demand, driven by the exponential data generation rate and its applications in various fields.', 'Exponential data generation rate: 2.5 quintillion bytes of data are generated daily, with an estimated increase to 1.7 MB per second for every individual by 2020.', "Applications of machine learning in companies like Netflix, Facebook, and Amazon: Examples include Netflix's recommendation engine, Facebook's auto-tagging feature, and Amazon's Alexa.", "Machine learning's role in solving complex problems: From detecting genes linked to diseases to building self-driving cars, it plays a crucial role in addressing complex challenges.", 'The definition of machine learning: It is a subset of artificial intelligence that enables machines to learn and improve from experience without explicit programming, with the ability to solve problems by gaining the ability to think.']}, {'end': 8388.53, 'start': 7863.649, 'title': 'Machine learning process', 'summary': 'Discusses the machine learning process, including defining the objective, data collection, preparation, exploration, and building and evaluating a machine learning model, with an example of predicting rain based on weather conditions.', 'duration': 524.881, 'highlights': ['The machine learning process involves defining the objective, data collection, preparation, exploration, building and evaluating a machine learning model, with an example of predicting rain based on weather conditions.', 'Training data is used to build the machine learning model and is usually larger than the testing data.', 'The machine learning model is evaluated using the testing data to check its efficiency and accuracy in predicting outcomes.']}, {'end': 9735.618, 'start': 8389.09, 'title': 'Understanding linear regression', 'summary': 'Provides an in-depth understanding of linear regression, including its definition, uses, comparison with logistic regression, selection criteria, mathematical implementation, and evaluation using r-squared value, emphasizing the process and significance of minimizing error and the impact of r-squared values on predictive models.', 'duration': 1346.528, 'highlights': ['Linear Regression Defined and its Uses', 'Comparison with Logistic Regression', 'Mathematical Implementation and Evaluation using R-squared value']}, {'end': 10361.1, 'start': 9735.978, 'title': 'Implementing linear regression using python', 'summary': 'Covers implementing linear regression using python, including importing the dataset, calculating mean values, finding coefficients, plotting the linear model, and calculating the r square value, achieving an r square value of 0.63, and implementing the model using scikit-learn with the same r2 score.', 'duration': 625.122, 'highlights': ['The chapter covers implementing linear regression using Python, including importing the dataset, calculating mean values, finding coefficients, plotting the linear model, and calculating the R square value, achieving an R square value of 0.63, and implementing the model using scikit-learn with the same R2 score.', 'The dataset consists of 237 rows and four columns, including gender, age range, head size in centimeter cube, and brain weights in gram.', 'The values of B1 and B naught are calculated as 0.263 and 325.57, respectively, for the linear regression model.']}, {'end': 10897.657, 'start': 10361.1, 'title': 'Logistic regression analysis', 'summary': 'Explains the concept of logistic regression, its equation, differences from linear regression, use cases, and practical implementations. it also delves into the titanic data analysis project and its significance in predicting survival based on various features.', 'duration': 536.557, 'highlights': ['Logistic regression equation derivation', 'Differences between linear and logistic regression', 'Use cases of logistic regression in real life', 'Practical implementation of logistic regression in Titanic data analysis']}], 'duration': 3722.057, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF07175600.jpg', 'highlights': ['Exponential data generation rate: 2.5 quintillion bytes of data are generated daily, with an estimated increase to 1.7 MB per second for every individual by 2020.', "Applications of machine learning in companies like Netflix, Facebook, and Amazon: Examples include Netflix's recommendation engine, Facebook's auto-tagging feature, and Amazon's Alexa.", 'The chapter covers implementing linear regression using Python, achieving an R square value of 0.63, and implementing the model using scikit-learn with the same R2 score.', 'The machine learning process involves defining the objective, data collection, preparation, exploration, building and evaluating a machine learning model, with an example of predicting rain based on weather conditions.', 'Statisticians define a threshold value to determine probability, such as 5%, to differentiate between cheating and luck in a given situation.']}, {'end': 13903.88, 'segs': [{'end': 11151.72, 'src': 'embed', 'start': 11122.224, 'weight': 8, 'content': [{'end': 11125.008, 'text': "So now let me just go back to presentation and let's see what is my next step.", 'start': 11122.224, 'duration': 2.784}, {'end': 11129.25, 'text': "So we're done with the collecting data next step is to analyze your data.", 'start': 11125.768, 'duration': 3.482}, {'end': 11136.453, 'text': 'So over here will be creating different plots to check the relationship between variables, as in how one variable is affecting the other.', 'start': 11129.61, 'duration': 6.843}, {'end': 11142.416, 'text': 'so you can simply explore your data set by making use of various columns and then you can plot a graph between them.', 'start': 11136.453, 'duration': 5.963}, {'end': 11144.577, 'text': 'So you can either plot a correlation graph.', 'start': 11142.436, 'duration': 2.141}, {'end': 11146.078, 'text': 'You can plot a distribution graph.', 'start': 11144.617, 'duration': 1.461}, {'end': 11147.318, 'text': "It's up to you guys.", 'start': 11146.398, 'duration': 0.92}, {'end': 11151.72, 'text': 'So let me just go back to my Jupiter notebook and let me analyze some of the data over here.', 'start': 11147.618, 'duration': 4.102}], 'summary': 'Data analysis involves creating plots to check relationships between variables, like correlation and distribution graphs.', 'duration': 29.496, 'max_score': 11122.224, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF011122224.jpg'}, {'end': 11182.515, 'src': 'embed', 'start': 11153.721, 'weight': 6, 'content': [{'end': 11161.393, 'text': 'So I just put this in header 2 Now to put this in here to I just have to go on code click on Markdown and I just run this suppose.', 'start': 11153.721, 'duration': 7.672}, {'end': 11166.097, 'text': 'Let us plot a count plot will compare between the passengers who survived and who did not survive.', 'start': 11161.413, 'duration': 4.684}, {'end': 11168.259, 'text': "So for that I'll be using the seaborne library.", 'start': 11166.318, 'duration': 1.941}, {'end': 11169.12, 'text': 'So over here.', 'start': 11168.519, 'duration': 0.601}, {'end': 11171.041, 'text': 'I have imported seaborne as SNS.', 'start': 11169.18, 'duration': 1.861}, {'end': 11172.763, 'text': "So I don't have to write the whole name.", 'start': 11171.402, 'duration': 1.361}, {'end': 11175.185, 'text': 'I simply say SNS dot count plot.', 'start': 11173.143, 'duration': 2.042}, {'end': 11182.515, 'text': "I'll say X is good to survive and the data that I'll be using is the Titanic data,", 'start': 11178.146, 'duration': 4.369}], 'summary': 'Using seaborn library to plot count comparison between surviving and non-surviving passengers in titanic dataset.', 'duration': 28.794, 'max_score': 11153.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF011153721.jpg'}, {'end': 11219.097, 'src': 'embed', 'start': 11192.38, 'weight': 5, 'content': [{'end': 11198.684, 'text': 'I have the count so 0 basically stands for did not survive and one stands for the passengers who did survive over here.', 'start': 11192.38, 'duration': 6.304}, {'end': 11205.768, 'text': 'You can see that around 550 of the passengers who did not survive and they were around 350 passengers who only survive.', 'start': 11198.704, 'duration': 7.064}, {'end': 11210.531, 'text': 'So here you can basically conclude that there are very less survivors than non-survivors.', 'start': 11206.168, 'duration': 4.363}, {'end': 11212.372, 'text': 'So this was the very first plot.', 'start': 11211.011, 'duration': 1.361}, {'end': 11219.097, 'text': 'Now let us plot another plot to compare the sex as to whether, out of all the passengers who survived and who did not survive,', 'start': 11212.732, 'duration': 6.365}], 'summary': 'Around 550 passengers did not survive, while approximately 350 did survive, indicating fewer survivors than non-survivors.', 'duration': 26.717, 'max_score': 11192.38, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF011192380.jpg'}, {'end': 11272.233, 'src': 'embed', 'start': 11239.012, 'weight': 0, 'content': [{'end': 11240.173, 'text': "Okay, I've done a mistake over here.", 'start': 11239.012, 'duration': 1.161}, {'end': 11246.21, 'text': 'So over here, you can see I have survived column on the x-axis and I have the count on the Y now.', 'start': 11240.888, 'duration': 5.322}, {'end': 11250.451, 'text': 'So here your blue color stands for your male passengers and orange stands for your female.', 'start': 11246.49, 'duration': 3.961}, {'end': 11255.74, 'text': 'So, as you can see here the passengers who did not survive, that has a value 0.', 'start': 11251.072, 'duration': 4.668}, {'end': 11259.123, 'text': 'So we can see that majority of males did not survive.', 'start': 11255.74, 'duration': 3.383}, {'end': 11263.526, 'text': 'and if we see the people who survived here, we can see the majority of females survive.', 'start': 11259.123, 'duration': 4.403}, {'end': 11266.368, 'text': 'So this basically concludes the gender of the survival rate.', 'start': 11263.866, 'duration': 2.502}, {'end': 11272.233, 'text': 'So it appears on average women were more than three times more likely to survive than men next.', 'start': 11266.689, 'duration': 5.544}], 'summary': 'Most females survived, indicating over 3 times higher survival rate than males.', 'duration': 33.221, 'max_score': 11239.012, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF011239012.jpg'}, {'end': 11371.044, 'src': 'embed', 'start': 11338.537, 'weight': 4, 'content': [{'end': 11340.358, 'text': "So we'll be using Pandas library for this.", 'start': 11338.537, 'duration': 1.821}, {'end': 11343.7, 'text': "I'll declare an array and I'll pass in the column that is age.", 'start': 11340.839, 'duration': 2.861}, {'end': 11347.502, 'text': 'So I plot and I want a histogram.', 'start': 11345.741, 'duration': 1.761}, {'end': 11348.623, 'text': "So I'll say plot dot hist.", 'start': 11347.522, 'duration': 1.101}, {'end': 11358.155, 'text': 'So you can notice over here that we have more of young passengers, or you can see the children between the ages 0 to 10,', 'start': 11351.77, 'duration': 6.385}, {'end': 11362.698, 'text': 'and then we have the average age people, and if you go ahead, lesser would be the population.', 'start': 11358.155, 'duration': 4.543}, {'end': 11365.3, 'text': 'So this is the analysis on the age column.', 'start': 11363.139, 'duration': 2.161}, {'end': 11371.044, 'text': 'So we saw that we have more young passengers and more mediocre age passengers which are traveling in the Titanic.', 'start': 11365.34, 'duration': 5.704}], 'summary': 'Using pandas, a histogram analysis shows more young and average age passengers on the titanic.', 'duration': 32.507, 'max_score': 11338.537, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF011338537.jpg'}, {'end': 11874.005, 'src': 'embed', 'start': 11845.726, 'weight': 3, 'content': [{'end': 11849.53, 'text': 'So I just copy this part and I just use the sum function to calculate the sum.', 'start': 11845.726, 'duration': 3.804}, {'end': 11857.731, 'text': 'So here that tells me that data set is clean as in the data set does not contain any null value or any any n value.', 'start': 11850.905, 'duration': 6.826}, {'end': 11861.074, 'text': 'So now we have Rangel a data you can say cleaner data.', 'start': 11858.231, 'duration': 2.843}, {'end': 11866.138, 'text': 'So here we have done just one step in data wrangling that is just removing one column out of it.', 'start': 11861.674, 'duration': 4.464}, {'end': 11867.86, 'text': 'Now you can do a lot of things.', 'start': 11866.499, 'duration': 1.361}, {'end': 11874.005, 'text': 'you can actually fill in the values with some other values, or you can just calculate the mean and then you can just fit in the null values.', 'start': 11867.86, 'duration': 6.145}], 'summary': 'Data wrangling involved removing one column and ensuring data is clean and complete.', 'duration': 28.279, 'max_score': 11845.726, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF011845726.jpg'}, {'end': 12320.852, 'src': 'embed', 'start': 12291.274, 'weight': 2, 'content': [{'end': 12294.015, 'text': 'So everything else are the features which leads to the survival rate.', 'start': 12291.274, 'duration': 2.741}, {'end': 12301.183, 'text': 'So once we have defined the independent variable and the dependent variable next step is to split your data into training and testing subset.', 'start': 12294.58, 'duration': 6.603}, {'end': 12306.686, 'text': "So for that we'll be using sklearn I just type in from sklearn dot cross validation.", 'start': 12301.703, 'duration': 4.983}, {'end': 12309.787, 'text': 'Import train test plate.', 'start': 12308.566, 'duration': 1.221}, {'end': 12320.852, 'text': 'Now here if you just click on shift and tab you can go to the documentation and you can just see the examples over here.', 'start': 12314.089, 'duration': 6.763}], 'summary': 'Features impact survival rate. data split using sklearn for training and testing.', 'duration': 29.578, 'max_score': 12291.274, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF012291274.jpg'}, {'end': 13139.266, 'src': 'embed', 'start': 13110.666, 'weight': 1, 'content': [{'end': 13115.911, 'text': 'So in your SUV prediction you can actually analyze, clean your data and you can do a lot of things.', 'start': 13110.666, 'duration': 5.245}, {'end': 13120.155, 'text': 'so you can just go ahead, pick up any data set and explore it as much as you can.', 'start': 13115.911, 'duration': 4.244}, {'end': 13132.943, 'text': 'Open your eyes and see around, you will find dozens of applications of machine learning which you are using and interacting with in your daily life.', 'start': 13125.66, 'duration': 7.283}, {'end': 13139.266, 'text': 'be it be using the face detection algorithm in Facebook or getting the recommendation for similar products from Amazon,', 'start': 13132.943, 'duration': 6.323}], 'summary': 'Analyze and explore suv prediction data for machine learning applications in daily life.', 'duration': 28.6, 'max_score': 13110.666, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF013110666.jpg'}, {'end': 13187.198, 'src': 'embed', 'start': 13164.178, 'weight': 7, 'content': [{'end': 13171.825, 'text': 'What are its various types where it is used or what its use cases now once you get your fundamental clear will jump to the decision tree part.', 'start': 13164.178, 'duration': 7.647}, {'end': 13176.789, 'text': "Under this first of all, I'll teach you to mathematically create a decision tree from scratch.", 'start': 13172.365, 'duration': 4.424}, {'end': 13184.316, 'text': "Then once you get your concepts clear, we'll see how you can write a decision tree classifier from scratch in Python using the cart algorithm.", 'start': 13177.069, 'duration': 7.247}, {'end': 13185.076, 'text': 'All right.', 'start': 13184.816, 'duration': 0.26}, {'end': 13187.198, 'text': 'I hope the agenda is clear to you guys.', 'start': 13185.577, 'duration': 1.621}], 'summary': 'Teaching decision tree creation and implementation in python using the cart algorithm.', 'duration': 23.02, 'max_score': 13164.178, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF013164178.jpg'}], 'start': 10898.158, 'title': 'Logistic regression, titanic data analysis, and data wrangling', 'summary': 'Discusses logistic regression, titanic data analysis, and data wrangling, covering the steps, tools, and visualization techniques, such as using libraries like pandas, numpy, seaborn, and matplotlib, analyzing the titanic dataset, and performing data wrangling for logistic regression, achieving 89% accuracy, and exploring various classification algorithms.', 'chapters': [{'end': 11022.847, 'start': 10898.158, 'title': 'Logistic regression data analysis', 'summary': 'Discusses the five key steps in logistic regression, including data analysis, data wrangling, model building, data splitting, and accuracy checking, while highlighting the use of libraries such as pandas, numpy, seaborn, and matplotlib for data analysis and plotting.', 'duration': 124.689, 'highlights': ['The chapter emphasizes the importance of data analysis and exploration to understand the dataset thoroughly before proceeding with logistic regression implementation, promoting a comprehensive approach to data understanding.', 'It highlights the significance of data wrangling, specifically focusing on the cleaning process by removing unnecessary items and null values, contributing to the overall data quality improvement for accurate model building.', 'The chapter stresses the crucial step of model building using the train data set, followed by testing using a test data set, aiming to achieve accurate predictions and demonstrate the effectiveness of the logistic regression model.', "It discusses the vital process of data splitting, which involves dividing the dataset into training and testing datasets, enabling the evaluation of the model's performance and accuracy.", 'The chapter underscores the use of essential libraries such as pandas for data analysis, numpy for scientific computation, seaborn for statistical plotting, and matplotlib for data visualization, emphasizing their significance in the logistic regression implementation.']}, {'end': 11406.448, 'start': 11023.288, 'title': 'Titanic data analysis', 'summary': 'Covers the process of importing the titanic dataset, displaying the top 10 rows, finding the total number of passengers (891), and analyzing the relationship between variables through various plots, including a count plot comparing survivors and non-survivors, a gender comparison plot showing that women were three times more likely to survive than men, a plot based on passenger class indicating that the majority of non-survivors belonged to the third class, and an analysis of the age and fare distribution.', 'duration': 383.16, 'highlights': ['The total number of passengers in the original data set is 891, indicating the number of individuals traveling on the Titanic (891).', 'Women were more than three times more likely to survive than men, as evidenced by the comparison plot based on gender.', 'The majority of non-survivors belonged to the third class, while survivors were predominantly from the higher classes (first and second class).', 'The analysis of the age column revealed a higher number of young passengers (0-10 years) and average age passengers on the Titanic.', 'The fare distribution analysis showed that the majority of fares were between 0 to 100, with a higher concentration in the lower fare range.']}, {'end': 11867.86, 'start': 11407.128, 'title': 'Titanic data analysis', 'summary': 'Discusses the analysis of titanic data, including columns left, survival rates, gender-based analysis, passenger class, sibling/spouse count, data wrangling steps, and removal of null values, with a maximum of 177 missing values in a column and a heatmap visualization of missing data.', 'duration': 460.732, 'highlights': ['The chapter discusses the analysis of Titanic data, including columns left, survival rates, gender-based analysis, passenger class, and sibling/spouse count.', 'Data wrangling steps are performed, including the removal of null values, with a maximum of 177 missing values in a column.', 'A heatmap visualization is used to identify missing data, with a maximum of 177 missing values in a column.']}, {'end': 12374.214, 'start': 11867.86, 'title': 'Data wrangling for logistic regression', 'summary': 'Discusses the process of data wrangling for logistic regression, including converting string values to categorical variables using pandas, creating dummy variables, dropping irrelevant columns, and splitting the dataset into training and testing subsets using sklearn with a 70-30 ratio.', 'duration': 506.354, 'highlights': ['The process of converting string values to categorical variables using Pandas and creating dummy variables is crucial for implementing logistic regression, as it ensures that the input variables do not contain string values.', "The chapter emphasizes the importance of dropping irrelevant columns, such as 'P class', 'embarked', 'sex', 'passenger ID', and 'name', to streamline the dataset for logistic regression analysis.", 'The process of splitting the dataset into training and testing subsets using sklearn with a 70-30 ratio and a random state of 1 is highlighted as a crucial step in preparing the data for logistic regression analysis.']}, {'end': 12927.427, 'start': 12375.293, 'title': 'Logistic regression model training and evaluation', 'summary': "Covers the process of training a logistic regression model on a dataset to predict outcomes, evaluating the model's accuracy, using a confusion matrix and classification report, and splitting the data into train and test subsets. furthermore, it discusses the application of logistic regression in predicting suv purchases based on user data.", 'duration': 552.134, 'highlights': ['The chapter covers the process of training a logistic regression model on a dataset to predict outcomes', "Evaluating the model's accuracy, using a confusion matrix and classification report", 'Discussion on splitting the data into train and test subsets', 'Application of logistic regression in predicting SUV purchases based on user data']}, {'end': 13903.88, 'start': 12927.968, 'title': 'Logistic regression and decision tree training', 'summary': 'Covers the application of logistic regression in predicting values and calculating accuracy, achieving 89% accuracy, followed by an introduction to decision tree, classification, and various types of classification algorithms, such as decision tree, random forest, naive bias, and knn algorithm.', 'duration': 975.912, 'highlights': ['The accuracy of the logistic regression model is 89% after predicting values and calculating accuracy.', 'Introduction to decision tree, classification, and various types of classification algorithms, including decision tree, random forest, naive bias, and KNN algorithm.', 'Decision tree is a graphical representation of all the possible solutions to a decision based on certain conditions, and it is one of the few interpretable models.']}], 'duration': 3005.722, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF010898158.jpg', 'highlights': ['Logistic regression model achieves 89% accuracy', 'Data wrangling involves removing null values, improving data quality', 'Importance of data analysis and exploration for model building', 'Use of essential libraries: pandas, numpy, seaborn, matplotlib', 'Significance of model building using train and test datasets', 'Analysis of Titanic data: survival rates, passenger class, gender', 'Visualization techniques: heatmap, comparison plot based on gender', 'Crucial step of splitting dataset into training and testing subsets', 'Importance of converting string values to categorical variables', 'Introduction to decision tree, random forest, naive bias, KNN algorithm']}, {'end': 15314.16, 'segs': [{'end': 13981.576, 'src': 'embed', 'start': 13959.327, 'weight': 4, 'content': [{'end': 13967.995, 'text': 'They have the same features but different labels both have yellow as their color and diameter as three but the labels are mango and lemon fine.', 'start': 13959.327, 'duration': 8.668}, {'end': 13971.378, 'text': "Let's move on and see how our decision tree handles this case.", 'start': 13968.375, 'duration': 3.003}, {'end': 13976.242, 'text': 'All right in order to build a tree will use a decision tree algorithm called cart.', 'start': 13971.838, 'duration': 4.404}, {'end': 13981.576, 'text': 'The Scott algorithm stands for classification and regression tree algorithm.', 'start': 13977.293, 'duration': 4.283}], 'summary': 'Decision tree algorithm cart used for classifying fruits with same features', 'duration': 22.249, 'max_score': 13959.327, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF013959327.jpg'}, {'end': 14048.729, 'src': 'embed', 'start': 14025.602, 'weight': 1, 'content': [{'end': 14034.672, 'text': 'There is no uncertainty about the type of label as it consists of only grapes right on the other hand the labels in this node are still mixed up.', 'start': 14025.602, 'duration': 9.07}, {'end': 14043.242, 'text': 'So we would ask another question to further drill it down, right? But before that we need to understand which question to ask and when.', 'start': 14035.453, 'duration': 7.789}, {'end': 14048.729, 'text': 'and to do that we need to quantify how much question helps to unmix the label,', 'start': 14044.105, 'duration': 4.624}], 'summary': "Labels consist of only grapes, but still mixed up. need to quantify question's help in unmixing.", 'duration': 23.127, 'max_score': 14025.602, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF014025602.jpg'}, {'end': 14095.182, 'src': 'embed', 'start': 14069.471, 'weight': 0, 'content': [{'end': 14075.124, 'text': "We'll continue dividing the data until there are no further question to ask and finally we reach to our leaf.", 'start': 14069.471, 'duration': 5.653}, {'end': 14075.806, 'text': 'All right.', 'start': 14075.585, 'duration': 0.221}, {'end': 14076.868, 'text': 'All right.', 'start': 14076.607, 'duration': 0.261}, {'end': 14078.392, 'text': 'So this was about decision tree.', 'start': 14077.048, 'duration': 1.344}, {'end': 14081.837, 'text': 'So, in order to create a decision tree, first of all, what do you have to do??', 'start': 14079.136, 'duration': 2.701}, {'end': 14089.34, 'text': 'You have to identify different set of questions that you can ask to a tree like is this color green and what will be these question?', 'start': 14081.897, 'duration': 7.443}, {'end': 14091.321, 'text': 'these questions will be decided by your data set.', 'start': 14089.34, 'duration': 1.981}, {'end': 14093.101, 'text': 'like is this colored green?', 'start': 14091.321, 'duration': 1.78}, {'end': 14095.182, 'text': 'is the diameter greater than equal to three?', 'start': 14093.101, 'duration': 2.081}], 'summary': 'Creating a decision tree involves identifying questions based on the data set to reach a leaf node.', 'duration': 25.711, 'max_score': 14069.471, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF014069471.jpg'}, {'end': 14137.643, 'src': 'embed', 'start': 14110.252, 'weight': 5, 'content': [{'end': 14118.418, 'text': 'all right, if the color is green or the diameter is greater than equal to three, or the color is yellow, vision tree terminologies.', 'start': 14110.252, 'duration': 8.166}, {'end': 14122.081, 'text': 'so, starting with root node, root node is the base node of a tree.', 'start': 14118.418, 'duration': 3.663}, {'end': 14124.163, 'text': 'the entire tree starts from a root node.', 'start': 14122.081, 'duration': 2.082}, {'end': 14126.932, 'text': 'In other words, it is the first node of a tree.', 'start': 14124.79, 'duration': 2.142}, {'end': 14137.643, 'text': 'It represents the entire population or sample and this entire population is further segregated or divided into two or more homogeneous set fine.', 'start': 14127.273, 'duration': 10.37}], 'summary': 'Tree node criteria: green, diameter>=3, or yellow. root node is base of entire tree, dividing population into homogeneous sets.', 'duration': 27.391, 'max_score': 14110.252, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF014110252.jpg'}, {'end': 14784.102, 'src': 'embed', 'start': 14757.796, 'weight': 2, 'content': [{'end': 14763.619, 'text': 'So total number of years when it was sunny was 2 and total number of no that was 3 fine.', 'start': 14757.796, 'duration': 5.823}, {'end': 14770.382, 'text': "So let's put up in the formula since the probability of yes is 2 by 5 and the probability of no is 3 by 5.", 'start': 14764.099, 'duration': 6.283}, {'end': 14771.683, 'text': 'So you will get something like this.', 'start': 14770.382, 'duration': 1.301}, {'end': 14772.323, 'text': 'All right.', 'start': 14772.123, 'duration': 0.2}, {'end': 14777.735, 'text': 'So you are getting the entropy of Sunny as 0.971 fine.', 'start': 14772.93, 'duration': 4.805}, {'end': 14781.019, 'text': "Next you'll calculate the entropy for overcast.", 'start': 14778.516, 'duration': 2.503}, {'end': 14784.102, 'text': 'when it was overcast, remember it was all yes, right?', 'start': 14781.019, 'duration': 3.083}], 'summary': "Total sunny years: 2, no. of no's: 3. calculated probabilities and entropy.", 'duration': 26.306, 'max_score': 14757.796, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF014757796.jpg'}, {'end': 15126.808, 'src': 'embed', 'start': 15099.496, 'weight': 3, 'content': [{'end': 15107.677, 'text': 'in case of temperature, the information was around 0.911 and the information gain that was equal to 0.029.', 'start': 15099.496, 'duration': 8.181}, {'end': 15115.299, 'text': 'in case of humidity, the information gain was 0.152 and in the case of windy, the information gain was 0.048..', 'start': 15107.677, 'duration': 7.622}, {'end': 15119.2, 'text': "So what we'll do will select the attribute with the maximum fine.", 'start': 15115.299, 'duration': 3.901}, {'end': 15126.808, 'text': 'Now we are selected Outlook as our root node and it is further subdivided into three different parts sunny overcast and rain.', 'start': 15119.9, 'duration': 6.908}], 'summary': 'Using decision tree analysis, attributes like temperature, humidity, and windy were evaluated, with information gains of 0.911, 0.152, and 0.048 respectively. outlook was chosen as the root node, further divided into sunny, overcast, and rain.', 'duration': 27.312, 'max_score': 15099.496, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF015099496.jpg'}], 'start': 13903.88, 'title': 'Decision trees and information gain', 'summary': 'Discusses the decision tree algorithm cart, key terminologies such as gini index, information gain, and entropy, attribute selection, and specific information gain calculations for the outlook feature with values and probabilities, providing insights into decision tree analysis and model comparison.', 'chapters': [{'end': 14215.101, 'start': 13903.88, 'title': 'Understanding decision trees', 'summary': 'Discusses the decision tree algorithm cart, which splits the data using true/false questions to create pure distributions of labels, and explains key decision tree terminologies such as root node, leaf node, splitting, branch, pruning, parent and child nodes.', 'duration': 311.221, 'highlights': ['The algorithm CART uses true/false questions to split the data and create pure distributions of labels, reducing uncertainty using the gene impurity metric and information gain.', 'Explains key decision tree terminologies such as root node, leaf node, splitting, branch, pruning, parent and child nodes, and their roles in the decision tree structure.']}, {'end': 14696.788, 'start': 14215.101, 'title': 'Decision tree and attribute selection', 'summary': 'Covers the process of creating a decision tree manually, including understanding key terminologies such as gini index, information gain, reduction in variance, g square, entropy, and information gain calculation, with a focus on attribute selection and root node determination for building the decision tree.', 'duration': 481.687, 'highlights': ['The chapter emphasizes the process of creating a decision tree manually and understanding key terminologies such as Gini index, information gain, reduction in variance, G square, entropy, and information gain calculation.', 'The importance of determining the best attribute for classifying the training data is highlighted, emphasizing the need to calculate information gain and select the attribute that returns the highest information gain.', 'The process of determining the root node for the decision tree is explained, focusing on calculating entropy and information gain for each of the different nodes to select the appropriate root node.']}, {'end': 14919.953, 'start': 14697.501, 'title': 'Decision tree information gain', 'summary': 'Explains the calculation of information gain for the outlook feature in a decision tree, with specific values and probabilities, resulting in an information gain of 0.247.', 'duration': 222.452, 'highlights': ['Calculation of information gain for the Outlook feature', 'Entropy calculation for different weather conditions', 'Weighted average calculation for information gain']}, {'end': 15314.16, 'start': 14920.193, 'title': 'Decision tree analysis', 'summary': "Describes the process of decision tree analysis using the example of calculating the information gained from the root node 'wendy', which resulted in an information gain of 0.048. it also discusses the concept of pruning and compares decision tree models with linear regression models.", 'duration': 393.967, 'highlights': ['The information gained from Wendy was 0.048, and it was calculated based on the entropy of Wendy when it was true and false, along with the total information collected from Wendy.', 'The concept of pruning in decision tree analysis involves reducing complexity by cutting down nodes to achieve the optimal solution.', 'Comparing decision tree models with linear regression models, it is highlighted that decision tree models outperform in cases of high non-linearity and complex relationships between dependent and independent variables.']}], 'duration': 1410.28, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF013903880.jpg', 'highlights': ['CART algorithm uses true/false questions to split data and reduce uncertainty (information gain)', 'Key terminologies: root node, leaf node, splitting, pruning, parent and child nodes', 'Importance of determining best attribute for classifying training data and calculating information gain', 'Calculation of information gain for the Outlook feature and weighted average calculation', 'Pruning in decision tree analysis involves reducing complexity to achieve optimal solution', 'Decision tree models outperform linear regression in cases of high non-linearity and complex relationships']}, {'end': 17213.586, 'segs': [{'end': 15516.753, 'src': 'embed', 'start': 15490.789, 'weight': 1, 'content': [{'end': 15495.132, 'text': 'it compares the feature value in an example to the feature value in this question.', 'start': 15490.789, 'duration': 4.343}, {'end': 15502.759, 'text': 'when next will define a function as our EPR, which is just a helper method to print the question in a readable format?', 'start': 15495.132, 'duration': 7.627}, {'end': 15503.479, 'text': 'next, what we are doing.', 'start': 15502.759, 'duration': 0.72}, {'end': 15505.101, 'text': 'We are defining a function partition.', 'start': 15503.519, 'duration': 1.582}, {'end': 15510.005, 'text': 'Well, this function is used to partition the data set, each row in the data set.', 'start': 15505.76, 'duration': 4.245}, {'end': 15512.087, 'text': 'it checks if it matches the question or not.', 'start': 15510.005, 'duration': 2.082}, {'end': 15516.753, 'text': 'if it does so, it adds it to the true rows or, if not, then it adds to the false rows.', 'start': 15512.087, 'duration': 4.666}], 'summary': 'Defining functions to compare feature values and partition dataset.', 'duration': 25.964, 'max_score': 15490.789, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF015490789.jpg'}, {'end': 16162.652, 'src': 'embed', 'start': 16131.514, 'weight': 2, 'content': [{'end': 16134.876, 'text': 'So random forest is an ensemble classifier actually.', 'start': 16131.514, 'duration': 3.362}, {'end': 16139.479, 'text': "Now let's understand what this word ensemble means.", 'start': 16135.397, 'duration': 4.082}, {'end': 16147.645, 'text': 'So ensemble methods actually use multiple machine learning algorithms to obtain better predictive performance.', 'start': 16140.2, 'duration': 7.445}, {'end': 16155.35, 'text': 'So particularly talking about random forest, so random forest uses multiple decision trees for prediction.', 'start': 16148.265, 'duration': 7.085}, {'end': 16162.652, 'text': 'right. so you are assembling a lot of decision trees to come up to your final outcome,', 'start': 16156.03, 'duration': 6.622}], 'summary': 'Random forest uses multiple decision trees for prediction, improving predictive performance.', 'duration': 31.138, 'max_score': 16131.514, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF016131514.jpg'}, {'end': 16244.108, 'src': 'embed', 'start': 16210.695, 'weight': 3, 'content': [{'end': 16214.658, 'text': 'You might have studied about naive Bayes Theorem in your 10 standard as well.', 'start': 16210.695, 'duration': 3.963}, {'end': 16218.402, 'text': "So let's just see what Bayes Theorem describes.", 'start': 16215.179, 'duration': 3.223}, {'end': 16230.084, 'text': 'Bayes-Rim actually describes the probability of an event based on certain prior knowledge of conditions that might be related to the event right.', 'start': 16220.3, 'duration': 9.784}, {'end': 16234.925, 'text': 'so, for example, if cancer is related to age right,', 'start': 16230.084, 'duration': 4.841}, {'end': 16244.108, 'text': "so then person's age can be used to more accurately assess probability of having a cancer than without having the knowledge of age.", 'start': 16234.925, 'duration': 9.183}], 'summary': "Bayes theorem describes probability based on prior knowledge, such as age's relation to cancer risk.", 'duration': 33.413, 'max_score': 16210.695, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF016210695.jpg'}, {'end': 16323.006, 'src': 'embed', 'start': 16293.944, 'weight': 4, 'content': [{'end': 16295.005, 'text': 'Where does it learn from?', 'start': 16293.944, 'duration': 1.061}, {'end': 16303.111, 'text': 'Well, a computer system actually learns from the data which represents some past experiences of an application domain.', 'start': 16295.866, 'duration': 7.245}, {'end': 16312.721, 'text': "so now let's see how random forest helps in building up the learning model with a very simple use case of credit risk detection.", 'start': 16303.757, 'duration': 8.964}, {'end': 16323.006, 'text': 'now, needless to say that credit card companies have a very nested interest in identifying financial transactions that are illegitimate and criminal in nature.', 'start': 16312.721, 'duration': 10.285}], 'summary': 'Computer system learns from data representing past experiences. random forest aids in credit risk detection for identifying illegitimate financial transactions.', 'duration': 29.062, 'max_score': 16293.944, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF016293944.jpg'}, {'end': 16385.519, 'src': 'embed', 'start': 16359.596, 'weight': 0, 'content': [{'end': 16368.399, 'text': 'a credit card company receives thousands of applications for new cards and each application contains information about an applicant, right?', 'start': 16359.596, 'duration': 8.803}, {'end': 16378.543, 'text': 'So here, as you can see that from all those applications, what we can actually figure out is our predictor variables, right?', 'start': 16370.44, 'duration': 8.103}, {'end': 16381.217, 'text': 'what is the marital status of the person?', 'start': 16379.056, 'duration': 2.161}, {'end': 16383.458, 'text': 'what is the gender of the person?', 'start': 16381.217, 'duration': 2.241}, {'end': 16385.519, 'text': 'what is the age of the person?', 'start': 16383.458, 'duration': 2.061}], 'summary': 'Credit card company processes thousands of applications to collect predictor variables like marital status, gender, and age.', 'duration': 25.923, 'max_score': 16359.596, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF016359596.jpg'}], 'start': 15314.16, 'title': 'Implementing decision tree and random forest in python', 'summary': 'Demonstrates the process of writing a decision tree classifier in python, building a decision tree algorithm for classification, introducing random forest and its application in credit risk detection and fraud detection, and discussing the decision-making process using decision trees and probability.', 'chapters': [{'end': 15544.906, 'start': 15314.16, 'title': 'Decision tree classifier in python', 'summary': 'Demonstrates the process of writing a decision tree classifier using python, initializing the training dataset, adding column labels, defining functions for unique values and class count, and implementing a class for partitioning the dataset based on specific questions.', 'duration': 230.746, 'highlights': ['The chapter demonstrates the process of writing a decision tree classifier using Python', 'It explains the process of initializing the training dataset and adding column labels', 'It defines functions for unique values and class count', 'It introduces a class for partitioning the dataset based on specific questions']}, {'end': 15831.343, 'start': 15546.947, 'title': 'Building decision tree algorithm', 'summary': 'Covers the implementation of a decision tree algorithm for classification, including defining information gain function, finding the best split, building the tree, and testing the algorithm, with an example result and conclusion.', 'duration': 284.396, 'highlights': ['The information gain function calculates the information gain using the uncertainty of the starting node, minus the weighted impurity of the child node.', 'The function for finding the best split iterates over every feature or value to calculate information gain for the best question to ask.', 'The build tree function is used to initially partition the data set for unique attributes, calculate information gain, and recursively build the decision tree.', "The testing of the algorithm results in the prediction of categories based on the decision tree, with an example result of predicting 'mango', 'lemon', or 'grape'.", 'The scikit-learn algorithm cheat sheet provides guidance on algorithm selection based on the number of samples and the nature of the prediction.']}, {'end': 16312.721, 'start': 15837.028, 'title': 'Introduction to random forest', 'summary': 'Covers a brief introduction to random forest, including its need, working, and application, as well as an overview of classification techniques, such as decision tree, random forest, and naive bayes, and the use of random forest in a credit risk detection case study.', 'duration': 475.693, 'highlights': ['Random forest is an ensemble classifier that uses multiple decision trees for prediction, achieving better predictive performance through the compilation of results from all the decision trees.', 'Classification is a machine learning technique used in supervised learning models to categorize data based on predefined categories.', 'Naive Bayes classifier is based on Bayes Theorem, which describes the probability of an event based on certain prior knowledge of conditions related to the event.', "Random forest's application in credit risk detection demonstrates how it helps in building learning models using past data experiences of an application domain.", 'Decision tree splits the entire data set in the structure of a tree and makes decisions at each node, thus aiding in making final decisions based on predefined parameters.']}, {'end': 16877.123, 'start': 16312.721, 'title': 'Random forest for credit card fraud detection', 'summary': 'Discusses the use of random forest in credit card fraud detection, highlighting the importance of identifying fraudulent transactions, the significant volume of credit card purchases in 2012, the estimated $6.1 billion loss due to unauthorized transactions, and the application of random forest in predicting loan approval based on income and age, leading to a final outcome through the compilation of decision trees.', 'duration': 564.402, 'highlights': ['The significant volume of credit card purchases in 2012, totaling 26.2 billion, and the estimated loss of US $6.1 billion due to unauthorized transactions that year.', 'The application of random forest in predicting loan approval based on income and age, leading to a final outcome through the compilation of decision trees.', 'The importance of identifying fraudulent transactions and minimizing financial damage through predictive variables such as marital status, gender, age, payment history, and income source.']}, {'end': 17213.586, 'start': 16877.143, 'title': 'Decision making: edge of tomorrow', 'summary': "Discusses decision-making process using decision trees and probability, based on friends' opinions, to determine whether to watch edge of tomorrow, emphasizing the role of genre preferences and cast preferences in influencing the final decision.", 'duration': 336.443, 'highlights': ["The probability of liking 'Edge of Tomorrow' is influenced by genre and cast preferences, as demonstrated by decision tree scenarios based on friends' opinions.", "The decision-making process involves considering multiple friends' opinions to make a precise decision, with each friend's input affecting the overall decision.", "The influence of genre preferences, such as adventure and science fiction, is highlighted in determining the likelihood of enjoying 'Edge of Tomorrow', with specific references to other movies and their genres."]}], 'duration': 1899.426, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF015314160.jpg', 'highlights': ['Random forest is an ensemble classifier using multiple decision trees for prediction', 'The chapter demonstrates the process of writing a decision tree classifier using Python', 'The significant volume of credit card purchases in 2012 totaled 26.2 billion', 'The information gain function calculates the information gain using the uncertainty of the starting node', "The probability of liking 'Edge of Tomorrow' is influenced by genre and cast preferences"]}, {'end': 18848.096, 'segs': [{'end': 17582.915, 'src': 'embed', 'start': 17550.818, 'weight': 1, 'content': [{'end': 17553.941, 'text': 'so every time you will get a new decision tree.', 'start': 17550.818, 'duration': 3.123}, {'end': 17556.397, 'text': 'So there will be variety right?', 'start': 17554.696, 'duration': 1.701}, {'end': 17562.221, 'text': 'So the classification model will be actually much more intelligent than the previous one.', 'start': 17556.737, 'duration': 5.484}, {'end': 17569.166, 'text': 'Now it has got varied experiences, so definitely it will make different decisions each time.', 'start': 17562.621, 'duration': 6.545}, {'end': 17576.21, 'text': 'And then when you will compile all those different decisions, it will be a new, more accurate and efficient result.', 'start': 17569.666, 'duration': 6.544}, {'end': 17582.915, 'text': 'So the first important step is to select certain number of features out of all the features.', 'start': 17577.291, 'duration': 5.624}], 'summary': 'Using varied decision trees results in a more intelligent and accurate classification model.', 'duration': 32.097, 'max_score': 17550.818, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF017550818.jpg'}, {'end': 17900.849, 'src': 'embed', 'start': 17877.037, 'weight': 0, 'content': [{'end': 17886.04, 'text': 'so now the final outcome out of this decision tree will be play, because here the ratio between the play and no play is 2 is to 1.', 'start': 17877.037, 'duration': 9.003}, {'end': 17890.101, 'text': 'so we get to a certain decision from a first decision tree.', 'start': 17886.04, 'duration': 4.061}, {'end': 17895.624, 'text': 'now let us look at the second subset now, since second subset has different number of variables.', 'start': 17890.101, 'duration': 5.523}, {'end': 17900.849, 'text': 'so that is why this decision tree is absolutely different from what we saw in our first subset.', 'start': 17895.624, 'duration': 5.225}], 'summary': "Decision tree outcome is 'play' with 2:1 ratio, leading to a different subset tree.", 'duration': 23.812, 'max_score': 17877.037, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF017877037.jpg'}, {'end': 18037.28, 'src': 'embed', 'start': 18001.44, 'weight': 3, 'content': [{'end': 18001.801, 'text': 'All right.', 'start': 18001.44, 'duration': 0.361}, {'end': 18005.783, 'text': "so now let's just have a look at various features of random forest, right?", 'start': 18001.801, 'duration': 3.982}, {'end': 18012.087, 'text': 'So the first and the foremost feature is that it is one of the most accurate learning algorithms, right?', 'start': 18006.384, 'duration': 5.703}, {'end': 18014.049, 'text': 'So why it is so??', 'start': 18012.548, 'duration': 1.501}, {'end': 18021.194, 'text': 'Because single decision trees are actually prone to having high variance or high bias.', 'start': 18014.109, 'duration': 7.085}, {'end': 18025.495, 'text': 'and, on the contrary, actually random forest.', 'start': 18022.114, 'duration': 3.381}, {'end': 18030.517, 'text': 'it averages the entire variance across the decision trees.', 'start': 18025.495, 'duration': 5.022}, {'end': 18037.28, 'text': "so let's say, if the variance is, say, x for decision tree, but for random forest, let's say,", 'start': 18030.517, 'duration': 6.763}], 'summary': 'Random forest is known for its high accuracy and variance reduction across decision trees.', 'duration': 35.84, 'max_score': 18001.44, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF018001440.jpg'}, {'end': 18154.025, 'src': 'embed', 'start': 18124.057, 'weight': 4, 'content': [{'end': 18128.838, 'text': 'Right. so while we are implementing multiple decision trees,', 'start': 18124.057, 'duration': 4.781}, {'end': 18139.24, 'text': 'so it has got implicit method which will automatically pick up some random features out of all your parameters and then it will go on and implementing different decision trees.', 'start': 18128.838, 'duration': 10.402}, {'end': 18146.522, 'text': 'So, for example, if you just give one simple command that all right, I want to implement 500 decision trees,', 'start': 18139.66, 'duration': 6.862}, {'end': 18154.025, 'text': 'no matter how so random forest will automatically take care and it will implement all those 500 decision trees.', 'start': 18146.522, 'duration': 7.503}], 'summary': 'Random forest automatically picks random features and implements 500 decision trees.', 'duration': 29.968, 'max_score': 18124.057, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF018124057.jpg'}], 'start': 17214.267, 'title': 'Random forest applications', 'summary': 'Explores the diverse applications of random forest in decision-making, including predicting loan defaults and disease probabilities in banking and medicine, and its use in weather prediction. it also discusses the algorithm overview, scalability, and its role in addressing balanced error in datasets, as well as its combination with knn algorithm for search applications and recommendation engines, generating over 35% of revenue for amazon.', 'chapters': [{'end': 17318.104, 'start': 17214.267, 'title': 'Applications of random forest', 'summary': 'Explores the application of random forest in decision-making by compiling results from different sources, and highlights its diverse use in domains such as banking and medicine for predicting loan defaults and disease probabilities.', 'duration': 103.837, 'highlights': ['Random forest is used in banking to predict whether an applicant will be a defaulter or non-defaulter, enabling the approval or rejection of loan applications.', 'In medicine, random forest is widely used to predict the probability of a person having a particular disease based on factors such as glucose concentration, BMI, insulin levels, and age.']}, {'end': 17732.143, 'start': 17318.104, 'title': 'Random forest algorithm overview', 'summary': 'Explains how random forest is used in various sectors such as medicine, land use, and marketing, and details the step-by-step process of the random forest algorithm, including the selection of features, node splitting, and majority voting for compiling results.', 'duration': 414.039, 'highlights': ['Random forest is used in medicine for predicting diabetes and in land use for deciding industry locations.', 'Random forest is applied in marketing to identify customer churn, particularly in e-commerce industries like Amazon and Flipkart.', 'The step-by-step process of the random forest algorithm is explained, including the selection of features, node splitting, and majority voting for compiling results.']}, {'end': 18193.966, 'start': 17733.396, 'title': 'Random forest: weather prediction', 'summary': 'Discusses how random forest is used to predict whether a match will take place based on weather conditions, and highlights the features of random forest including its accuracy, scalability, and implicit feature selection.', 'duration': 460.57, 'highlights': ['Random forest averages the entire variance across decision trees, reducing the final variance.', 'Random forest works well for both classification and regression problems.', 'Random forest is scalable and performs efficiently on large databases.', 'Random forest requires almost no input preparation and performs implicit feature selection.', 'Random forest can easily grow in parallel, reducing computation time.']}, {'end': 18848.096, 'start': 18194.878, 'title': 'Random forest and knn algorithms', 'summary': "Discusses the features and applications of random forest and knn algorithms, with random forest addressing balanced error in datasets and knn used for search applications, with amazon's recommendation engine generating over 35% of its revenue and knn applied in concept search and document classification. it also explains the working of knn algorithm and the process of predicting using the algorithm, highlighting the significance of k in the knn algorithm and the use of euclidean distance for predictions.", 'duration': 653.218, 'highlights': ['Random Forest addresses balanced error in datasets and is not biased towards any particular decision tree or class, ensuring balanced errors in datasets.', "Amazon's recommendation engine generates over 35% of its revenue, utilizing the KNN algorithm as a targeted marketing tool to increase average order value and upsell or cross-sell customers.", 'KNN algorithm is utilized in concept search and document classification, including applications in handwriting detection, OCR, image recognition, and video recognition.', "The KNN algorithm works by selecting the K nearest neighbors based on similarity measure and uses Euclidean distance for predictions, with the significance of K in the algorithm's prediction process."]}], 'duration': 1633.829, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF017214267.jpg', 'highlights': ['Random forest is used in banking to predict loan defaults, enabling approval or rejection of applications.', 'Random forest predicts disease probability based on factors like glucose concentration and age.', 'Random forest identifies customer churn in e-commerce industries like Amazon and Flipkart.', 'Random forest averages variance across decision trees, reducing the final variance.', 'Random forest addresses balanced error in datasets, ensuring balanced errors.', "Amazon's recommendation engine generates over 35% of revenue using the KNN algorithm.", 'KNN algorithm is utilized in concept search, document classification, OCR, and image recognition.', 'KNN algorithm selects K nearest neighbors based on similarity measure for predictions.']}, {'end': 20558.421, 'segs': [{'end': 19606.721, 'src': 'embed', 'start': 19578.373, 'weight': 4, 'content': [{'end': 19583.518, 'text': "It's rather confusing, right? So let's take an example to understand this theorem.", 'start': 19578.373, 'duration': 5.145}, {'end': 19593.772, 'text': 'So suppose I have a deck of cards, And if a single card is drawn from the deck of playing cards, the probability that the card is a king is 4 by 52,', 'start': 19584.258, 'duration': 9.514}, {'end': 19596.374, 'text': 'since there are four Kings in a standard deck of 52 cards.', 'start': 19593.772, 'duration': 2.602}, {'end': 19606.721, 'text': 'Now if King is an event this card is a king the probability of King is given as 4 by 52 that is equal to 1 by 13.', 'start': 19597.435, 'duration': 9.286}], 'summary': 'Probability of drawing a king from a deck of 52 cards is 1/13.', 'duration': 28.348, 'max_score': 19578.373, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF019578373.jpg'}, {'end': 20007.092, 'src': 'embed', 'start': 19981.399, 'weight': 1, 'content': [{'end': 19989.902, 'text': 'So now that you have an idea of what exactly is naive bias how it works and we have seen how it can be implemented on a particular data set.', 'start': 19981.399, 'duration': 8.503}, {'end': 19992.403, 'text': "Let's see where it is used in the industry.", 'start': 19990.482, 'duration': 1.921}, {'end': 19997.664, 'text': 'Starting with our first industrial use case, which is news categorization,', 'start': 19993.701, 'duration': 3.963}, {'end': 20002.789, 'text': 'or we can use the term text classification to broaden the spectrum of this algorithm.', 'start': 19997.664, 'duration': 5.125}, {'end': 20007.092, 'text': 'news in the web are rapidly growing in the era of information age,', 'start': 20002.789, 'duration': 4.303}], 'summary': 'Naive bias is used for news categorization in the industry.', 'duration': 25.693, 'max_score': 19981.399, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF019981399.jpg'}, {'end': 20545.335, 'src': 'embed', 'start': 20514.86, 'weight': 0, 'content': [{'end': 20520.622, 'text': 'We can open the file with the open function and read the data lines using the read functions in the CSV module.', 'start': 20514.86, 'duration': 5.762}, {'end': 20527.759, 'text': 'Now We also need to convert the attributes that were loaded as strings into numbers so that we can work with them.', 'start': 20521.454, 'duration': 6.305}, {'end': 20532.103, 'text': 'So let me show you how this can be implemented for that.', 'start': 20528.44, 'duration': 3.663}, {'end': 20538.449, 'text': 'You need to install python on your system and use the Jupiter notebook or the python shell.', 'start': 20532.163, 'duration': 6.286}, {'end': 20545.335, 'text': "Hey, I'm using the anaconda Navigator which has all the things required to do programming in python.", 'start': 20539.63, 'duration': 5.705}], 'summary': 'Using python, we can read data from a file, convert attributes from strings to numbers, and work with them. anaconda navigator has all the necessary tools.', 'duration': 30.475, 'max_score': 20514.86, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF020514860.jpg'}], 'start': 18848.156, 'title': 'Various algorithm implementations', 'summary': 'Covers the implementation of knn algorithm with a 67:33 test-to-train ratio using the iris dataset, achieving an accuracy rate of 97.29% with the canon algorithm, and discusses the concept and real-life application of naive bayes algorithm with various industrial use cases.', 'chapters': [{'end': 19062.083, 'start': 18848.156, 'title': 'Knn algorithm implementation', 'summary': 'Introduces the knn algorithm, explaining its lazy learning approach and steps for practical implementation, using the iris dataset with 150 observations, 4 features, and 1 class label. it covers data handling, similarity calculation, neighbor selection, response generation, accuracy function creation, and main function integration, achieving a 67:33 test-to-train ratio.', 'duration': 213.927, 'highlights': ['The iris dataset consists of 150 observations, 4 features, and 1 class label.', 'The chapter covers the steps for practical implementation of the KNN algorithm, including data handling, similarity calculation, neighbor selection, response generation, accuracy function creation, and main function integration.', 'The split ratio for the test and train data is set at 67:33, resulting in 97 training data sets and 53 test data sets.']}, {'end': 19425.067, 'start': 19062.263, 'title': 'Canon algorithm for iris data', 'summary': 'Discusses implementing the canon algorithm for iris data set, including calculating similarity using euclidean distance, selecting k-nearest neighbors, predicting response, and evaluating accuracy, achieving an accuracy rate of 97.29%.', 'duration': 362.804, 'highlights': ['The function load data set is performing well, and step 2 involves calculating similarity for making predictions.', 'The Euclidean distance measure is used to calculate the similarity between data instances, with a focus on controlling which fields to include in the distance calculation.', "The process involves defining functions for Euclidean distance, getting nearest neighbors, predicting response based on neighbors' majority vote, and evaluating the accuracy of the model.", 'The accuracy rate achieved for the Canon algorithm on the Iris data set is 97.29%.']}, {'end': 19976.897, 'start': 19430.532, 'title': 'Naive bayes in machine learning', 'summary': 'Introduces the concept of naive bayes algorithm, explains the bayes theorem and its application, and demonstrates the implementation of naive bayes in a real-life scenario, showcasing the likelihood calculation for classifying whether to play or not based on weather conditions.', 'duration': 546.365, 'highlights': ['The naive Bayes algorithm is a simple but powerful classification technique based on Bayes theorem with an assumption of independence among predictors, making it particularly useful for large data sets in probability theory and statistics. (Relevance Score: 5)', 'The Bayes theorem is a way to figure out conditional probability, relating the probability of hypothesis before and after getting evidence, where the likelihood ratio plays a crucial role in this relation. (Relevance Score: 4)', 'The application of Bayes theorem is demonstrated through a real-life scenario where the likelihood of playing or not playing based on weather conditions is calculated using the probability of outlook, humidity, and wind categories, providing a practical implementation of the theorem. (Relevance Score: 3)', 'The demonstration of the likelihood calculation for playing or not playing based on weather conditions showcases the practical implementation of Bayes theorem, resulting in the probability of playing or not playing, which is calculated using the likelihood of different weather conditions. (Relevance Score: 2)']}, {'end': 20558.421, 'start': 19981.399, 'title': 'Applications of naive bayes classifier', 'summary': 'Discusses the industrial applications of the naive bayes classifier, including news categorization, spam filtering, object detection, medical diagnosis, and weather prediction, demonstrating its effectiveness through empirical comparisons and specific use cases.', 'duration': 577.022, 'highlights': ['Naive Bayes classifier is widely used in news categorization and text classification, where it helps in classifying news articles based on user preferences, with an example of its implementation in web news categorization.', 'It is a popular statistical technique for email filtering, using bag-of-words features to identify spam emails and achieve low false-positive spam detection rates, with roots dating back to the 1990s.', 'Naive Bayes is used in medical diagnosis, proving effective in analyzing voluminous and complicated medical data, and outperforming other classifiers in empirical comparisons on medical datasets.', 'It is utilized in weather prediction, aiming to reduce the damage caused by weather uncertainty, although the accuracy of weather prediction remains a challenging problem.', 'The chapter also covers the implementation of a Naive Bayes model for predicting the onset of diabetes using medical details of Pima Indian patients, demonstrating the practical application of the algorithm in healthcare.']}], 'duration': 1710.265, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF018848156.jpg', 'highlights': ['The accuracy rate achieved for the Canon algorithm on the Iris data set is 97.29%.', 'The split ratio for the test and train data is set at 67:33, resulting in 97 training data sets and 53 test data sets.', 'The iris dataset consists of 150 observations, 4 features, and 1 class label.', 'The chapter covers the steps for practical implementation of the KNN algorithm, including data handling, similarity calculation, neighbor selection, response generation, accuracy function creation, and main function integration.', 'The naive Bayes algorithm is a simple but powerful classification technique based on Bayes theorem with an assumption of independence among predictors, making it particularly useful for large data sets in probability theory and statistics.']}, {'end': 22157.852, 'segs': [{'end': 20611.699, 'src': 'embed', 'start': 20588.226, 'weight': 1, 'content': [{'end': 20598.211, 'text': "So, as you can see, I've created a load CSV function which will take the Pima Indian diabetes data dot CSV file using the CSV dot reader method,", 'start': 20588.226, 'duration': 9.985}, {'end': 20603.274, 'text': 'and then we are converting every element of that data set into float.', 'start': 20598.211, 'duration': 5.063}, {'end': 20606.256, 'text': 'originally, all the elements are in string.', 'start': 20603.274, 'duration': 2.982}, {'end': 20610.358, 'text': 'but we need to convert them into float for all calculation purposes.', 'start': 20606.755, 'duration': 3.603}, {'end': 20611.699, 'text': 'So next,', 'start': 20611.319, 'duration': 0.38}], 'summary': 'Created load csv function for pima indian diabetes data to convert elements into float.', 'duration': 23.473, 'max_score': 20588.226, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF020588226.jpg'}, {'end': 21566.286, 'src': 'embed', 'start': 21536.209, 'weight': 4, 'content': [{'end': 21539.89, 'text': 'Alright, so SVM can be used both for classification and for regression.', 'start': 21536.209, 'duration': 3.681}, {'end': 21548.033, 'text': "Now this is one of the reasons why a lot of people prefer SVM because it's a very good classifier and along with that it is also used for regression.", 'start': 21540.37, 'duration': 7.663}, {'end': 21551.94, 'text': 'Another feature is the SVM kernel functions.', 'start': 21549.378, 'duration': 2.562}, {'end': 21556.862, 'text': 'SVM can be used for classifying non-linear data by using the kernel trick.', 'start': 21552.44, 'duration': 4.422}, {'end': 21566.286, 'text': 'The kernel trick basically means to transform your data into another dimension so that you can easily draw a hyperplane between the different classes of the data.', 'start': 21557.362, 'duration': 8.924}], 'summary': 'Svm is used for classification and regression, favored for its good classifier and ability to classify non-linear data using kernel trick.', 'duration': 30.077, 'max_score': 21536.209, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF021536209.jpg'}, {'end': 21643.015, 'src': 'embed', 'start': 21618.247, 'weight': 0, 'content': [{'end': 21624.954, 'text': 'So if I do that and if I try to draw a decision boundary between the rabbits and the wolves, it looks something like this.', 'start': 21618.247, 'duration': 6.707}, {'end': 21628.471, 'text': 'Okay, now you can clearly build a fence along this line.', 'start': 21625.37, 'duration': 3.101}, {'end': 21631.552, 'text': 'In simple terms, this is exactly how SVM works.', 'start': 21628.811, 'duration': 2.741}, {'end': 21639.314, 'text': 'It draws a decision boundary, which is a hyperplane between any two classes in order to separate them or classify them.', 'start': 21631.892, 'duration': 7.422}, {'end': 21640.854, 'text': "Now, I know you're thinking.", 'start': 21639.634, 'duration': 1.22}, {'end': 21643.015, 'text': 'how do you know where to draw a hyperplane?', 'start': 21640.854, 'duration': 2.161}], 'summary': 'Svm draws hyperplane to separate classes, enabling fence-like boundary for classification.', 'duration': 24.768, 'max_score': 21618.247, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF021618247.jpg'}], 'start': 20559.202, 'title': 'Python programming and machine learning models', 'summary': "Covers the use of jupyter notebook for python programming, including data loading and conversion, as well as the implementation of naive bias and support vector machine models for data split, prediction, and classification, achieving an accuracy of 68% and precision and recall of 0.96, while also discussing svm's real-world applications in cancer classification and unsupervised learning methods.", 'chapters': [{'end': 20611.699, 'start': 20559.202, 'title': 'Jupyter notebook for python programming', 'summary': 'Explains how to use jupyter notebook for python programming, including loading a dataset, converting elements into float, and importing necessary methods.', 'duration': 52.497, 'highlights': ["A function 'load CSV' is created to load the Pima Indian diabetes data from a CSV file and convert each element into float for calculation purposes.", 'The Jupyter notebook is launched, taking the user to the Jupyter home page, where they can program in Python.', 'Importing necessary methods such as CSV, math, and random is required before creating the load CSV function.']}, {'end': 21002.129, 'start': 20611.699, 'title': 'Naive bias model for data split and prediction', 'summary': 'Discusses the process of splitting data into training and testing sets in a 67-33 ratio, summarizing the data attributes, and making predictions using a naive bias model, resulting in an accuracy of 68% for a specific dataset.', 'duration': 390.43, 'highlights': ['The accuracy of the model is 68% with a 67-33 split ratio for the dataset.', 'The process involves summarizing the data attributes by calculating mean and standard deviation for each class value.', 'The process includes calculating Gaussian probability density function and estimating the accuracy of the model.']}, {'end': 21445.557, 'start': 21002.129, 'title': 'Implementing naive bias classifier with scikit-learn', 'summary': 'Explains the implementation of a naive bias classifier using scikit-learn library, with a demonstration on the iris dataset, achieving precision and recall of 0.96, and it also introduces the support vector machine (svm) as an effective machine learning classifier used in various fields.', 'duration': 443.428, 'highlights': ['The chapter explains the implementation of a Naive Bias Classifier using scikit-learn library on the Iris dataset, achieving precision and recall of 0.96.', 'Introduction to support vector machine (SVM) as an effective machine learning classifier used in various fields.', 'Explanation of different types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.']}, {'end': 22157.852, 'start': 21446.237, 'title': 'Support vector machine (svm) in a nutshell', 'summary': "Discusses the svm algorithm, its features, working principle, and real-world application, emphasizing its effectiveness in cancer classification and its use cases in the industry. svm is a supervised learning algorithm used for classification and regression, employing a hyperplane to separate data into different classes and is also capable of classifying non-linear data using kernel functions. svm's real-world application in cancer classification demonstrated its accuracy even with small data sets, outperforming other algorithms. the chapter also introduces the concept of unsupervised learning, particularly focusing on clustering as a method used to draw inferences from unlabeled data.", 'duration': 711.615, 'highlights': ["SVM's real-world application in cancer classification demonstrated its accuracy even with small data sets, outperforming other algorithms.", 'SVM is a supervised learning algorithm used for classification and regression, employing a hyperplane to separate data into different classes and is also capable of classifying non-linear data using kernel functions.', 'The chapter introduces the concept of unsupervised learning, particularly focusing on clustering as a method used to draw inferences from unlabeled data.']}], 'duration': 1598.65, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF020559202.jpg', 'highlights': ["SVM's real-world application in cancer classification demonstrated its accuracy even with small data sets, outperforming other algorithms.", 'The chapter explains the implementation of a Naive Bias Classifier using scikit-learn library on the Iris dataset, achieving precision and recall of 0.96.', 'The accuracy of the model is 68% with a 67-33 split ratio for the dataset.', 'Introduction to support vector machine (SVM) as an effective machine learning classifier used in various fields.', "A function 'load CSV' is created to load the Pima Indian diabetes data from a CSV file and convert each element into float for calculation purposes."]}, {'end': 23963.875, 'segs': [{'end': 22275.8, 'src': 'embed', 'start': 22248.118, 'weight': 3, 'content': [{'end': 22251, 'text': "Now, if you have a look at the algorithm working here, you're right.", 'start': 22248.118, 'duration': 2.882}, {'end': 22256.623, 'text': 'So first of all, it starts with identifying the number of clusters, which is key.', 'start': 22251.58, 'duration': 5.043}, {'end': 22258.505, 'text': 'Then again, we find the centroid.', 'start': 22257.044, 'duration': 1.461}, {'end': 22265.529, 'text': 'We find the distance objects to the distance object to the centroid distance of objects to the centroid.', 'start': 22258.925, 'duration': 6.604}, {'end': 22268.651, 'text': 'Then we find the grouping based on the minimum distance.', 'start': 22265.809, 'duration': 2.842}, {'end': 22270.814, 'text': 'has the centroid converge?', 'start': 22269.171, 'duration': 1.643}, {'end': 22273.117, 'text': 'if true, then we make a cluster.', 'start': 22270.814, 'duration': 2.303}, {'end': 22275.8, 'text': 'if false, we then again find the centroid.', 'start': 22273.117, 'duration': 2.683}], 'summary': 'Algorithm identifies clusters, finds centroid, and groups based on distance. converges to form clusters.', 'duration': 27.682, 'max_score': 22248.118, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF022248118.jpg'}, {'end': 22862.032, 'src': 'embed', 'start': 22832.12, 'weight': 0, 'content': [{'end': 22835.784, 'text': 'So first of all, it allows a data point to be in multiple clusters.', 'start': 22832.12, 'duration': 3.664}, {'end': 22836.365, 'text': "That's a pro.", 'start': 22835.804, 'duration': 0.561}, {'end': 22839.428, 'text': "It's a more neutral representation of the behavior of genes.", 'start': 22836.685, 'duration': 2.743}, {'end': 22842.371, 'text': 'Genes usually are involved in multiple functions.', 'start': 22839.989, 'duration': 2.382}, {'end': 22846.896, 'text': "So it is a very good type of clustering when we're talking about genes.", 'start': 22842.872, 'duration': 4.024}, {'end': 22855.146, 'text': 'First of all, and again, if we talk about the cons, again, we have to define C, which is the number of clusters, same as K.', 'start': 22847.64, 'duration': 7.506}, {'end': 22857.889, 'text': 'Next, we need to determine the membership cutoff value also.', 'start': 22855.146, 'duration': 2.743}, {'end': 22862.032, 'text': "So that takes a lot of time and it's time consuming.", 'start': 22857.989, 'duration': 4.043}], 'summary': 'Allows data points in multiple clusters, good for gene clustering, but time-consuming to define c and membership cutoff.', 'duration': 29.912, 'max_score': 22832.12, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF022832120.jpg'}, {'end': 23523.001, 'src': 'embed', 'start': 23497.415, 'weight': 2, 'content': [{'end': 23503.5, 'text': 'Now the first step is to build a list of item sets of size 1 by using this transactional data.', 'start': 23497.415, 'duration': 6.085}, {'end': 23509.217, 'text': 'and one thing to note here is that the minimum support count which is given here is 2.', 'start': 23504.156, 'duration': 5.061}, {'end': 23510.918, 'text': "let's suppose it's 2.", 'start': 23509.217, 'duration': 1.701}, {'end': 23516.399, 'text': 'so the first step is to create item sets of size 1 and calculate their support values.', 'start': 23510.918, 'duration': 5.481}, {'end': 23523.001, 'text': 'so, as you can see here, we have the table C1, in which we have the item sets 1, 2, 3, 4, 5 and the support values.', 'start': 23516.399, 'duration': 6.602}], 'summary': 'Building item sets of size 1 with minimum support count of 2.', 'duration': 25.586, 'max_score': 23497.415, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF023497415.jpg'}, {'end': 23610.249, 'src': 'embed', 'start': 23584.682, 'weight': 1, 'content': [{'end': 23591.024, 'text': 'Now, if you calculate the support here again, we can see that the item set 1 comma 2 has a support of 1,', 'start': 23584.682, 'duration': 6.342}, {'end': 23594.225, 'text': 'which is again less than the specified threshold.', 'start': 23591.024, 'duration': 3.201}, {'end': 23595.905, 'text': "So we're going to discard that.", 'start': 23594.785, 'duration': 1.12}, {'end': 23604.627, 'text': 'So, if we have a look at the table F2, we have 1 comma 3, 1, 5, 2, 3, 2, 5 and 3, 5.', 'start': 23596.785, 'duration': 7.842}, {'end': 23610.249, 'text': "again, we're going to move forward and create the item set of size 3 and calculate the support values.", 'start': 23604.627, 'duration': 5.622}], 'summary': 'Item set 1,2 has support of 1, less than threshold.', 'duration': 25.567, 'max_score': 23584.682, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF023584682.jpg'}], 'start': 22157.852, 'title': 'Clustering techniques and a priori algorithm', 'summary': 'Covers the three types of clustering - exclusive, overlapping, and hierarchical, with a detailed explanation of the k-means clustering algorithm and the a priori algorithm, including key concepts, use cases, and the importance of deciding the number of clusters and association rule mining for effective product placement and revenue generation.', 'chapters': [{'end': 22380.173, 'start': 22157.852, 'title': 'Clustering techniques and k-means algorithm', 'summary': 'Discusses the three types of clustering - exclusive, overlapping, and hierarchical, with a detailed explanation of the k-means clustering algorithm, including the process, steps, and the importance of deciding the number of clusters using the elbow method.', 'duration': 222.321, 'highlights': ["K-means clustering: Algorithm's main goal is to group similar data points into a cluster based on dissimilarity between groups and similarity within each group.", 'Types of clustering: Exclusive, overlapping, and hierarchical clustering are explained with examples and characteristics.', 'Determining number of clusters: Importance of deciding the number of clusters and the use of the elbow method for this purpose is emphasized.', "Algorithm working: Detailed explanation of the K-means algorithm's process, including identifying clusters, finding centroids, and calculating distances."]}, {'end': 22645.432, 'start': 22380.173, 'title': 'K-means clustering for movie data', 'summary': 'Discusses the process of choosing the number of clusters using the sum squared error (sse) and the elbow method, the key points to consider while working with k-means clustering, as well as the pros and cons of k-means clustering. it then outlines the use case of using k-means clustering for grouping 5000 movies into clusters based on facebook likes.', 'duration': 265.259, 'highlights': ['The key idea of the ELBO method is to choose the key at which the SSE decreases abruptly, as seen in the graph where the best number of clusters is at the elbow, indicating that four is the optimal number of clusters for the given example.', 'It is important to be careful about where the k-means clustering starts, by choosing the centers at random, ensuring the second center is far away from the first, and selecting the nth center as far away as possible from the closest of all the other centers.', 'K-means clustering is simple and understandable, with automatic assignment of items to clusters, but a major drawback is the need to define the number of clusters, which can be difficult without prior knowledge.', 'The k-means clustering method struggles to handle noisy data and outliers, posing challenges for machine learning engineers and data scientists as they work with uncleaned data.']}, {'end': 22972.471, 'start': 22645.872, 'title': 'Clustering techniques overview', 'summary': 'Provides an overview of k-means clustering, fuzzy c-means clustering, and hierarchical clustering, highlighting their key concepts and pros and cons, including the importance of sklearn library in python for machine learning and the differences between hard and soft clustering.', 'duration': 326.599, 'highlights': ['The chapter discusses k-means clustering, demonstrating how to import and use it to find cluster centers, with an example of five clusters and a mention of how the number of clusters depends on SSE (sum squared errors).', 'It explains fuzzy C-means clustering as an extension of K-means clustering, emphasizing the concept of soft clustering where each data point can belong to more than one cluster, and the assignment of the degree of membership to an object to a given cluster.', 'The chapter introduces hierarchical clustering, outlining the process of building a hierarchy from the bottom up or top to bottom, and the two types of hierarchical clustering: agglomerative and divisive clustering.']}, {'end': 23963.875, 'start': 22977.773, 'title': 'A priori algorithm and market basket analysis', 'summary': 'Delves into the a priori algorithm and market basket analysis, highlighting the importance of association rule mining in uncovering item associations for effective product placement and revenue generation, and details the steps involved in a priori algorithm, including the use of support, confidence, and lift measures to filter and create association rules from frequent item sets.', 'duration': 986.102, 'highlights': ['Association rule mining is crucial for effective product placement and revenue generation, as it uncovers item associations to target customers with strategic offers, increasing the likelihood of additional purchases.', "The a priori algorithm's key steps involve utilizing support, confidence, and lift measures to filter and create association rules from frequent item sets, ensuring the generation of actionable insights without creating an overwhelming number of rules.", 'Data cleanup and consolidation are essential steps in preparing the transactional data for association rule mining, ensuring the data set is optimized for deriving meaningful insights.']}], 'duration': 1806.023, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF022157852.jpg', 'highlights': ["K-means clustering: Algorithm's main goal is to group similar data points into a cluster based on dissimilarity between groups and similarity within each group.", 'Types of clustering: Exclusive, overlapping, and hierarchical clustering are explained with examples and characteristics.', 'Determining number of clusters: Importance of deciding the number of clusters and the use of the elbow method for this purpose is emphasized.', 'Association rule mining is crucial for effective product placement and revenue generation, as it uncovers item associations to target customers with strategic offers, increasing the likelihood of additional purchases.']}, {'end': 24863.356, 'segs': [{'end': 24209.924, 'src': 'embed', 'start': 24153.134, 'weight': 0, 'content': [{'end': 24159.336, 'text': 'Okay, and with many hurdles in between the agent is supposed to find the best possible path to reach the reward.', 'start': 24153.134, 'duration': 6.202}, {'end': 24162.336, 'text': 'So guys, I hope you all are clear with the reinforcement learning.', 'start': 24159.816, 'duration': 2.52}, {'end': 24164.877, 'text': "Now, let's look at the reinforcement learning process.", 'start': 24162.757, 'duration': 2.12}, {'end': 24169.245, 'text': 'So generally a reinforcement learning system has two main components.', 'start': 24165.543, 'duration': 3.702}, {'end': 24173.066, 'text': 'The first is an agent and the second one is an environment.', 'start': 24169.985, 'duration': 3.081}, {'end': 24180.249, 'text': 'Now in the previous case we saw that the agent was a baby and the environment was a living room wherein the baby was crawling.', 'start': 24173.446, 'duration': 6.803}, {'end': 24187.853, 'text': 'The environment is the setting that the agent is acting on and the agent over here represents the reinforcement learning algorithm.', 'start': 24180.79, 'duration': 7.063}, {'end': 24188.673, 'text': 'So, guys,', 'start': 24188.313, 'duration': 0.36}, {'end': 24197.518, 'text': 'the reinforcement learning process starts when the environment sends a state to the agent and then the agent will take some actions based on the observations.', 'start': 24188.673, 'duration': 8.845}, {'end': 24202.621, 'text': 'In turn, the environment will send the next state and the respective reward back to the agent.', 'start': 24198.038, 'duration': 4.583}, {'end': 24209.924, 'text': 'The agent will update its knowledge with a reward returned by the environment and it uses that to evaluate its previous action.', 'start': 24203.101, 'duration': 6.823}], 'summary': 'Reinforcement learning involves an agent navigating an environment to maximize rewards.', 'duration': 56.79, 'max_score': 24153.134, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF024153134.jpg'}, {'end': 24369.55, 'src': 'embed', 'start': 24341.366, 'weight': 5, 'content': [{'end': 24347.068, 'text': 'Okay, so an agent takes actions like for example a soldier in Counter-Strike navigating through the game.', 'start': 24341.366, 'duration': 5.702}, {'end': 24348.208, 'text': "That's also an action.", 'start': 24347.288, 'duration': 0.92}, {'end': 24351.93, 'text': "Okay, if he moves left right or if he shoots at somebody that's also an action.", 'start': 24348.348, 'duration': 3.582}, {'end': 24355.731, 'text': 'Okay, so the agent is responsible for taking actions in the environment.', 'start': 24352.37, 'duration': 3.361}, {'end': 24358.692, 'text': 'Now the environment is the whole Counter-Strike game.', 'start': 24356.251, 'duration': 2.441}, {'end': 24361.473, 'text': "Okay, it's basically the world through which the agent moves.", 'start': 24358.912, 'duration': 2.561}, {'end': 24369.55, 'text': "The environment takes the agent's current state and action as input and it returns the agent's reward and its next state as output.", 'start': 24362.096, 'duration': 7.454}], 'summary': 'An agent in counter-strike takes actions in the game environment, impacting its current state and potential rewards.', 'duration': 28.184, 'max_score': 24341.366, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF024341366.jpg'}], 'start': 23964.635, 'title': 'Association rules and reinforcement learning', 'summary': "Discusses generating frequent item sets with support of at least 7% and adding constraints on rules such as lift > 6 and confidence > 0.8. it also introduces reinforcement learning concepts including agent, environment, state, action, reward, policy, value, action value, reward maximization, discounting, exploration, exploitation, and markov's decision process, emphasizing the goal of maximizing rewards.", 'chapters': [{'end': 24027.749, 'start': 23964.635, 'title': 'Association rules - a priori algorithm', 'summary': 'Discusses generating frequent item sets with support at least 7%, adding constraints on the rules such as lift > 6 and confidence > 0.8, and creating association rules using the a priori algorithm for market basket analysis.', 'duration': 63.114, 'highlights': ['Creating association rules using the a priori algorithm helps in market basket analysis for big companies like Walmart, Reliance, Target, and IKEA.', 'Generating frequent item sets with support at least 7% is essential for obtaining close enough rules with corresponding support, confidence, and lift.', 'Adding constraints on the rules such as lift > 6 and confidence > 0.8 refines the association rules for more specific insights.']}, {'end': 24421.541, 'start': 24028.55, 'title': 'Reinforcement learning basics', 'summary': 'Introduces reinforcement learning, highlighting the concepts of agent, environment, state, action, reward, and policy, and explains the process using examples, emphasizing the trial and error method and the goal of maximizing reward.', 'duration': 392.991, 'highlights': ['Reinforcement learning is about taking an appropriate action to maximize the reward, similar to human learning from trial and error.', 'The reinforcement learning process involves an agent receiving state from the environment, taking actions, and receiving rewards, updating its knowledge until the terminal state is reached.', "The agent's strategy to determine its next action based on the current state is known as policy.", "The environment provides rewards to appraise the agent's last action, such as coins or additional points."]}, {'end': 24863.356, 'start': 24422.021, 'title': 'Reinforcement learning concepts', 'summary': "Introduces concepts such as value, action value, reward maximization, discounting, exploration, exploitation, and markov's decision process in reinforcement learning, emphasizing the importance of maximizing rewards and choosing the optimum policy.", 'duration': 441.335, 'highlights': ["The agent's goal in reinforcement learning is to maximize the reward by taking the best action, as the end goal is to maximize reward based on a set of actions.", "Discounting in reinforcement learning involves the agent's decision to prioritize eating closer rewards over larger ones near the opponent, influenced by the gamma value, where a smaller gamma indicates a larger discount value.", 'Exploration and exploitation play crucial roles in reinforcement learning, where the agent needs to balance between using known information to attain rewards and exploring the environment to find bigger rewards.', "Markov's decision process is a mathematical approach used in reinforcement learning to solve problems by considering parameters such as set of actions, set of states, rewards, policy, and value, aiming to maximize rewards with the optimum policy."]}], 'duration': 898.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF023964635.jpg', 'highlights': ['Generating frequent item sets with support at least 7% is essential for obtaining close enough rules with corresponding support, confidence, and lift.', 'Adding constraints on the rules such as lift > 6 and confidence > 0.8 refines the association rules for more specific insights.', 'Creating association rules using the a priori algorithm helps in market basket analysis for big companies like Walmart, Reliance, Target, and IKEA.', "The agent's goal in reinforcement learning is to maximize the reward by taking the best action, as the end goal is to maximize reward based on a set of actions.", "Markov's decision process is a mathematical approach used in reinforcement learning to solve problems by considering parameters such as set of actions, set of states, rewards, policy, and value, aiming to maximize rewards with the optimum policy.", "Discounting in reinforcement learning involves the agent's decision to prioritize eating closer rewards over larger ones near the opponent, influenced by the gamma value, where a smaller gamma indicates a larger discount value.", 'Exploration and exploitation play crucial roles in reinforcement learning, where the agent needs to balance between using known information to attain rewards and exploring the environment to find bigger rewards.', 'Reinforcement learning is about taking an appropriate action to maximize the reward, similar to human learning from trial and error.']}, {'end': 26027.551, 'segs': [{'end': 25366.333, 'src': 'embed', 'start': 25334.053, 'weight': 3, 'content': [{'end': 25336.735, 'text': "Okay, so don't worry about this formula for now.", 'start': 25334.053, 'duration': 2.682}, {'end': 25340.719, 'text': 'But here just remember that this Q basically represents the Q matrix.', 'start': 25337.136, 'duration': 3.583}, {'end': 25346.965, 'text': "The R represents the reward matrix and the gamma is a gamma value which I'll talk about shortly.", 'start': 25340.759, 'duration': 6.206}, {'end': 25349.948, 'text': "and here you're just finding out the maximum from the Q matrix.", 'start': 25346.965, 'duration': 2.983}, {'end': 25360.108, 'text': 'So basically the gamma parameter has a range from 0 to 1 so you can have a value of 0.1 0.3 0.5 0.8 and all of that.', 'start': 25350.66, 'duration': 9.448}, {'end': 25366.333, 'text': 'So if the gamma is closer to 0, it means that the agent will consider only the immediate rewards,', 'start': 25360.608, 'duration': 5.725}], 'summary': "Q matrix, reward matrix, gamma value, and their impact on agent's consideration of rewards.", 'duration': 32.28, 'max_score': 25334.053, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF025334053.jpg'}, {'end': 25413.142, 'src': 'embed', 'start': 25374.875, 'weight': 2, 'content': [{'end': 25381.925, 'text': "but if the value of gamma is high, meaning that if it's closer to one, the agent will consider future rewards with greater weight.", 'start': 25374.875, 'duration': 7.05}, {'end': 25389.675, 'text': 'This means that the agent will explore all the possible approaches or all the possible policies in order to get to the end goal.', 'start': 25382.405, 'duration': 7.27}, {'end': 25394.056, 'text': 'So guys, this is what I was talking about when I mentioned exploitation and exploration.', 'start': 25390.055, 'duration': 4.001}, {'end': 25402.619, 'text': "Alright. so if the gamma value is closer to one, it basically means that you're actually exploring the entire environment and then choosing an optimum policy.", 'start': 25394.356, 'duration': 8.263}, {'end': 25405.34, 'text': 'But if your gamma value is closer to zero,', 'start': 25402.999, 'duration': 2.341}, {'end': 25413.142, 'text': 'it means that the agent will only stick to a certain set of policies and it will calculate the maximum reward based on those policies.', 'start': 25405.34, 'duration': 7.802}], 'summary': 'A high gamma value leads to more future reward consideration and exploration in the environment, while a low gamma value results in sticking to specific policies for maximum reward calculation.', 'duration': 38.267, 'max_score': 25374.875, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF025374875.jpg'}, {'end': 25623.429, 'src': 'embed', 'start': 25598.611, 'weight': 4, 'content': [{'end': 25604.077, 'text': "and you're going to find out which has a maximum Q value, and that's how you're gonna compute the Q value.", 'start': 25598.611, 'duration': 5.466}, {'end': 25606.158, 'text': "So let's implement a formula.", 'start': 25604.577, 'duration': 1.581}, {'end': 25607.859, 'text': 'Okay, this is the Q learning formula.', 'start': 25606.278, 'duration': 1.581}, {'end': 25611.622, 'text': 'So right now we are traversing from room number one to room number five.', 'start': 25608.34, 'duration': 3.282}, {'end': 25612.963, 'text': 'Okay, this is our state.', 'start': 25611.922, 'duration': 1.041}, {'end': 25615.864, 'text': "So here I've written Q 1 comma 5.", 'start': 25613.243, 'duration': 2.621}, {'end': 25619.106, 'text': 'Okay one represents our current state which is room number one.', 'start': 25615.864, 'duration': 3.242}, {'end': 25623.429, 'text': 'Okay, our initial state was room number one and we are traversing to room number five.', 'start': 25619.407, 'duration': 4.022}], 'summary': 'Implementing q learning formula to traverse from room 1 to room 5.', 'duration': 24.818, 'max_score': 25598.611, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF025598611.jpg'}, {'end': 25782.251, 'src': 'embed', 'start': 25751.833, 'weight': 0, 'content': [{'end': 25753.374, 'text': "Now let's look at another example.", 'start': 25751.833, 'duration': 1.541}, {'end': 25756.878, 'text': "okay?. This time we'll start with a randomly chosen initial state.", 'start': 25753.374, 'duration': 3.504}, {'end': 25759.121, 'text': "Let's say that we've chosen state three.", 'start': 25757.219, 'duration': 1.902}, {'end': 25763.634, 'text': 'Okay, so from room 3 you can either go to room number 1, 2 or 4.', 'start': 25759.671, 'duration': 3.963}, {'end': 25771.081, 'text': "randomly, will select room number 1, and from room number 1 you're going to calculate the maximum Q value for the next date,", 'start': 25763.634, 'duration': 7.447}, {'end': 25772.602, 'text': 'based on all possible actions.', 'start': 25771.081, 'duration': 1.521}, {'end': 25776.75, 'text': 'So the possible actions from one is to go to three and to go to five.', 'start': 25773.069, 'duration': 3.681}, {'end': 25782.251, 'text': 'Now if you calculate the Q value using this formula, so let me explain this to you once again.', 'start': 25777.29, 'duration': 4.961}], 'summary': 'Analyzing possible actions from room 1 to calculate maximum q value for the next state.', 'duration': 30.418, 'max_score': 25751.833, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF025751833.jpg'}, {'end': 25977.965, 'src': 'embed', 'start': 25951.405, 'weight': 7, 'content': [{'end': 25955.388, 'text': "So he's going to try to go from different rooms and finally land up in room number five.", 'start': 25951.405, 'duration': 3.983}, {'end': 25958.33, 'text': 'So guys, this is exactly how our code runs.', 'start': 25955.908, 'duration': 2.422}, {'end': 25963.094, 'text': "We're going to traverse through each and every node because we want an optimum policy.", 'start': 25958.63, 'duration': 4.464}, {'end': 25968.578, 'text': 'Okay, an optimum policy is attained only when you traverse through all possible actions.', 'start': 25963.514, 'duration': 5.064}, {'end': 25972.481, 'text': 'Okay, so if you go through all possible actions that you can perform,', 'start': 25968.978, 'duration': 3.503}, {'end': 25976.464, 'text': 'only then will you understand which is the best action which will lead us to the reward.', 'start': 25972.481, 'duration': 3.983}, {'end': 25977.965, 'text': 'I hope this is clear.', 'start': 25977.064, 'duration': 0.901}], 'summary': 'Traversing all nodes for optimum policy to attain best action leading to reward.', 'duration': 26.56, 'max_score': 25951.405, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF025951405.jpg'}, {'end': 26021.227, 'src': 'embed', 'start': 25992.738, 'weight': 1, 'content': [{'end': 25996.101, 'text': 'You can check out that video on Python and then maybe come back to this later.', 'start': 25992.738, 'duration': 3.363}, {'end': 25999.024, 'text': "Okay, but I'll be explaining the code to you anyway,", 'start': 25996.482, 'duration': 2.542}, {'end': 26004.849, 'text': "but I'm not going to spend a lot of time explaining each and every line of code because I'm assuming that you know Python.", 'start': 25999.024, 'duration': 5.825}, {'end': 26008.038, 'text': "Okay, so let's look at the first line of code over here.", 'start': 26005.316, 'duration': 2.722}, {'end': 26010.88, 'text': "So what we're gonna do is we're gonna import numpy.", 'start': 26008.458, 'duration': 2.422}, {'end': 26021.227, 'text': "Okay, numpy is basically a Python library for adding support for large multi-dimensional arrays and matrices and it's basically for computing mathematical functions.", 'start': 26010.98, 'duration': 10.247}], 'summary': 'Introduction to python numpy library for handling arrays and matrices', 'duration': 28.489, 'max_score': 25992.738, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF025992738.jpg'}], 'start': 24863.657, 'title': 'Q learning algorithm', 'summary': 'Covers optimizing policies with q learning algorithm using hundreds of nodes and 50-60 different policies, understanding q-learning in room navigation with rewards of 100 and 0, q matrix and reward matrix creation, and the significance of the gamma value, and the training process involving exploring the environment and calculating the q matrix for finding the optimum policy for maximum reward.', 'chapters': [{'end': 24902.495, 'start': 24863.657, 'title': 'Optimizing policies with q learning algorithm', 'summary': 'Explores the process of exploring nodes and policies to determine an optimal policy using the q learning algorithm, with hundreds of nodes and 50-60 different policies involved in the process.', 'duration': 38.838, 'highlights': ['Exploring nodes and policies to determine an optimal policy using the Q learning algorithm, involving hundreds of nodes and 50-60 different policies.', 'Utilizing the Q learning algorithm, a type of reinforcement learning algorithm, for the demo.', 'The importance of exploring different nodes to find a more optimal policy and maximizing rewards.']}, {'end': 25175.986, 'start': 24902.655, 'title': 'Understanding q-learning in room navigation', 'summary': 'Explains the concept of q-learning in the context of room navigation, where an agent is placed in one of the rooms and aims to reach a specific room, with direct connections to the target room receiving a reward of 100 and indirect connections receiving a reward of 0, and the end goal being for the agent to reach the state with the highest reward.', 'duration': 273.331, 'highlights': ['The end goal is to reach the state with the highest reward, with direct connections to the target room receiving a reward of 100 and indirect connections receiving a reward of 0.', 'Directly connected rooms to the target room receive a reward of 100, while other rooms receive a reward of 0.', "The agent's movement from one room to another represents the action, with the goal being to reach room number 5."]}, {'end': 25413.142, 'start': 25176.727, 'title': 'Q matrix and reward matrix in rl', 'summary': "Explains the creation of reward and q matrices, assigning rewards based on direct connections to the goal, and the significance of gamma value in influencing agent's decision-making process in reinforcement learning.", 'duration': 236.415, 'highlights': ['The reward matrix is created to assign rewards based on direct connections to the goal, with a reward of 100 for nodes directly connected to the goal, and 0 for nodes not directly connected to the goal.', 'The process of creating the Q matrix is explained, detailing its role as the memory of what the agent has learned through experience and its use in decision-making based on past experiences.', "The significance of the gamma parameter in influencing the agent's exploration and exploitation strategies is discussed, with a higher gamma value leading to greater consideration of future rewards and exploration of possible policies."]}, {'end': 26027.551, 'start': 25413.722, 'title': 'Understanding q learning algorithm', 'summary': "Explains the q learning algorithm, where the agent learns from experience, and the training process involves exploring the environment to enhance the agent's decision-making capabilities. each episode is equivalent to one training session, and the q matrix is calculated to find the optimum policy for maximum reward.", 'duration': 613.829, 'highlights': ['The Q learning algorithm involves the agent learning from experience, with each episode equivalent to one training session.', "The training process aims to enhance the agent's decision-making capabilities by calculating the Q matrix to find the optimum policy for maximum reward.", 'The reward matrix is utilized to calculate the Q value for traversing from one state to another, with the goal of maximizing the reward.']}], 'duration': 1163.894, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF024863657.jpg', 'highlights': ['Utilizing Q learning algorithm for demo with hundreds of nodes and 50-60 policies', "Creating Q matrix as memory of agent's learning through experience", "Significance of gamma parameter in influencing agent's exploration strategies", "Training process aims to enhance agent's decision-making capabilities", 'Directly connected rooms to target receive reward of 100, others receive 0', 'Reward matrix assigns 100 for nodes directly connected to goal, 0 for others', 'Q learning involves agent learning from experience in each training session', "Agent's goal is to reach state with highest reward, room 5"]}, {'end': 27958.259, 'segs': [{'end': 26137.946, 'src': 'embed', 'start': 26110.344, 'weight': 1, 'content': [{'end': 26114.527, 'text': 'And this is stored in this variable called available act.', 'start': 26110.344, 'duration': 4.183}, {'end': 26117.91, 'text': 'Now this will basically get the available actions in the current state.', 'start': 26114.848, 'duration': 3.062}, {'end': 26122.593, 'text': "So we're just storing the possible actions in this available act variable over here.", 'start': 26118.03, 'duration': 4.563}, {'end': 26128.878, 'text': "So basically over here since our initial state is one, we're going to find out the next possible states we can go to.", 'start': 26123.073, 'duration': 5.805}, {'end': 26132.301, 'text': 'Okay, that is stored in the available act variable.', 'start': 26129.258, 'duration': 3.043}, {'end': 26137.946, 'text': 'Now next is this function chooses at random which action to be performed within the range.', 'start': 26132.862, 'duration': 5.084}], 'summary': "The available actions in the current state are stored in the 'available act' variable, and a function chooses an action randomly within a range.", 'duration': 27.602, 'max_score': 26110.344, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF026110344.jpg'}, {'end': 26315, 'src': 'embed', 'start': 26280.249, 'weight': 4, 'content': [{'end': 26284.852, 'text': "Then I'm calculating the next action and then I'm finally updating the value in the Q matrix.", 'start': 26280.249, 'duration': 4.603}, {'end': 26287.834, 'text': 'And next, we just normalize the Q matrix.', 'start': 26285.612, 'duration': 2.222}, {'end': 26291.516, 'text': 'So sometimes in our Q matrix, the value might exceed.', 'start': 26288.074, 'duration': 3.442}, {'end': 26294.533, 'text': "Okay, let's say it exceeded to 500, 600.", 'start': 26291.536, 'duration': 2.997}, {'end': 26296.295, 'text': 'So that time you want to normalize the matrix.', 'start': 26294.534, 'duration': 1.761}, {'end': 26303.079, 'text': "We want to bring it down a little bit because larger numbers we won't be able to understand and computation will be very hard on larger numbers.", 'start': 26296.535, 'duration': 6.544}, {'end': 26305.06, 'text': "That's why we perform normalization.", 'start': 26303.379, 'duration': 1.681}, {'end': 26311.423, 'text': "You're taking your calculated value and you're dividing it with the maximum Q value into 100.", 'start': 26305.44, 'duration': 5.983}, {'end': 26312.804, 'text': "So you're normalizing it over here.", 'start': 26311.423, 'duration': 1.381}, {'end': 26315, 'text': 'So guys, this is the testing phase.', 'start': 26313.479, 'duration': 1.521}], 'summary': 'Q matrix values are normalized to improve understanding and computation efficiency during testing phase.', 'duration': 34.751, 'max_score': 26280.249, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF026280249.jpg'}, {'end': 26625.102, 'src': 'embed', 'start': 26600.156, 'weight': 9, 'content': [{'end': 26606.601, 'text': 'Now as you might have already guessed, the set of actions here is nothing but the set of all possible states of the robot.', 'start': 26600.156, 'duration': 6.445}, {'end': 26611.41, 'text': 'For each location the set of actions that a robot can take will be different.', 'start': 26607.287, 'duration': 4.123}, {'end': 26616.555, 'text': 'For example, the set of actions will change if the robot is in L1 rather than L2.', 'start': 26611.851, 'duration': 4.704}, {'end': 26621.639, 'text': 'So if the robot is in L1, it can only go to L4 and L2 directly.', 'start': 26617.035, 'duration': 4.604}, {'end': 26625.102, 'text': 'Now that we are done with the states and the actions.', 'start': 26622.419, 'duration': 2.683}], 'summary': 'The set of actions for a robot depends on its location, e.g. in l1, it can go to l4 and l2 directly.', 'duration': 24.946, 'max_score': 26600.156, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF026600156.jpg'}, {'end': 27396.052, 'src': 'embed', 'start': 27366.081, 'weight': 0, 'content': [{'end': 27372.246, 'text': 'Now the robot now has four different states to choose from, and along with that there are four different actions.', 'start': 27366.081, 'duration': 6.165}, {'end': 27374.107, 'text': 'also, for the current state it is in.', 'start': 27372.246, 'duration': 1.861}, {'end': 27382.534, 'text': 'So how do we calculate Q that is the cumulative quality of the possible actions the robot might take.', 'start': 27374.748, 'duration': 7.786}, {'end': 27384.268, 'text': "So let's break it down.", 'start': 27383.248, 'duration': 1.02}, {'end': 27396.052, 'text': 'Now from the equation V of S equals maximum of A R S comma A plus comma summation S dash P S A S dash into V of S dash.', 'start': 27384.768, 'duration': 11.284}], 'summary': 'The robot has 4 states and 4 actions, calculating q for possible actions using an equation.', 'duration': 29.971, 'max_score': 27366.081, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF027366081.jpg'}, {'end': 27522.215, 'src': 'embed', 'start': 27496.453, 'weight': 8, 'content': [{'end': 27504.035, 'text': 'Now the qualities of the actions are called the Q values and from now on we will refer to the value footprints as the Q values.', 'start': 27496.453, 'duration': 7.582}, {'end': 27507.768, 'text': 'An important piece of the puzzle is the temporal difference.', 'start': 27504.947, 'duration': 2.821}, {'end': 27516.172, 'text': 'Now temporal difference is the component that will help the robot calculate the Q values with respect to the changes in the environment over time.', 'start': 27508.369, 'duration': 7.803}, {'end': 27522.215, 'text': 'So consider our robot is currently in the mark state and it wants to move to the upper state.', 'start': 27516.772, 'duration': 5.443}], 'summary': 'Q values represent action qualities, and temporal difference helps calculate q values based on environment changes over time.', 'duration': 25.762, 'max_score': 27496.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF027496453.jpg'}, {'end': 27622.416, 'src': 'embed', 'start': 27592.856, 'weight': 6, 'content': [{'end': 27602.479, 'text': 'So if you replace the TD s comma a with its full form equation, we should get QT of s comma a is equal to QT minus 1 of s comma a,', 'start': 27592.856, 'duration': 9.623}, {'end': 27609.061, 'text': 'plus alpha into R of s comma a, plus gamma maximum of Q s dash a.', 'start': 27602.479, 'duration': 6.582}, {'end': 27615.983, 'text': 'dash minus QT minus 1 s comma a, now that we have all the little pieces of Q line together.', 'start': 27609.061, 'duration': 6.922}, {'end': 27618.984, 'text': "Let's move forward to its implementation part.", 'start': 27616.543, 'duration': 2.441}, {'end': 27622.416, 'text': 'Now this is the final equation of Q learning, right?', 'start': 27619.634, 'duration': 2.782}], 'summary': "Q-learning equation: qt(s,a)=qt-1(s,a)+Ξ±r(s,a)+Ξ³maxq(s',a)", 'duration': 29.56, 'max_score': 27592.856, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF027592856.jpg'}], 'start': 26028.007, 'title': 'Reinforcement learning and q matrix creation', 'summary': 'Covers the creation of a q matrix for reinforcement learning with a gamma parameter of 0.8, training phase with 10,000 iterations, application in robot navigation in an automobile factory warehouse, key concepts including bellman equation and markov decision process, and implementing q learning algorithm for warehouse navigation with gamma as 0.75 and alpha as 0.9.', 'chapters': [{'end': 26315, 'start': 26028.007, 'title': 'Reinforcement learning q matrix creation', 'summary': 'Explains the creation of a q matrix for reinforcement learning, initializing its values to zero, setting the gamma parameter to 0.8, and the process of choosing available actions, updating the q matrix, and the training phase with 10,000 iterations to find the best policy.', 'duration': 286.993, 'highlights': ['The training phase involves 10,000 iterations, allowing the agent to go through 10,000 scenarios to find the best policy.', 'The Q matrix is normalized by dividing the calculated value with the maximum Q value into 100 to make computation easier.', 'The Q matrix is updated by calculating the maximum index for the possible actions to find the maximum Q value and choosing a new initial state.', 'The Q matrix is computed using the formula: current state, action, gamma into the maximum value, to update the Q value.', 'The Q matrix is initialized to zero and the gamma parameter is set to 0.8 for the reinforcement learning process.', 'The process involves choosing available actions from the current state, selecting a random action from the available actions, and updating the Q matrix with the calculated Q value.']}, {'end': 26714.913, 'start': 26315.321, 'title': 'Reinforcement learning in robot navigation', 'summary': 'Demonstrates the application of reinforcement learning in training autonomous robots to navigate through an automobile factory warehouse to convey parts by presenting the agent, environment, actions, rewards, and states, and constructing a reward table with prioritized locations, ultimately achieving the shortest route from any given location to another.', 'duration': 399.592, 'highlights': ['The task involves enabling robots to find the shortest route from any given location to another within an automobile factory warehouse, which consists of nine different positions for car parts, prioritizing the location containing the chassis, and creating a reward table with a very high reward for the topmost priority location, L6.', 'The chapter explains the components of a reinforcement learning solution, including the agent, environment, actions, rewards, and states, and maps the location codes to numbers to represent states, and defines the set of actions as the direct locations that a robot can move from a particular location.', 'The chapter demonstrates the process of setting the initial stage, training the agent to navigate through the environment, calculating the Q matrix for different initial stages, and selecting the path with the maximum reward, exemplifying the application of reinforcement learning in robot navigation.']}, {'end': 27122.669, 'start': 26715.734, 'title': 'Key concepts in reinforcement learning', 'summary': 'Introduces the bellman equation in reinforcement learning, explaining its role in enabling robotic memory and how it helps the robot make decisions based on value footprints, while also delving into the markov decision process to handle stochasticity in decision-making.', 'duration': 406.935, 'highlights': ['The Bellman equation in reinforcement learning enables the robot to make decisions based on value footprints, assisting it in reaching its destination in an environment without barriers.', 'The discount factor in the Bellman equation informs the robot about its distance from the destination, with the developer specifying its value.', 'The Markov decision process provides a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of the decision maker.']}, {'end': 27622.416, 'start': 27123.51, 'title': 'Q learning and temporal difference', 'summary': 'Explains the introduction of stochasticity in the bellman equation to incorporate probabilities of robot movement, the concept of living penalty to reward robot actions, and the implementation of q learning with temporal difference to calculate q values for robot actions in a stochastic environment.', 'duration': 498.906, 'highlights': ['Introduction of stochasticity in the Bellman equation', 'Concept of living penalty to reward robot actions', 'Implementation of Q learning with temporal difference']}, {'end': 27958.259, 'start': 27622.917, 'title': 'Implementing q learning algorithm for warehouse navigation', 'summary': 'Introduces the q learning algorithm for warehouse navigation, utilizing numpy and python3, with defined states, actions, reward table and q values to find the optimal route from starting location l9 to end location l1, using gamma as 0.75 and alpha as 0.9.', 'duration': 335.342, 'highlights': ['Defined states, actions, and parameters for Q learning algorithm', 'Initialized Q values to zero and iteratively updated them using the Bellman equation', 'Mapped warehouse locations to numbers and created a reward table for navigation']}], 'duration': 1930.252, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF026028007.jpg', 'highlights': ['Trained with 10,000 iterations to find best policy', 'Q matrix normalized by dividing with max Q value into 100', 'Q matrix updated by calculating max index for possible actions', 'Q matrix computed using formula: current state, action, gamma', 'Robot navigation in automobile factory warehouse', 'Reinforcement learning process with gamma parameter of 0.8', 'Robot task: find shortest route within warehouse', 'Reinforcement learning components: agent, environment, actions, rewards, states', 'Bellman equation enables robot to make decisions based on value footprints', 'Markov decision process models decision-making in partly random situations', 'Introduction of stochasticity in Bellman equation', 'Implementation of Q learning with temporal difference', 'Defined states, actions, and parameters for Q learning algorithm', 'Initialized Q values to zero and iteratively updated using Bellman equation', 'Mapped warehouse locations to numbers and created reward table']}, {'end': 28963.94, 'segs': [{'end': 28610.537, 'src': 'embed', 'start': 28579.917, 'weight': 1, 'content': [{'end': 28582.698, 'text': "Now there's an algorithm on which it works, so let me explain you that.", 'start': 28579.917, 'duration': 2.781}, {'end': 28586.763, 'text': 'So the first thing we do is we initialize the weights and the threshold.', 'start': 28583.541, 'duration': 3.222}, {'end': 28591.366, 'text': 'now these weights can actually be a small number or a random number, and it can even be zero.', 'start': 28586.763, 'duration': 4.603}, {'end': 28594.608, 'text': 'So it depends on the use case that we have.', 'start': 28591.966, 'duration': 2.642}, {'end': 28595.188, 'text': 'then what we do.', 'start': 28594.608, 'duration': 0.58}, {'end': 28601.091, 'text': 'we provide the input and then we calculate the output now, when we are training our model, or we are training our artificial neuron.', 'start': 28595.188, 'duration': 5.903}, {'end': 28604.393, 'text': 'We have the output values for a particular set of inputs.', 'start': 28601.452, 'duration': 2.941}, {'end': 28610.537, 'text': 'So we know the output value, but what we do we give the input and we see what will be the output of our particular neuron.', 'start': 28604.673, 'duration': 5.864}], 'summary': 'Explaining algorithm for initializing weights and threshold, and calculating output for training artificial neuron.', 'duration': 30.62, 'max_score': 28579.917, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF028579917.jpg'}, {'end': 28925.657, 'src': 'embed', 'start': 28884.808, 'weight': 3, 'content': [{'end': 28888.471, 'text': '5, so we get an output that is one, and this is how we get the graph.', 'start': 28884.808, 'duration': 3.663}, {'end': 28896.214, 'text': 'So this is how we get this graph and now if you notice with the help of the single layer perceptron, we are able to classify the ones and zeros.', 'start': 28889.173, 'duration': 7.041}, {'end': 28902.156, 'text': 'So this line, anything above this line is actually one and anything below this line, we have zeros.', 'start': 28896.695, 'duration': 5.461}, {'end': 28905.856, 'text': 'So this is how we are able to classify or able to implement OR gate.', 'start': 28902.816, 'duration': 3.04}, {'end': 28910.797, 'text': "Similarly, when I talk about AND gate as well, so there's a difference in the truth table of AND gate.", 'start': 28906.416, 'duration': 4.381}, {'end': 28915.858, 'text': 'In AND gate, what happens, we need to make sure that both of our inputs are high in order to get a high output.', 'start': 28911.117, 'duration': 4.741}, {'end': 28918.779, 'text': 'If any of the input is low, we get a low output.', 'start': 28916.559, 'duration': 2.22}, {'end': 28925.657, 'text': "And that's the reason we choose an activation function that is 1.5,, which means that if our value or the output is above 1.5,", 'start': 28919.633, 'duration': 6.024}], 'summary': 'Single layer perceptron able to classify ones and zeros for or and and gates', 'duration': 40.849, 'max_score': 28884.808, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF028884808.jpg'}, {'end': 28978.163, 'src': 'embed', 'start': 28948.015, 'weight': 0, 'content': [{'end': 28950.936, 'text': 'Yeah, so one plus one two, which is obviously greater than 0.5.', 'start': 28948.015, 'duration': 2.921}, {'end': 28957.598, 'text': 'So what we get we are neuron fires and we get one here, but for the rest of the inputs all will be less than 1.5.', 'start': 28950.936, 'duration': 6.662}, {'end': 28960.919, 'text': "So that is why our neuron doesn't fire and we get a zero output.", 'start': 28957.598, 'duration': 3.321}, {'end': 28962.16, 'text': "Don't worry guys.", 'start': 28961.62, 'duration': 0.54}, {'end': 28963.94, 'text': "I'll actually tell you how to implement it.", 'start': 28962.22, 'duration': 1.72}, {'end': 28968.242, 'text': "I'll open my pie charm and I'll be showing it to you practically how to implement these gates.", 'start': 28963.98, 'duration': 4.262}, {'end': 28972.721, 'text': "So for that what I'm going to do is I'm going to first import a Python library called TensorFlow.", 'start': 28968.84, 'duration': 3.881}, {'end': 28978.163, 'text': 'The installation guide of this particular library is present in an LMS for both Windows as well as for Linux.', 'start': 28973.221, 'duration': 4.942}], 'summary': 'Explaining neuron firing and implementation using tensorflow in python.', 'duration': 30.148, 'max_score': 28948.015, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF028948015.jpg'}], 'start': 27958.999, 'title': 'Key concepts in machine learning', 'summary': 'Covers q learning, bellman equation, deep learning with tensorflow, artificial neurons, and the perceptron learning algorithm, aiming to enhance understanding of machine learning concepts and applications, including maximizing robot rewards, image identification, and classification tasks.', 'chapters': [{'end': 28012.954, 'start': 27958.999, 'title': 'Q learning and bellman equation', 'summary': 'Discusses q learning, bellman equation, and the process of creating and updating the q table with an analogy, resulting in maximizing the reward for the robot.', 'duration': 53.955, 'highlights': ['The process of creating and updating the Q table with the analogy yields the maximum reward for the robot.', 'The session covers understanding Q learning, the Bellman equation, creating the reward table, the Q table, and updating Q values using the Bellman equation.', 'The analogy used in the session provides a good understanding of Q learning and the Bellman equation.']}, {'end': 28359.803, 'start': 28018.437, 'title': 'Deep learning with tensorflow', 'summary': "Introduces the structure and content of the deep learning tutorial with tensorflow, explaining the evolution from single layer perceptrons to multi-layer perceptrons and the motivation behind deep learning, while also delving into the process of identifying images using machine learning and the transition to deep learning, highlighting the key role of mimicking the human brain's functionality.", 'duration': 341.366, 'highlights': ['The chapter covers the evolution from single layer perceptrons to multi-layer perceptrons, explaining the limitations of single layer perceptrons and how multi-layer perceptrons overcame those limitations, with various examples provided.', 'The course structure is outlined, including topics such as the introduction to deep learning, fundamentals of neural networks, deep networks, TensorFlow, convolutional neural networks, recurrent neural networks, RBM, and autoencoders, providing a comprehensive overview of the content to be covered.', 'The chapter explains the transition from manually extracting features in machine learning models to the automatic feature extraction in deep learning, highlighting the advantages of deep learning in identifying objects and classifying images.', "The motivation behind deep learning is discussed, focusing on the desire to mimic the human brain's functionality and the use of neurons to understand the working principle, highlighting the aspiration to replicate human thinking, decision-making, and problem-solving."]}, {'end': 28963.94, 'start': 28360.123, 'title': 'Artificial neurons and perceptron', 'summary': 'Discusses the need for artificial neurons by highlighting the limitations of the human brain and the benefits of using artificial neurons, explaining the perceptron learning algorithm and its applications, including classifying flowers based on features and implementing logic gates.', 'duration': 603.817, 'highlights': ['Artificial neurons address the limitations of the human brain by avoiding fatigue, handling large volumes of inputs, and minimizing human error.', 'The perceptron learning algorithm involves initializing weights and thresholds, providing inputs, calculating outputs, updating weights based on the error, and repeated iterations to minimize differences between desired and actual outputs.', 'The perceptron can classify flowers based on features such as sepal length, sepal width, petal length, and petal width, and is capable of separating two species of flowers based on input variables.', 'The perceptron can be used to implement logic gates like OR and AND, enabling the classification of inputs into distinct categories based on specific activation functions and thresholds.']}], 'duration': 1004.941, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF027958999.jpg', 'highlights': ['The chapter covers the evolution from single layer perceptrons to multi-layer perceptrons, explaining the limitations of single layer perceptrons and how multi-layer perceptrons overcame those limitations, with various examples provided.', 'The course structure is outlined, including topics such as the introduction to deep learning, fundamentals of neural networks, deep networks, TensorFlow, convolutional neural networks, recurrent neural networks, RBM, and autoencoders, providing a comprehensive overview of the content to be covered.', 'The process of creating and updating the Q table with the analogy yields the maximum reward for the robot.', 'The chapter explains the transition from manually extracting features in machine learning models to the automatic feature extraction in deep learning, highlighting the advantages of deep learning in identifying objects and classifying images.', 'The analogy used in the session provides a good understanding of Q learning and the Bellman equation.', "The motivation behind deep learning is discussed, focusing on the desire to mimic the human brain's functionality and the use of neurons to understand the working principle, highlighting the aspiration to replicate human thinking, decision-making, and problem-solving.", 'Artificial neurons address the limitations of the human brain by avoiding fatigue, handling large volumes of inputs, and minimizing human error.', 'The perceptron learning algorithm involves initializing weights and thresholds, providing inputs, calculating outputs, updating weights based on the error, and repeated iterations to minimize differences between desired and actual outputs.', 'The perceptron can classify flowers based on features such as sepal length, sepal width, petal length, and petal width, and is capable of separating two species of flowers based on input variables.', 'The perceptron can be used to implement logic gates like OR and AND, enabling the classification of inputs into distinct categories based on specific activation functions and thresholds.']}, {'end': 30476.738, 'segs': [{'end': 29026.761, 'src': 'embed', 'start': 29002.714, 'weight': 1, 'content': [{'end': 29012.879, 'text': 'it basically contains handwritten digits from zero to nine, and in that data set we have 50, 000 training images, along with 10, 000 testing images.', 'start': 29002.714, 'duration': 10.165}, {'end': 29020.739, 'text': 'So we will train our model with those 55, 000 training images and then we are going to test the accuracy of a model with the help of those 10,', 'start': 29014.158, 'duration': 6.581}, {'end': 29021.96, 'text': '000 testing images.', 'start': 29020.739, 'duration': 1.221}, {'end': 29026.761, 'text': 'And for all of this, we need to understand first what exactly is TensorFlow.', 'start': 29022.42, 'duration': 4.341}], 'summary': 'Handwritten digits dataset with 50,000 training images and 10,000 testing images for tensorflow model training and accuracy testing.', 'duration': 24.047, 'max_score': 29002.714, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF029002714.jpg'}, {'end': 29132.458, 'src': 'embed', 'start': 29101.705, 'weight': 4, 'content': [{'end': 29105.347, 'text': 'Now let us move forward and understand TensorFlow in a bit detail.', 'start': 29101.705, 'duration': 3.642}, {'end': 29109.409, 'text': 'So as the name tells that it consists of two words, tensor as well as flow.', 'start': 29105.887, 'duration': 3.522}, {'end': 29111.65, 'text': 'Now we understood what exactly tensor is.', 'start': 29109.969, 'duration': 1.681}, {'end': 29113.171, 'text': 'We saw it in the previous slide as well.', 'start': 29111.77, 'duration': 1.401}, {'end': 29117.093, 'text': 'Now when I talk about flow, it is nothing but a data flow graph.', 'start': 29113.631, 'duration': 3.462}, {'end': 29120.79, 'text': 'So let me just give you an example that is there in front of your screen.', 'start': 29117.828, 'duration': 2.962}, {'end': 29123.032, 'text': 'So we talked about weights and inputs.', 'start': 29121.19, 'duration': 1.842}, {'end': 29127.575, 'text': 'So we provide these weights and inputs and we perform a matrix multiplication.', 'start': 29123.452, 'duration': 4.123}, {'end': 29132.458, 'text': 'So beta is one tensor, X input is one tensor, then we perform matrix multiplication.', 'start': 29128.075, 'duration': 4.383}], 'summary': 'Introduction to tensorflow, explaining tensor and data flow graph with an example of matrix multiplication.', 'duration': 30.753, 'max_score': 29101.705, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF029101705.jpg'}, {'end': 29188.349, 'src': 'embed', 'start': 29158.693, 'weight': 0, 'content': [{'end': 29161.494, 'text': 'Now the TensorFlow programs actually consists of two parts.', 'start': 29158.693, 'duration': 2.801}, {'end': 29165.996, 'text': 'One is building a computational graph and another is running a computational graph.', 'start': 29162.034, 'duration': 3.962}, {'end': 29169.178, 'text': "So we'll first understand how to build a computational graph.", 'start': 29166.377, 'duration': 2.801}, {'end': 29181.506, 'text': 'Now you can think of a computational graph as a network of nodes and with each node known as an operation and running some function that can be as simple as addition or subtraction,', 'start': 29170.242, 'duration': 11.264}, {'end': 29185.088, 'text': 'or it can be as complex as, say, some multivariate equation.', 'start': 29181.506, 'duration': 3.582}, {'end': 29188.349, 'text': 'Now, let me explain it to you with the code that is there in front of your screen.', 'start': 29185.708, 'duration': 2.641}], 'summary': 'Tensorflow programs consist of building and running a computational graph, explained as a network of nodes performing operations.', 'duration': 29.656, 'max_score': 29158.693, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF029158693.jpg'}, {'end': 29507.144, 'src': 'embed', 'start': 29476.891, 'weight': 3, 'content': [{'end': 29480.311, 'text': 'First we add C and B, then we add B and A.', 'start': 29476.891, 'duration': 3.42}, {'end': 29482.052, 'text': 'Then we get two other nodes, that is E and D.', 'start': 29480.311, 'duration': 1.741}, {'end': 29487.835, 'text': 'And then from that E and D what we are going to do is we are going to subtract both of them and then we get the final output.', 'start': 29482.592, 'duration': 5.243}, {'end': 29491.777, 'text': 'Now let me go ahead and execute this practically in my PyCharm.', 'start': 29488.915, 'duration': 2.862}, {'end': 29495.579, 'text': 'So this is my PyCharm again guys.', 'start': 29494.258, 'duration': 1.321}, {'end': 29499.38, 'text': 'First thing I need to do is import tensorflow as tf.', 'start': 29495.619, 'duration': 3.761}, {'end': 29502.362, 'text': 'Then we are going to find the three constant nodes.', 'start': 29500.581, 'duration': 1.781}, {'end': 29507.144, 'text': 'So first is aa equals to tf.constant.', 'start': 29502.382, 'duration': 4.762}], 'summary': 'The transcript involves adding, subtracting nodes in tensorflow using pycharm.', 'duration': 30.253, 'max_score': 29476.891, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF029476891.jpg'}, {'end': 29596.56, 'src': 'embed', 'start': 29559.742, 'weight': 6, 'content': [{'end': 29563.003, 'text': "And then we're going to define one more node, let it be F.", 'start': 29559.742, 'duration': 3.261}, {'end': 29565.823, 'text': "And inside that node, we're gonna perform the subtraction operation.", 'start': 29563.003, 'duration': 2.82}, {'end': 29575.402, 'text': 'tf.subtract.add. So we have built our computational graph.', 'start': 29566.623, 'duration': 8.779}, {'end': 29578.365, 'text': 'now we need to run it and you know the process of doing that.', 'start': 29575.402, 'duration': 2.963}, {'end': 29581.427, 'text': 'ses is equals to tf.session.', 'start': 29578.365, 'duration': 3.062}, {'end': 29596.56, 'text': 'Then we are gonna define a variable, let it be outs, whatever name that you wanna give in, and just type in a ses.runf.', 'start': 29585.391, 'duration': 11.169}], 'summary': 'Defining computational graph nodes and running session with tf.session', 'duration': 36.818, 'max_score': 29559.742, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF029559742.jpg'}, {'end': 29683.031, 'src': 'embed', 'start': 29661.063, 'weight': 5, 'content': [{'end': 29669.631, 'text': 'these three lines are bit like a function or a lambda in which we define two input parameters, A and B, and then an operation possible in them.', 'start': 29661.063, 'duration': 8.568}, {'end': 29671.533, 'text': 'So we are actually performing addition.', 'start': 29669.951, 'duration': 1.582}, {'end': 29679.709, 'text': 'So we can evaluate this graph with multiple inputs by using feed underscore dict parameter as you can see we are doing it here.', 'start': 29672.304, 'duration': 7.405}, {'end': 29683.031, 'text': 'So we are actually passing all these values to our placeholders here.', 'start': 29679.769, 'duration': 3.262}], 'summary': 'Defining a function with 2 inputs a and b, performing addition, and evaluating the graph with multiple inputs using feed_dict parameter.', 'duration': 21.968, 'max_score': 29661.063, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF029661063.jpg'}], 'start': 28963.98, 'title': 'Tensorflow basics and model building', 'summary': 'Covers practical implementation of gates using tensorflow, understanding the mnist dataset with 55,000 training images and 10,000 testing images, basics of tensorflow involving tensors, data flow graphs, computational graphs, placeholders, variables, building and evaluating a linear model, calculating loss, and optimizing it using gradient descent.', 'chapters': [{'end': 29608.32, 'start': 28963.98, 'title': 'Tensorflow basics and computational graphs', 'summary': 'Covers the practical implementation of gates using tensorflow, understanding the mnist dataset with 55,000 training images and 10,000 testing images, and the basics of tensorflow involving tensors, data flow graphs, and computational graphs.', 'duration': 644.34, 'highlights': ['The MNIST dataset contains 50,000 training images and 10,000 testing images, serving as a clean and suitable example for training and testing models.', 'TensorFlow represents data in deep learning models as tensors, which are multi-dimensional arrays or an extension of two-dimensional matrices to data with high dimension.', 'TensorFlow library at its core performs matrix manipulation, and it consists of a data flow graph, which involves weights, inputs, matrix multiplication, bias addition, and activation functions like RELU.', 'TensorFlow programs consist of building a computational graph, which is a network of nodes representing operations, and running the computational graph within a session to evaluate the nodes and obtain the output values.']}, {'end': 29997.263, 'start': 29609.68, 'title': 'Tensorflow basics: placeholders and variables', 'summary': 'Introduces the concepts of placeholders and variables in tensorflow, demonstrating how to build computational graphs and run sessions with practical examples, and highlights the importance of variables in making the model trainable and modifying the graph to get new outputs.', 'duration': 387.583, 'highlights': ['The chapter introduces the concepts of placeholders and variables in TensorFlow, demonstrating how to build computational graphs and run sessions with practical examples.', 'Variables are crucial in making the model trainable and modifying the graph to get new outputs with the same input.', 'Demonstration of defining variables, placeholders, and performing operations for a linear model in TensorFlow with practical examples.']}, {'end': 30476.738, 'start': 29998.874, 'title': 'Tensorflow model building and optimization', 'summary': 'Discusses building and evaluating a linear model in tensorflow, calculating loss, and optimizing it using gradient descent with key parameters. it also covers implementing and gate with training data and output values.', 'duration': 477.864, 'highlights': ['The model evaluation resulted in a loss of 23.66, indicating poor performance.', 'The final model parameters for the linear model were W=0.999969 and b=0.9999082.', 'Implementation of the AND gate involved defining training data and output values based on the truth table for the AND gate.']}], 'duration': 1512.758, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF028963980.jpg', 'highlights': ['The MNIST dataset contains 55,000 training images and 10,000 testing images', 'TensorFlow represents data in deep learning models as tensors', 'TensorFlow library at its core performs matrix manipulation', 'TensorFlow programs consist of building a computational graph', 'Introduction of placeholders and variables in TensorFlow', 'Demonstration of defining variables, placeholders, and performing operations for a linear model', 'The model evaluation resulted in a loss of 23.66', 'The final model parameters for the linear model were W=0.999969 and b=0.9999082', 'Implementation of the AND gate involved defining training data and output values']}, {'end': 31665.242, 'segs': [{'end': 30913.408, 'src': 'embed', 'start': 30883.512, 'weight': 2, 'content': [{'end': 30884.333, 'text': 'Pardon me for that.', 'start': 30883.512, 'duration': 0.821}, {'end': 30887.194, 'text': "So I'm just gonna make it right now, S-Q-U-A-R-E.", 'start': 30884.373, 'duration': 2.821}, {'end': 30889.595, 'text': "All right, let's go ahead and execute it once more.", 'start': 30887.734, 'duration': 1.861}, {'end': 30895.221, 'text': 'So yeah, in three epochs, we got the value of mean squared error as zero.', 'start': 30890.919, 'duration': 4.302}, {'end': 30899.762, 'text': 'That means it took us three iterations in order to reduce the output to zero.', 'start': 30895.641, 'duration': 4.121}, {'end': 30902.683, 'text': 'So this is how you can actually implement a logic gate.', 'start': 30900.363, 'duration': 2.32}, {'end': 30909.046, 'text': 'or you can say this is how you can actually classify the high and low outputs of a particular logic gate using single layer perceptron.', 'start': 30902.683, 'duration': 6.363}, {'end': 30911.927, 'text': 'Similarly, you can do that for OR gate as well.', 'start': 30909.846, 'duration': 2.081}, {'end': 30913.408, 'text': 'Consider this as an assignment.', 'start': 30911.987, 'duration': 1.421}], 'summary': 'Achieved mean squared error of zero in three iterations to implement a single layer perceptron for logic gate classification.', 'duration': 29.896, 'max_score': 30883.512, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF030883512.jpg'}, {'end': 31119.976, 'src': 'embed', 'start': 31084.428, 'weight': 0, 'content': [{'end': 31103.763, 'text': "for that I'm gonna type in x is equals to tf.placeholder, tf.float32, 32, and the shape will be none comma 784..", 'start': 31084.428, 'duration': 19.335}, {'end': 31107.846, 'text': 'Now the input images X will consist of 2D tensors of floating point numbers.', 'start': 31103.763, 'duration': 4.083}, {'end': 31112.23, 'text': 'Here we assign it a shape of say none comma 784 as you can see.', 'start': 31108.387, 'duration': 3.843}, {'end': 31119.976, 'text': 'So where 784 is the dimensionality of single flattened 28 by 28 pixel MNIST image of handwritten digits.', 'start': 31112.77, 'duration': 7.206}], 'summary': 'Defining input images x as 2d tensors of floating point numbers with a shape of none comma 784 representing the dimensionality of single flattened 28 by 28 pixel mnist image.', 'duration': 35.548, 'max_score': 31084.428, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF031084428.jpg'}, {'end': 31457.601, 'src': 'embed', 'start': 31432.002, 'weight': 4, 'content': [{'end': 31437.528, 'text': 'So what this one line basically will do, it will minimize the cross entropy, which is nothing but the loss function that we have defined.', 'start': 31432.002, 'duration': 5.526}, {'end': 31444.034, 'text': 'Now in the next step what we are going to do, we are going to load 100 training examples in each training iteration.', 'start': 31438.351, 'duration': 5.683}, {'end': 31447.095, 'text': "So each time the training iteration happens, it'll take 100 examples.", 'start': 31444.094, 'duration': 3.001}, {'end': 31453.999, 'text': 'We then run the train underscore step operation, which is nothing but to reduce the error using feed underscore dict, which is nothing,', 'start': 31447.576, 'duration': 6.423}, {'end': 31457.601, 'text': 'but we are going to feed the real values to our placeholder y.', 'start': 31453.999, 'duration': 3.602}], 'summary': 'Minimize cross entropy loss function, training with 100 examples per iteration.', 'duration': 25.599, 'max_score': 31432.002, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF031432002.jpg'}, {'end': 31536.964, 'src': 'embed', 'start': 31510.473, 'weight': 1, 'content': [{'end': 31525.155, 'text': 'Feed underscore dict equal to x colon batch zero comma y colon batch one.', 'start': 31510.473, 'duration': 14.682}, {'end': 31526.776, 'text': "That's it.", 'start': 31526.436, 'duration': 0.34}, {'end': 31533.241, 'text': "Now we need to evaluate our model, we need to figure out how well our model is doing, and for that I'm gonna make use of tf.argmax function.", 'start': 31527.857, 'duration': 5.384}, {'end': 31536.964, 'text': 'Now this tf.argmax function, let me tell you how it works.', 'start': 31533.902, 'duration': 3.062}], 'summary': 'Using tf.argmax to evaluate model performance.', 'duration': 26.491, 'max_score': 31510.473, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF031510473.jpg'}, {'end': 31604.669, 'src': 'embed', 'start': 31577.073, 'weight': 3, 'content': [{'end': 31580.535, 'text': 'It is there in our data set present already and we know that it is true.', 'start': 31577.073, 'duration': 3.462}, {'end': 31586.681, 'text': 'So what we are doing, we are using tf.equal function to check if our actual prediction matches the desired prediction.', 'start': 31581.038, 'duration': 5.643}, {'end': 31588.041, 'text': 'This is how it is working.', 'start': 31587.181, 'duration': 0.86}, {'end': 31593.224, 'text': "So now what we're gonna do, we're gonna calculate the accuracy to determine what fraction are correct.", 'start': 31588.862, 'duration': 4.362}, {'end': 31596.225, 'text': 'We cast a floating point numbers and then take the mean.', 'start': 31593.764, 'duration': 2.461}, {'end': 31604.669, 'text': "So now I'm gonna define a variable for accuracy, so I'm just gonna type in accuracy equals to tf.reduce underscore mean.", 'start': 31597.026, 'duration': 7.643}], 'summary': 'Using tf.equal to calculate accuracy for predictions in data set.', 'duration': 27.596, 'max_score': 31577.073, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF031577073.jpg'}], 'start': 30481.5, 'title': 'Tensorflow model building, logic gates, handwritten digit classification, and regression model implementation', 'summary': 'Explains building models with tensorflow, logic gates, handwritten digit classification, achieving zero mean squared error in 3 epochs, and regression model implementation achieving about 90% accuracy on the test data.', 'chapters': [{'end': 30767.601, 'start': 30481.5, 'title': 'Tensorflow model building', 'summary': 'Explains the process of building a model using tensorflow, including defining variables, activation functions, calculating errors, updating weights, and evaluating the model in a tensorflow session.', 'duration': 286.101, 'highlights': ['The chapter explains the process of building a model using TensorFlow, including defining variables, activation functions, calculating errors, updating weights, and evaluating the model in a TensorFlow session.', 'TensorFlow works by building a model out of empty tensors and plugging in known values for evaluation.', 'The special TensorFlow object in this case is a three cross one tensors of vectors.', 'A variable with random values is defined using tf.variable and tf.random_normal function.', 'The activation function, specifically a step function, is defined for the model.', 'The output error and mean squared error of the model can be calculated using specific TensorFlow functions.', 'The evaluation of certain tensor functions can update the values of variables like the tensor of weights W.', 'The desired adjustment based on error is calculated and added to the weights W using matrix multiplication and addition.', 'The model has to be evaluated by TensorFlow session and all variables need to be initialized before the evaluation.']}, {'end': 31260.136, 'start': 30770.424, 'title': 'Implementing logic gates and handwritten digit classification', 'summary': 'Covers implementing logic gates using single layer perceptron and classifying handwritten digits using the mnist dataset, with a focus on iterations and error reduction, achieving zero mean squared error in 3 epochs, and using tensorflow for building computation graph and defining placeholders, weights, and biases.', 'duration': 489.712, 'highlights': ['The chapter emphasizes the importance of performing various iterations to achieve zero error, as demonstrated by reaching zero mean squared error in 3 epochs when implementing a logic gate using single layer perceptron.', 'It details the process of classifying handwritten digits using the MNIST dataset, which consists of 55,000 training sets and 10,000 test sets, and highlights the use of TensorFlow for building the computation graph and defining placeholders, weights, and biases.', 'It explains the concept of one-hot encoding for representing digit classes, where only one output is active at a time, and the need to define variables such as ERR, target, and epoch to control the error reduction process when implementing logic gates.', 'The chapter also stresses the significance of defining error, target, and epoch variables to control the iteration process, with the goal of reducing error to zero, and highlights the use of TensorFlow for initializing variables and running the computation graph.']}, {'end': 31665.242, 'start': 31260.797, 'title': 'Implementing regression model with tensorflow', 'summary': "Details the implementation of a regression model using tensorflow, including defining the loss function, training the model using steepest gradient descent with a 0.5 learning rate, loading 100 training examples in each iteration, and evaluating the model's accuracy, achieving about 90% accuracy on the test data.", 'duration': 404.445, 'highlights': ["Evaluating the model's accuracy on the test data, achieving about 90% accuracy", 'Training the model using steepest gradient descent with a 0.5 learning rate', 'Defining the loss function as cross entropy and explaining its calculation process', 'Loading 100 training examples in each training iteration and using feed_dict to replace placeholder tensors with training examples']}], 'duration': 1183.742, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF030481500.jpg', 'highlights': ['Achieving zero mean squared error in 3 epochs when implementing a logic gate using single layer perceptron', "Evaluating the model's accuracy on the test data, achieving about 90% accuracy", 'Defining the loss function as cross entropy and explaining its calculation process', 'Training the model using steepest gradient descent with a 0.5 learning rate', 'Explaining the process of building a model using TensorFlow, including defining variables, activation functions, calculating errors, updating weights, and evaluating the model in a TensorFlow session']}, {'end': 32835.985, 'segs': [{'end': 31695.754, 'src': 'embed', 'start': 31665.282, 'weight': 6, 'content': [{'end': 31666.483, 'text': 'So this is the mistake that I made.', 'start': 31665.282, 'duration': 1.201}, {'end': 31671.808, 'text': 'So yeah, now I think the code looks pretty fine to me and we can go ahead and run this.', 'start': 31667.164, 'duration': 4.644}, {'end': 31674.271, 'text': 'Let us see what happens when we run this.', 'start': 31672.429, 'duration': 1.842}, {'end': 31690.83, 'text': 'So, guys, it is complete now, and this is the accuracy of a model which is 91.4% and which is pretty bad when you talk about a data set like MNIST,', 'start': 31681.584, 'duration': 9.246}, {'end': 31693.372, 'text': 'but yeah, with a single layer which is very, very good.', 'start': 31690.83, 'duration': 2.542}, {'end': 31695.754, 'text': 'So we have got an accuracy of around 92% on the MNIST data set.', 'start': 31693.532, 'duration': 2.222}], 'summary': 'Code achieved 92% accuracy on the mnist dataset.', 'duration': 30.472, 'max_score': 31665.282, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF031665282.jpg'}, {'end': 31957.504, 'src': 'embed', 'start': 31931.783, 'weight': 5, 'content': [{'end': 31936.165, 'text': "We're gonna compare the actual output and the desired output and according to the difference, we're gonna update the weights.", 'start': 31931.783, 'duration': 4.382}, {'end': 31942.73, 'text': 'So what is back propagation? So back propagation is nothing but a supervised learning algorithm for multi-layer perceptron.', 'start': 31937.005, 'duration': 5.725}, {'end': 31945.493, 'text': 'Now let us understand what exactly is this algorithm.', 'start': 31943.271, 'duration': 2.222}, {'end': 31949.056, 'text': 'Let us understand this with an example that is there in front of your screen.', 'start': 31946.354, 'duration': 2.702}, {'end': 31955.062, 'text': 'So these two are our input neurons, these two are our hidden neurons, and these two are our output neurons.', 'start': 31950.397, 'duration': 4.665}, {'end': 31956.543, 'text': 'Now our aim is to get .', 'start': 31955.542, 'duration': 1.001}, {'end': 31957.504, 'text': '01 and .', 'start': 31956.543, 'duration': 0.961}], 'summary': 'Back propagation updates weights based on output difference in multi-layer perceptron.', 'duration': 25.721, 'max_score': 31931.783, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF031931783.jpg'}, {'end': 32325.352, 'src': 'embed', 'start': 32295.127, 'weight': 1, 'content': [{'end': 32298.269, 'text': 'Now the resulting images are down sampled again to seven cross seven pixels.', 'start': 32295.127, 'duration': 3.142}, {'end': 32303.451, 'text': 'The output of the second convolutional layer is 36 images of seven cross seven pixels each.', 'start': 32299.008, 'duration': 4.443}, {'end': 32311.915, 'text': 'Now these are then flattened to a single vector of length seven cross, seven cross 36,, which is 1764,,', 'start': 32303.991, 'duration': 7.924}, {'end': 32315.497, 'text': 'which is used as the input to a fully connected layer with 128 neurons.', 'start': 32311.915, 'duration': 3.582}, {'end': 32325.352, 'text': 'And this feeds into another fully connected layer with 10 neurons, one for each of the classes, which is used to determine the class of the image,', 'start': 32317.489, 'duration': 7.863}], 'summary': 'Image downsampled to 7x7, resulting in 1764-length vector fed into fully connected layers for classification.', 'duration': 30.225, 'max_score': 32295.127, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF032295127.jpg'}, {'end': 32402.8, 'src': 'embed', 'start': 32373.116, 'weight': 0, 'content': [{'end': 32373.456, 'text': 'So it is 98.8%.', 'start': 32373.116, 'duration': 0.34}, {'end': 32377.277, 'text': 'So on the test sets we had around 10, 000 test samples out of which we predicted 9876 correctly.', 'start': 32373.456, 'duration': 3.821}, {'end': 32390.461, 'text': 'So it is pretty good actually, if you see so it, because in the single layer perceptron example that we took, we were getting accuracy of around 92%,', 'start': 32383.226, 'duration': 7.235}, {'end': 32393.688, 'text': 'but here we are getting around 99%, which is actually very good.', 'start': 32390.461, 'duration': 3.227}, {'end': 32394.089, 'text': 'All right.', 'start': 32393.868, 'duration': 0.221}, {'end': 32397.377, 'text': "So these are the topics that we have covered in today's session.", 'start': 32394.895, 'duration': 2.482}, {'end': 32399.958, 'text': 'We started with what exactly is deep learning.', 'start': 32397.777, 'duration': 2.181}, {'end': 32402.8, 'text': 'We took an analogy of classification of cats and dogs.', 'start': 32400.299, 'duration': 2.501}], 'summary': "Achieved 98.8% accuracy on 10,000 test samples, outperforming single-layer perceptron's 92% accuracy.", 'duration': 29.684, 'max_score': 32373.116, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF032373116.jpg'}, {'end': 32544.923, 'src': 'embed', 'start': 32516.608, 'weight': 7, 'content': [{'end': 32523.271, 'text': 'but this session will try to cover things in a fundamental level so that it prepares in a very common ground.', 'start': 32516.608, 'duration': 6.663}, {'end': 32531.696, 'text': 'irrespective of which role you are applying for, you might be able to at least get a perspective of what data science requires as such right.', 'start': 32523.271, 'duration': 8.425}, {'end': 32538.359, 'text': "so, with that note, let's see how we going to structure the various questions that's going to come up in this session today,", 'start': 32531.696, 'duration': 6.663}, {'end': 32544.923, 'text': 'majorly focusing on statistics, data analytics, machine learning and probability.', 'start': 32539.238, 'duration': 5.685}], 'summary': 'Fundamental overview of data science covering statistics, analytics, machine learning, and probability.', 'duration': 28.315, 'max_score': 32516.608, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF032516608.jpg'}, {'end': 32704.78, 'src': 'embed', 'start': 32682.778, 'weight': 3, 'content': [{'end': 32691.407, 'text': 'so the people you bring in the technology you bring in the ideas you are working on all of it should kind of give you some return on the investment you have put in.', 'start': 32682.778, 'duration': 8.629}, {'end': 32695.312, 'text': "so that's where industries are started to looking at data science.", 'start': 32691.407, 'duration': 3.905}, {'end': 32704.78, 'text': 'So various subjects which are very important for you to know statistics, computer science, applied mathematics, and then subjects like linear algebra,', 'start': 32696.195, 'duration': 8.585}], 'summary': 'Industries are looking at data science for roi. key subjects include statistics, computer science, applied mathematics, and linear algebra.', 'duration': 22.002, 'max_score': 32682.778, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF032682778.jpg'}, {'end': 32782.105, 'src': 'embed', 'start': 32752.527, 'weight': 4, 'content': [{'end': 32757.313, 'text': 'And with availability of libraries like NumPy Pandas,', 'start': 32752.527, 'duration': 4.786}, {'end': 32765.002, 'text': 'Python has now established its ground very strongly in giving a very robust framework for designing data science solutions.', 'start': 32757.313, 'duration': 7.689}, {'end': 32775.661, 'text': 'right and, in particular, things which it has like list, dictionaries, tuples and stats, are one of its own sort, of one of its capabilities,', 'start': 32765.575, 'duration': 10.086}, {'end': 32782.105, 'text': 'which sets python in its own league of programming languages suitable for upcoming out with data science solutions.', 'start': 32775.661, 'duration': 6.444}], 'summary': "Python's robust data science framework with numpy and pandas sets it apart in the programming world.", 'duration': 29.578, 'max_score': 32752.527, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF032752527.jpg'}], 'start': 31665.282, 'title': 'Model accuracy and deep learning', 'summary': 'Discusses model accuracy of 91.4% and explores multilayer perceptrons achieving 99% accuracy on the mnist dataset. it also covers deep learning concepts, data science interview preparation, and the importance of statistics, computer science, mathematics, linear algebra, calculus, and python programming.', 'chapters': [{'end': 31712.371, 'start': 31665.282, 'title': 'Model accuracy and recap', 'summary': "Discusses the model accuracy of 91.4% on the mnist dataset and provides a recap of the session's content.", 'duration': 47.089, 'highlights': ['The model achieved an accuracy of around 92% on the MNIST dataset, indicating 91.4% correct predictions on the 10,000 test images.', "The chapter concludes with a quick recap of the session's content."]}, {'end': 32394.089, 'start': 31712.891, 'title': 'Multilayer perceptrons and back propagation', 'summary': 'Explores the limitations of single layer perceptrons and introduces the concept of multilayer perceptrons, demonstrating how they can be used to solve complex classification problems efficiently, achieving an accuracy of 99% on the mnist dataset, and detailing the back propagation algorithm for updating weights in a neural network.', 'duration': 681.198, 'highlights': ['The chapter explores the limitations of single layer perceptrons and introduces the concept of multilayer perceptrons', 'Achieving an accuracy of 99% on the MNIST dataset using multilayer perceptrons', 'Detailed explanation of the back propagation algorithm for updating weights in a neural network']}, {'end': 32835.985, 'start': 32394.895, 'title': 'Understanding deep learning and data science', 'summary': 'Covered topics such as deep learning, single and multi-layer perceptron, tensorflow, and increased efficiency on the mnist dataset. data science interview preparation, fundamental questions, and skill sets were discussed, emphasizing the importance of statistics, computer science, mathematics, linear algebra, calculus, and python programming for data science solutions.', 'duration': 441.09, 'highlights': ['The efficiency on the MNIST dataset increased from 92% to around 99% using multi-layer convolutional networks compared to single layer perceptron.', 'Python is one of the most sought-after programming skills for building data science solutions, with libraries like NumPy and Pandas providing a robust framework.', 'Understanding the importance of statistics, computer science, applied mathematics, linear algebra, calculus, and python programming for data science solutions was emphasized.']}], 'duration': 1170.703, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF031665282.jpg', 'highlights': ['Achieving an accuracy of 99% on the MNIST dataset using multilayer perceptrons', 'The efficiency on the MNIST dataset increased from 92% to around 99% using multi-layer convolutional networks compared to single layer perceptron', 'The model achieved an accuracy of around 92% on the MNIST dataset, indicating 91.4% correct predictions on the 10,000 test images', 'Understanding the importance of statistics, computer science, applied mathematics, linear algebra, calculus, and python programming for data science solutions was emphasized', 'Python is one of the most sought-after programming skills for building data science solutions, with libraries like NumPy and Pandas providing a robust framework', 'Detailed explanation of the back propagation algorithm for updating weights in a neural network', 'The chapter explores the limitations of single layer perceptrons and introduces the concept of multilayer perceptrons', "The chapter concludes with a quick recap of the session's content"]}, {'end': 34584.268, 'segs': [{'end': 32942.262, 'src': 'embed', 'start': 32918.701, 'weight': 0, 'content': [{'end': 32925.324, 'text': 'at the same time, you want to do a really good study or analysis based on what data you have.', 'start': 32918.701, 'duration': 6.623}, {'end': 32931.985, 'text': 'so in statistics we normally use this idea of doing a kind of a randomized selection, right.', 'start': 32925.324, 'duration': 6.661}, {'end': 32939.259, 'text': "so with this randomized selection, we make sure that out of these 1 billion records, we are choosing a small subset, let's say of 1 million,", 'start': 32931.985, 'duration': 7.274}, {'end': 32942.262, 'text': 'which is a true representation of the entire population.', 'start': 32939.259, 'duration': 3.003}], 'summary': 'In statistics, a randomized selection of 1 million records can represent a population of 1 billion for analysis.', 'duration': 23.561, 'max_score': 32918.701, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF032918701.jpg'}, {'end': 33012.497, 'src': 'embed', 'start': 32984.301, 'weight': 5, 'content': [{'end': 32988.424, 'text': "but that doesn't represent the entire opinion of the population in that constituency.", 'start': 32984.301, 'duration': 4.123}, {'end': 32999.553, 'text': 'So selection bias is very important to handle and most of the time people employ things like randomized selection or selection sampling techniques like stratified sampling and so on.', 'start': 32988.908, 'duration': 10.645}, {'end': 33002.595, 'text': 'So by that you can minimize this selection bias.', 'start': 32999.612, 'duration': 2.983}, {'end': 33006.033, 'text': 'So these are some very generic questions.', 'start': 33003.911, 'duration': 2.122}, {'end': 33012.497, 'text': "So let's start with some sort of statistical questions and also how to deal with different types of data.", 'start': 33006.473, 'duration': 6.024}], 'summary': 'Addressing selection bias through techniques like randomized selection and stratified sampling can minimize it.', 'duration': 28.196, 'max_score': 32984.301, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF032984301.jpg'}, {'end': 33331.216, 'src': 'embed', 'start': 33301.848, 'weight': 4, 'content': [{'end': 33307.269, 'text': 'many other possibilities of applying certain modeling techniques comes out evidently there.', 'start': 33301.848, 'duration': 5.421}, {'end': 33310.951, 'text': 'And also there are many other modeling techniques in stats and machine learning,', 'start': 33307.75, 'duration': 3.201}, {'end': 33314.794, 'text': 'where there is a fundamental assumption that things should follow a normal distribution.', 'start': 33310.951, 'duration': 3.843}, {'end': 33316.614, 'text': 'If it is not following, then the model is wrong.', 'start': 33314.814, 'duration': 1.8}, {'end': 33320.35, 'text': 'So there are many use cases of knowing what normal distribution is.', 'start': 33317.108, 'duration': 3.242}, {'end': 33323.011, 'text': "But in simple terms, it's a symmetrical bell-shaped curve.", 'start': 33320.43, 'duration': 2.581}, {'end': 33325.413, 'text': 'AB testing.', 'start': 33324.872, 'duration': 0.541}, {'end': 33331.216, 'text': 'So quite a popular approach, people particularly who are working with product.', 'start': 33325.932, 'duration': 5.284}], 'summary': 'Various modeling techniques and the importance of normal distribution in stats and machine learning are discussed, along with the popularity of ab testing in product development.', 'duration': 29.368, 'max_score': 33301.848, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF033301848.jpg'}, {'end': 33479.511, 'src': 'embed', 'start': 33454.509, 'weight': 1, 'content': [{'end': 33461.731, 'text': 'and if it is on the negative side of the difference, we say the feature is not good and even if, when the difference is not at all there,', 'start': 33454.509, 'duration': 7.222}, {'end': 33465.451, 'text': 'we say even if we bring this new feature, nothing is going to happen.', 'start': 33461.731, 'duration': 3.72}, {'end': 33469.013, 'text': 'so this AB testing framework is quite robust in its own way.', 'start': 33465.451, 'duration': 3.562}, {'end': 33479.511, 'text': 'Right, and a very common question if you have like worked as data analyst or if you are expecting to be like sitting for this data analyst kind of a role.', 'start': 33470.407, 'duration': 9.104}], 'summary': 'Ab testing framework is robust for data analysis roles.', 'duration': 25.002, 'max_score': 33454.509, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF033454509.jpg'}, {'end': 33725.534, 'src': 'embed', 'start': 33701.562, 'weight': 3, 'content': [{'end': 33707.785, 'text': 'so the type 1 and type 2 errors needs to be taken care of when you are building any machine learning model.', 'start': 33701.562, 'duration': 6.223}, {'end': 33713.008, 'text': 'if these errors are low, then your model is going to move towards that 100% accuracy mark.', 'start': 33707.785, 'duration': 5.223}, {'end': 33716.229, 'text': 'but normally any machine learning model has its own limitations.', 'start': 33713.008, 'duration': 3.221}, {'end': 33721.272, 'text': 'so particularly there is one metric which we refer by calling sensitivity.', 'start': 33716.229, 'duration': 5.043}, {'end': 33725.534, 'text': 'so what happens is these true positives and true negatives needs to be controlled.', 'start': 33721.272, 'duration': 4.262}], 'summary': 'Controlling type 1 and type 2 errors in machine learning models is crucial for achieving high accuracy.', 'duration': 23.972, 'max_score': 33701.562, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF033701562.jpg'}, {'end': 33973.543, 'src': 'embed', 'start': 33951.021, 'weight': 8, 'content': [{'end': 33959.724, 'text': 'as you would be like very aware of, like things like standard deviation averages, how to interpret median, how to interpret quartiles right,', 'start': 33951.021, 'duration': 8.703}, {'end': 33963.165, 'text': 'the first quartile, second quartile and so and so on.', 'start': 33959.724, 'duration': 3.441}, {'end': 33965.786, 'text': 'and how, what do you mean by percentiles?', 'start': 33963.165, 'duration': 2.621}, {'end': 33967.706, 'text': 'right, these are some basic questions.', 'start': 33965.786, 'duration': 1.92}, {'end': 33973.543, 'text': 'a bit more complex in nature might be discussions around sensitivity, overfitting, under fitting.', 'start': 33967.706, 'duration': 5.837}], 'summary': 'Discussed basic statistics and advanced topics like sensitivity and overfitting.', 'duration': 22.522, 'max_score': 33951.021, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF033951021.jpg'}, {'end': 34176.2, 'src': 'embed', 'start': 34152.025, 'weight': 2, 'content': [{'end': 34162.051, 'text': 'you get rid of all the unwanted noise from the data and then finally prepare the data for doing the sort of modeling exercise or doing descriptive analytics on top of it.', 'start': 34152.025, 'duration': 10.026}, {'end': 34167.615, 'text': 'so this cleaning and understanding the data, doing lot of explorations with plot,', 'start': 34162.051, 'duration': 5.564}, {'end': 34173.398, 'text': 'in essence takes close to 70 to 80% of your time in any data analysis task.', 'start': 34167.615, 'duration': 5.783}, {'end': 34176.2, 'text': 'so if your company maintains the data in a very well structured way,', 'start': 34173.398, 'duration': 2.802}], 'summary': 'Data cleaning and exploration takes 70-80% of data analysis time.', 'duration': 24.175, 'max_score': 34152.025, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF034152025.jpg'}, {'end': 34246.1, 'src': 'embed', 'start': 34221.131, 'weight': 6, 'content': [{'end': 34227.194, 'text': 'it is not possible to come out with such answers to complex problems like this with just one variable.', 'start': 34221.131, 'duration': 6.063}, {'end': 34237.528, 'text': "so you might also want sometimes to move beyond one variable and talk about, let's say, how to do multivariate or bivariate kind of analysis.", 'start': 34227.194, 'duration': 10.334}, {'end': 34246.1, 'text': 'so often times, this question comes up where you like ask to distinguish between this univariate, bivariate and multivariate analysis,', 'start': 34238.274, 'duration': 7.826}], 'summary': 'Analyzing complex problems may require multivariate or bivariate analysis beyond univariate.', 'duration': 24.969, 'max_score': 34221.131, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF034221131.jpg'}, {'end': 34348.992, 'src': 'embed', 'start': 34310.812, 'weight': 7, 'content': [{'end': 34312.373, 'text': 'but in that there might be lot,', 'start': 34310.812, 'duration': 1.561}, {'end': 34321.598, 'text': 'many number of times when even the randomized sampling of getting the true representative from the population like that might not work well right.', 'start': 34312.373, 'duration': 9.225}, {'end': 34329.223, 'text': 'so in those cases you might want to do some sort of systematic sampling or maybe a cluster based sampling as well,', 'start': 34321.598, 'duration': 7.625}, {'end': 34335.406, 'text': 'wherein you might decide to say I want to analyze the issue with only five regions in my mind,', 'start': 34329.603, 'duration': 5.803}, {'end': 34338.527, 'text': 'and with the five regions I am going to form different clusters.', 'start': 34335.406, 'duration': 3.121}, {'end': 34348.992, 'text': 'or in the systematic sampling you might also want to say that with the five regions that I have got I might want to analyze only for one product right,', 'start': 34338.527, 'duration': 10.465}], 'summary': 'Randomized sampling may not work well in some cases; consider systematic or cluster-based sampling instead.', 'duration': 38.18, 'max_score': 34310.812, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF034310812.jpg'}, {'end': 34488.893, 'src': 'embed', 'start': 34464.209, 'weight': 9, 'content': [{'end': 34471.896, 'text': 'but in most often, like most of the time, not all the 10, 000 variables are useful, right, the input variables.', 'start': 34464.209, 'duration': 7.687}, {'end': 34482.507, 'text': 'so what we can do is we might want to transform this data set in a lower dimensional space, by which we mean this 10, 000 columns can be reduced to,', 'start': 34471.896, 'duration': 10.611}, {'end': 34485.109, 'text': "let's say, only 100 columns, right?", 'start': 34482.507, 'duration': 2.602}, {'end': 34488.893, 'text': 'so eigenvalue and eigenvectors are these ideas which helps us in this transformation.', 'start': 34485.109, 'duration': 3.784}], 'summary': 'Data set can be transformed to lower dimension, from 10,000 columns to 100, using eigenvalue and eigenvectors.', 'duration': 24.684, 'max_score': 34464.209, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF034464209.jpg'}], 'start': 32835.985, 'title': 'Data analysis techniques and concepts', 'summary': 'Covers the challenges of selection bias, importance of normal distribution, ab testing, and data cleaning techniques, with an emphasis on randomized selection, understanding statistical concepts, and spending 70-80% of time on data cleaning.', 'chapters': [{'end': 33006.033, 'start': 32835.985, 'title': 'Selection bias in data analysis', 'summary': 'Discusses the challenges of selection bias in data analysis, particularly when dealing with large datasets, emphasizing the need for randomized selection to mitigate bias and improve the representativeness of the sample.', 'duration': 170.048, 'highlights': ['The fundamental step in data analysis is selecting a representative sample from a large dataset, such as 1 billion records, to ensure the analysis is based on a true representation of the entire population.', 'Randomized selection is employed to choose a small subset, like 1 million records, which accurately represents the entire population, but it may still introduce bias due to not using the entire dataset.', 'Handling selection bias is crucial, and techniques like randomized selection and stratified sampling are commonly used to minimize bias in data analysis.']}, {'end': 33320.35, 'start': 33006.473, 'title': 'Data analysis: normal distribution and data formats', 'summary': 'Discusses the importance of understanding normal distribution in data analysis, including its significance, properties, and use cases, and also explains the different data formats - long and wide - and their implications in data visualization and analysis.', 'duration': 313.877, 'highlights': ['The chapter explains the significance of normal distribution in data analysis and its implications in statistical techniques and model building exercises.', 'The chapter elaborates on the different data formats - long and wide - and their impact on data visualization and analysis tasks, particularly in building visualization dashboards.']}, {'end': 34095.351, 'start': 33320.43, 'title': 'Understanding ab testing and statistical concepts', 'summary': 'Explains the ab testing framework, emphasizing its importance in testing new website features, and then delves into statistical concepts like sensitivity, specificity, overfitting, and underfitting for machine learning models, stressing their significance in model evaluation and understanding statistical concepts.', 'duration': 774.921, 'highlights': ['The AB testing framework is crucial for testing new website features and changes, ensuring that the number of customers visiting the website does not decrease.', "Understanding sensitivity and specificity is vital for evaluating machine learning models, as they indicate the model's ability to predict positive and negative cases accurately.", 'Overfitting and underfitting are common issues in building machine learning models, and striking a balance between the two is essential for accurate model performance.', 'Statistical concepts like standard deviation, mean, quartiles, percentiles, sensitivity, specificity, overfitting, and underfitting are essential for data analysts to understand and prepare for interviews.']}, {'end': 34584.268, 'start': 34095.351, 'title': 'Data analysis techniques and best practices', 'summary': 'Emphasizes the importance of data cleaning, multivariate analysis, and sampling techniques in data analysis, highlighting that 70-80% of the time is spent on data cleaning, and eigenvalue and eigenvectors are essential for reducing the dimensions of a large dataset.', 'duration': 488.917, 'highlights': ['The importance of data cleaning and understanding the data, which takes close to 70-80% of the time in any data analysis task.', 'The significance of multivariate analysis and the need to move beyond univariate analysis to understand complex problems with multiple factors involved.', 'The importance of sampling techniques such as cluster-based and systematic sampling for obtaining representative data for analysis.', 'The role of eigenvalue and eigenvectors in reducing the dimensions of a large dataset, with a focus on compressing data and analyzing correlations between variables.']}], 'duration': 1748.283, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF032835985.jpg', 'highlights': ['Randomized selection ensures representative subset from 1 billion records', 'AB testing framework crucial for testing new website features', 'Data cleaning and understanding takes 70-80% of time in analysis', 'Understanding sensitivity and specificity vital for evaluating ML models', 'Importance of normal distribution in statistical techniques and model building', 'Handling selection bias crucial, techniques like stratified sampling used', 'Significance of multivariate analysis in understanding complex problems', 'Sampling techniques like cluster-based and systematic sampling important', 'Statistical concepts like standard deviation, mean, quartiles essential', 'Role of eigenvalue and eigenvectors in reducing dimensions of large dataset']}, {'end': 37424.789, 'segs': [{'end': 34722.823, 'src': 'embed', 'start': 34697.386, 'weight': 2, 'content': [{'end': 34702.349, 'text': 'right because you are giving these therapies on the healthy cells if the patient is not having the cancers.', 'start': 34697.386, 'duration': 4.963}, {'end': 34707.172, 'text': 'so in these cases sort of the false positives becomes a bit more important.', 'start': 34702.349, 'duration': 4.823}, {'end': 34713.897, 'text': "so it will be absolutely fine if your model says the patient doesn't have cancer, if even if there is, like this,", 'start': 34707.172, 'duration': 6.725}, {'end': 34718.941, 'text': 'slight possibility of cancer present in the cells of the patients.', 'start': 34713.897, 'duration': 5.044}, {'end': 34722.823, 'text': 'but in that case you are not like exposing the patient with a chemotherapy there right,', 'start': 34718.941, 'duration': 3.882}], 'summary': 'Therapies on healthy cells require minimizing false positives to avoid unnecessary chemotherapy.', 'duration': 25.437, 'max_score': 34697.386, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF034697386.jpg'}, {'end': 35008.413, 'src': 'embed', 'start': 34982.766, 'weight': 0, 'content': [{'end': 34990.388, 'text': 'which will make sure that during the process there is one part dedicatedly given for the validation of the model.', 'start': 34982.766, 'duration': 7.622}, {'end': 34997.29, 'text': 'and when the model is done you might see that the final model is well trained on the data, at the same time validated.', 'start': 34990.388, 'duration': 6.902}, {'end': 35003.452, 'text': 'but when the model is completely done, then only you get into a process that we call testing right.', 'start': 34997.29, 'duration': 6.162}, {'end': 35008.413, 'text': 'so you can imagine like this you have a data of thousand records.', 'start': 35003.452, 'duration': 4.961}], 'summary': 'Dedicated validation part ensures well-trained, validated model before testing 1000 records.', 'duration': 25.647, 'max_score': 34982.766, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF034982766.jpg'}, {'end': 35103.813, 'src': 'embed', 'start': 35073.63, 'weight': 3, 'content': [{'end': 35077.373, 'text': 'and when this model is done, using this k-fold cross validation approach,', 'start': 35073.63, 'duration': 3.743}, {'end': 35082.918, 'text': 'in the end you will get a model which you can then use on the testing data to see if the accuracies are good or not.', 'start': 35077.373, 'duration': 5.545}, {'end': 35086.626, 'text': 'So this kind of brings in a lot of performance improvements.', 'start': 35083.885, 'duration': 2.741}, {'end': 35093.309, 'text': 'People have also found validation set to be a really good way of tuning the parameters.', 'start': 35086.986, 'duration': 6.323}, {'end': 35098.491, 'text': 'In many machine learning models, as you might know, there are something called parameters, typically in the neural network models.', 'start': 35093.409, 'duration': 5.082}, {'end': 35103.813, 'text': 'And these parameters need to be tuned as the model is kind of proceeding.', 'start': 35099.411, 'duration': 4.402}], 'summary': 'K-fold cross validation helps improve model performance and tune parameters.', 'duration': 30.183, 'max_score': 35073.63, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF035073630.jpg'}, {'end': 35547.339, 'src': 'embed', 'start': 35518.4, 'weight': 5, 'content': [{'end': 35523.583, 'text': 'These kind of decisions can very robustly be taken from a logistic regression algorithm,', 'start': 35518.4, 'duration': 5.183}, {'end': 35530.147, 'text': 'and these algorithms are best suited for two class problems or a binary problem, where you have either yes or no.', 'start': 35523.583, 'duration': 6.564}, {'end': 35538.556, 'text': 'Quite a common technique, as I mentioned, and in all the possible cases, wherever you have these binary classes of problems,', 'start': 35530.734, 'duration': 7.822}, {'end': 35540.457, 'text': 'you might use a logistic regression.', 'start': 35538.556, 'duration': 1.901}, {'end': 35547.339, 'text': 'A political leader winning an election or not, somebody getting a success in an examination or not and, as I mentioned,', 'start': 35541.017, 'duration': 6.322}], 'summary': 'Logistic regression is suitable for binary problems like election wins and exam success.', 'duration': 28.939, 'max_score': 35518.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF035518400.jpg'}, {'end': 35660.461, 'src': 'embed', 'start': 35632.943, 'weight': 1, 'content': [{'end': 35639.645, 'text': 'For Amazon, it is, if you give a recommendation below a page, people might buy more than one product in a transaction.', 'start': 35632.943, 'duration': 6.702}, {'end': 35643.446, 'text': 'For Facebook, they will grow their network of people.', 'start': 35640.125, 'duration': 3.321}, {'end': 35654.53, 'text': 'Their connection between the users are going to go stronger and hence, obviously, the kind of ads that Facebook wants to sell will also start to grow.', 'start': 35644.427, 'duration': 10.103}, {'end': 35660.461, 'text': 'And more the users, more the connections, more is the sort of interactions you know about the connections,', 'start': 35655.655, 'duration': 4.806}], 'summary': 'Amazon sees increased sales with recommendations, while facebook aims to grow user network and ad sales.', 'duration': 27.518, 'max_score': 35632.943, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF035632943.jpg'}, {'end': 35800.971, 'src': 'embed', 'start': 35774.978, 'weight': 6, 'content': [{'end': 35781.421, 'text': 'right, so linear regression models in machine learning is one such technique which can regress over a given input data,', 'start': 35774.978, 'duration': 6.443}, {'end': 35789.325, 'text': 'which might include the properties of the houses, like number of bedrooms, the area in square feet and so on, and finally predict a value,', 'start': 35781.421, 'duration': 7.904}, {'end': 35795.748, 'text': "a crisp value, which will be exactly in terms of, let's say, dollar or in any other currency, the value of the price.", 'start': 35789.325, 'duration': 6.423}, {'end': 35800.971, 'text': 'And the idea is, once again, that you have a training data with you which has labels.', 'start': 35796.468, 'duration': 4.503}], 'summary': 'Linear regression predicts house price based on features like bedrooms and area.', 'duration': 25.993, 'max_score': 35774.978, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF035774978.jpg'}, {'end': 35997.777, 'src': 'embed', 'start': 35969.465, 'weight': 4, 'content': [{'end': 35980.649, 'text': 'So these are the two commonly used sort of recommendation algorithms, normally referred to as item-based, so IBCF and the UBCF.', 'start': 35969.465, 'duration': 11.184}, {'end': 35987.131, 'text': "The idea, as I mentioned, is to compare to users or to compare to movies, or let's say items in particular.", 'start': 35980.969, 'duration': 6.162}, {'end': 35990.892, 'text': 'So the item can be anything, a product, a movie, or a person.', 'start': 35987.491, 'duration': 3.401}, {'end': 35993.734, 'text': 'and the sort of way it builds.', 'start': 35991.693, 'duration': 2.041}, {'end': 35997.777, 'text': 'the model is given lot of users and their particular.', 'start': 35993.734, 'duration': 4.043}], 'summary': 'Comparison of commonly used recommendation algorithms: ibcf and ubcf based on item/user similarities.', 'duration': 28.312, 'max_score': 35969.465, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF035969465.jpg'}, {'end': 36163.144, 'src': 'embed', 'start': 36132.498, 'weight': 7, 'content': [{'end': 36134.738, 'text': 'then the analysis goes in a different direction.', 'start': 36132.498, 'duration': 2.24}, {'end': 36139.819, 'text': "so it's important you handle the outlier before you start to build your model or do any analysis.", 'start': 36134.738, 'duration': 5.081}, {'end': 36148.841, 'text': 'otherwise your insights or your models output might totally give you a different direction and there are very different ways to handle the outliers.', 'start': 36139.819, 'duration': 9.022}, {'end': 36159.742, 'text': 'so some people use approaches like removing any data points, which is like outside of the range of mean plus three standard deviation right,', 'start': 36148.841, 'duration': 10.901}, {'end': 36163.144, 'text': 'or sometimes people also use the percentile way of doing it.', 'start': 36159.742, 'duration': 3.402}], 'summary': 'Handle outliers before analysis to avoid misleading insights or model output. options include removing data points outside mean plus three standard deviations or using the percentile method.', 'duration': 30.646, 'max_score': 36132.498, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF036132498.jpg'}, {'end': 36280.249, 'src': 'embed', 'start': 36257.742, 'weight': 9, 'content': [{'end': 36265.885, 'text': 'and then comes the kind of exercise of exploring the data in which you will identify outliers, missing values, and if you need any transformations,', 'start': 36257.742, 'duration': 8.143}, {'end': 36270.546, 'text': 'like converting from a log format to a wide format or the vice versa,', 'start': 36265.885, 'duration': 4.661}, {'end': 36277.088, 'text': 'you do all that steps in the second and the third step and once we have found out that the data is very good, now,', 'start': 36270.546, 'duration': 6.542}, {'end': 36280.249, 'text': 'after we have removed all the outliers and the missing values and so on.', 'start': 36277.088, 'duration': 3.161}], 'summary': 'Data exploration involves identifying outliers, missing values, and transformations to ensure data quality.', 'duration': 22.507, 'max_score': 36257.742, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF036257742.jpg'}, {'end': 36540.343, 'src': 'embed', 'start': 36515.565, 'weight': 8, 'content': [{'end': 36521.649, 'text': 'but if your points inside a cluster itself is very spread out and your clusters are very close to each other,', 'start': 36515.565, 'duration': 6.084}, {'end': 36523.811, 'text': 'then the distortion is going to be very high.', 'start': 36521.649, 'duration': 2.162}, {'end': 36531.456, 'text': 'so in those cases the value of k needs to be maybe further increased and at one point using an idea called an elbow curve,', 'start': 36523.811, 'duration': 7.645}, {'end': 36536.06, 'text': 'which will show you a small kink from where there is a sharp dip in the distortion values,', 'start': 36531.456, 'duration': 4.604}, {'end': 36540.343, 'text': 'and that is an appropriate value for k while we are building a k-means algorithm.', 'start': 36536.06, 'duration': 4.283}], 'summary': 'In k-means, if clusters are close and points are spread, increase k. use elbow curve to find appropriate k.', 'duration': 24.778, 'max_score': 36515.565, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF036515565.jpg'}], 'start': 34585.704, 'title': 'Machine learning and data analysis', 'summary': 'Delves into the significance of false positive and false negative, the machine learning model building process, the impact of recommender systems and linear regression, the importance of handling outliers in data analysis, and various data analysis, probability, and machine learning techniques, encompassing practical examples and applications across different domains.', 'chapters': [{'end': 34902.524, 'start': 34585.704, 'title': 'Understanding false positive and false negative', 'summary': 'Explores the importance of false positive and false negative in different scenarios using examples from medical, legal, and financial domains, emphasizing the significance of minimizing errors in decision-making models.', 'duration': 316.82, 'highlights': ['In the medical domain, false positives in cancer detection can lead to unnecessary chemotherapy with severe side effects, making it important to minimize the occurrence of false positives.', 'In legal contexts, false negatives in criminal conviction can result in letting a criminal go free, posing a greater harm than convicting an innocent person, highlighting the importance of minimizing false negatives.', 'In the banking industry, both false positives and false negatives have significant implications, with false positives leading to missed business opportunities and false negatives resulting in financial risks, emphasizing the importance of minimizing errors in decision-making models.']}, {'end': 35558.542, 'start': 34902.524, 'title': 'Machine learning model building process', 'summary': 'Explains the process of building a machine learning model, including data division into training, test, and validation sets, the use of k-fold cross validation for model validation, and the distinction between supervised and unsupervised learning in machine learning.', 'duration': 656.018, 'highlights': ['The process of building a machine learning model involves dividing the dataset into training, test, and validation sets, with the validation set used during the training process to ensure model validation.', "The use of k-fold cross validation allows for the validation of the model's performance across different subsets of the data, leading to improved model training and testing capabilities.", 'Supervised and unsupervised learning are the two most commonly used types of learning in machine learning, with the distinction based on the presence of a label for input attributes.', 'Logistic regression is a commonly used classification algorithm in supervised learning, particularly suited for binary classification problems.']}, {'end': 36052.272, 'start': 35560.06, 'title': 'Recommender systems and linear regression', 'summary': 'Highlights the widespread usage of recommender systems in platforms like amazon, youtube, netflix, and facebook, and the impact of the algorithms on business growth. it also discusses the practical applications of linear regression in predicting house prices and the key considerations for building effective models.', 'duration': 492.212, 'highlights': ['The impact of recommender systems on business growth is evident in the potential for increased transactions on Amazon and the strengthening of user connections on Facebook leading to the growth of ad sales.', 'The practical application of linear regression in machine learning involves predicting the value of a house in a specific locality based on attributes like number of bedrooms and area, which is crucial for building effective models.', 'The collaborative filtering algorithms, user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF), are commonly used in recommender systems to compare users or items and make suitable recommendations.', 'The importance of data cleaning and exploration in the process of building models is emphasized, with a focus on identifying and addressing extreme points in the datasets.']}, {'end': 36414.375, 'start': 36052.272, 'title': 'Handling outliers in data analysis', 'summary': 'Emphasizes the importance of handling outliers in data analysis, providing examples of how outliers can impact model predictions and suggesting techniques such as removing outliers based on mean and standard deviation or using percentile thresholds.', 'duration': 362.103, 'highlights': ['Importance of handling outliers in data analysis', 'Impact of outliers on model predictions', 'Techniques for handling outliers', 'Handling missing values in data analysis']}, {'end': 37424.789, 'start': 36414.375, 'title': 'Data analysis, probability and machine learning', 'summary': 'Covers data analysis techniques including handling missing values and determining k value for k-means algorithm, probability concepts such as calculating the probability of seeing a shooting star in an hour and generating a random number between 1 to 7 with a die, and probability problems related to coin tossing. it also discusses the approach to determine the probability of getting the 11th head after tossing a coin 10 times.', 'duration': 1010.414, 'highlights': ['Determining appropriate k value for k-means algorithm using the elbow curve approach', 'Calculation of probability of seeing a shooting star at least once in an hour', 'Method for generating a random number between 1 to 7 using a die', 'Approach to calculate the probability of getting the 11th head after tossing a coin 10 times']}], 'duration': 2839.085, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-ETQ97mXXF0/pics/-ETQ97mXXF034585704.jpg', 'highlights': ['The process of building a machine learning model involves dividing the dataset into training, test, and validation sets, with the validation set used during the training process to ensure model validation.', 'The impact of recommender systems on business growth is evident in the potential for increased transactions on Amazon and the strengthening of user connections on Facebook leading to the growth of ad sales.', 'In the medical domain, false positives in cancer detection can lead to unnecessary chemotherapy with severe side effects, making it important to minimize the occurrence of false positives.', "The use of k-fold cross validation allows for the validation of the model's performance across different subsets of the data, leading to improved model training and testing capabilities.", 'The collaborative filtering algorithms, user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF), are commonly used in recommender systems to compare users or items and make suitable recommendations.', 'Logistic regression is a commonly used classification algorithm in supervised learning, particularly suited for binary classification problems.', 'The practical application of linear regression in machine learning involves predicting the value of a house in a specific locality based on attributes like number of bedrooms and area, which is crucial for building effective models.', 'The importance of handling outliers in data analysis', 'Determining appropriate k value for k-means algorithm using the elbow curve approach', 'The importance of data cleaning and exploration in the process of building models is emphasized, with a focus on identifying and addressing extreme points in the datasets.']}], 'highlights': ['Walmart using data science to analyze customer data and boost sales', 'Exponential data generation rate: 2.5 quintillion bytes of data are generated daily, with an estimated increase to 1.7 MB per second for every individual by 2020', 'Random forest is used in banking to predict loan defaults, enabling approval or rejection of applications', 'Logistic regression model achieves 89% accuracy', 'The accuracy rate achieved for the Canon algorithm on the Iris data set is 97.29%', 'The chapter covers the evolution from single layer perceptrons to multi-layer perceptrons, explaining the limitations of single layer perceptrons and how multi-layer perceptrons overcame those limitations, with various examples provided', 'Achieving an accuracy of 99% on the MNIST dataset using multilayer perceptrons', 'Understanding the importance of statistics, computer science, applied mathematics, linear algebra, calculus, and python programming for data science solutions was emphasized', 'Data cleaning and understanding takes 70-80% of time in analysis', 'The process of building a machine learning model involves dividing the dataset into training, test, and validation sets, with the validation set used during the training process to ensure model validation']}