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Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial | Simplilearn

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🔥AI & Machine Learning Bootcamp(US Only): https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=MachineLearning-9f-GarcDY58&utm_medium=Descriptionff&utm_source=youtube
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This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning. It covers all the basics of Machine Learning, the different types of Machine Learning, and the various applications of Machine Learning used in different industries. This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression, K Means Clustering, Decision Tree, and Support Vector.
Dataset Link - https://drive.google.com/drive/folders/15lSrc4176J9z9_3WZo_b91BaNfItc2s0
Below topics are explained in this Machine Learning course for beginners:
0:00 Table of contents
01:46 Basics of Machine Learning
09:18 Why Machine Learning
13:25 What is Machine Learning
18:32 Types of Machine Learning
18:44 Supervised Learning
21:06 Reinforcement Learning
22:26 Supervised VS Unsupervised
23:38 Linear Regression
25:08 Introduction to Machine Learning
26:40 Application of Linear Regression
27:19 Understanding Linear Regression
28:00 Regression Equation
35:57 Multiple Linear Regression
55:45 Logistic Regression
56:04 What is Logistic Regression
59:35 What is Linear Regression
01:05:28 Comparing Linear & Logistic Regression
01:26:20 What is K-Means Clustering
01:38:00 How does K-Means Clustering work
02:15:15 What is Decision Tree
02:25:15 How does Decision Tree work
02:39:56 Random Forest Tutorial
02:41:52 Why Random Forest
02:43:21 What is Random Forest
02:52:02 How does Decision Tree work-
03:22:02 K-Nearest Neighbors Algorithm Tutorial
03:24:11 Why KNN
03:24:24 What is KNN
03:25:38 How do we choose 'K'
03:27:37 When do we use KNN
03:48:31 Applications of Support Vector Machine
03:48:55 Why Support Vector Machine
03:50:34 What Support Vector Machine
03:54:54 Advantages of Support Vector Machine
04:13:06 What is Naive Bayes
04:17:45 Where is Naive Bayes used
04:54:48 Top 10 Application of Machine Learning
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#MachineLearning #CompleteMachineLearningCourse #MachineLearningForBeginners #MachineLearningTutorial #MachineLearningWithPython #LearnMachineLearning #MachineLearingBasics #MachineLearningAlgorithms #MachineLearningEngineer #MachineLearningEngineerSalary #MachineLearningEngineerSkills #SimplilearnMachineLearning #MachineLearningCourse
➡️ About Post Graduate Program In AI And Machine Learning
This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.
✅ Key Features
- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools
✅ Skills Covered
- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many More…
Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-Full-Course-9f-GarcDY58&utm_medium=Tutorials&utm_source=youtube
🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearning&utm_medium=Description&utm_source=youtube"

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{'title': 'Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial | Simplilearn', 'heatmap': [{'end': 3207.441, 'start': 2975.734, 'weight': 1}], 'summary': 'Provides a comprehensive machine learning course covering various algorithms, industry insights, applications in profit estimation, data analysis, color compression, decision trees, kinect application, iris flower analysis, text classification, traffic analysis, and job opportunities, emphasizing python implementation and achieving high model accuracies.', 'chapters': [{'end': 1318.419, 'segs': [{'end': 55.106, 'src': 'embed', 'start': 27.385, 'weight': 0, 'content': [{'end': 33.369, 'text': 'will teach you the various concepts of machine learning, including algorithms, and how you can practically implement them.', 'start': 27.385, 'duration': 5.984}, {'end': 42.722, 'text': 'First we will understand what is machine learning and then we will go through various algorithms like linear regression, logistic regression, k-means,', 'start': 34.42, 'duration': 8.302}, {'end': 49.104, 'text': 'clustering decision tree, random forest KNN algorithm, SVM and Naive Bayes classifier.', 'start': 42.722, 'duration': 6.382}, {'end': 55.106, 'text': 'You will not only learn how each of these algorithms work, but also implement them using Python.', 'start': 50.044, 'duration': 5.062}], 'summary': 'Learn machine learning concepts and algorithms, implement with python.', 'duration': 27.721, 'max_score': 27.385, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5827385.jpg'}, {'end': 117.01, 'src': 'embed', 'start': 71.709, 'weight': 1, 'content': [{'end': 78.215, 'text': 'And finally, we have collected 30 of the most important questions that you might face in a machine learning interview.', 'start': 71.709, 'duration': 6.506}, {'end': 80.697, 'text': 'Before we move ahead with the full course,', 'start': 78.475, 'duration': 2.222}, {'end': 85.941, 'text': 'let us look at a short animated video we have made so that you can understand the basics of machine learning.', 'start': 80.697, 'duration': 5.244}, {'end': 92.33, 'text': 'We know humans learn from their past experiences and machines follow instructions given by humans.', 'start': 86.368, 'duration': 5.962}, {'end': 99.832, 'text': 'But what if humans can train the machines to learn from their past data and do what humans can do, and much faster?', 'start': 93.55, 'duration': 6.282}, {'end': 101.773, 'text': "Well, that's called machine learning.", 'start': 100.132, 'duration': 1.641}, {'end': 103.573, 'text': "But it's a lot more than just learning.", 'start': 101.833, 'duration': 1.74}, {'end': 106.054, 'text': "It's also about understanding and reasoning.", 'start': 103.733, 'duration': 2.321}, {'end': 109.375, 'text': 'So today we will learn about the basics of machine learning.', 'start': 106.254, 'duration': 3.121}, {'end': 111.235, 'text': "So that's Paul.", 'start': 110.455, 'duration': 0.78}, {'end': 113.296, 'text': 'He loves listening to new songs.', 'start': 111.535, 'duration': 1.761}, {'end': 117.01, 'text': 'He either likes them or dislikes them.', 'start': 115.106, 'duration': 1.904}], 'summary': 'Collected 30 important machine learning interview questions and presented basics of machine learning in a short animated video.', 'duration': 45.301, 'max_score': 71.709, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5871709.jpg'}, {'end': 498.159, 'src': 'embed', 'start': 467.535, 'weight': 3, 'content': [{'end': 470.919, 'text': 'And yes, computers now have great computational powers.', 'start': 467.535, 'duration': 3.384}, {'end': 474.143, 'text': 'So there are a lot of applications of machine learning out there.', 'start': 471.159, 'duration': 2.984}, {'end': 479.967, 'text': "To name a few, machine learning is used in healthcare where diagnostics are predicted for doctor's review.", 'start': 474.403, 'duration': 5.564}, {'end': 491.494, 'text': 'The sentiment analysis that the tech giants are doing on social media is another interesting application of machine learning fraud detection in the finance sector and also to predict customer churn in the e-commerce sector.', 'start': 480.067, 'duration': 11.427}, {'end': 498.159, 'text': 'While booking a cab, you must have encountered surge pricing often where it says the fare of your trip has been updated.', 'start': 491.775, 'duration': 6.384}], 'summary': 'Machine learning is widely used in healthcare, finance, e-commerce, and tech giants for various applications, such as diagnostics prediction, sentiment analysis, fraud detection, and customer churn prediction.', 'duration': 30.624, 'max_score': 467.535, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY58467535.jpg'}, {'end': 688.127, 'src': 'embed', 'start': 659.97, 'weight': 4, 'content': [{'end': 666.794, 'text': 'the company reviewed and categorized hundreds of thousands of posts to train a machine learning model that detects different types of engagement bait.', 'start': 659.97, 'duration': 6.824}, {'end': 669.736, 'text': "so in this case we have, we're using facebook, but this is, of course,", 'start': 667.134, 'duration': 2.602}, {'end': 676.34, 'text': 'across all the different social media they have different tools are building and the facebook scroll gif will be replaced,', 'start': 669.736, 'duration': 6.604}, {'end': 678.101, 'text': 'kind of like a virus coming in.', 'start': 676.34, 'duration': 1.761}, {'end': 682.804, 'text': "there notices that there's a certain setup with facebook and it's able to replace it.", 'start': 678.101, 'duration': 4.703}, {'end': 688.127, 'text': 'and they have like vote baiting, react baiting, share baiting they have all these different.', 'start': 682.804, 'duration': 5.323}], 'summary': 'Company reviewed 1000s posts, trained ml model to detect engagement bait across social media.', 'duration': 28.157, 'max_score': 659.97, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY58659970.jpg'}, {'end': 1296.26, 'src': 'embed', 'start': 1267.782, 'weight': 5, 'content': [{'end': 1275.772, 'text': 'Reinforcement. learning is an important type of machine learning, where an agent learns how to behave in an environment by performing actions and seeing the result.', 'start': 1267.782, 'duration': 7.99}, {'end': 1278.513, 'text': 'We have here, in this case, a baby.', 'start': 1276.292, 'duration': 2.221}, {'end': 1285.216, 'text': "It's actually great that they used an infant for this slide because the reinforcement learning is very much in its infant stages.", 'start': 1278.833, 'duration': 6.383}, {'end': 1296.26, 'text': "But it's also probably the biggest machine learning demand out there right now or in the future it's going to be coming up over the next few years is reinforcement learning and how to make that work for us.", 'start': 1285.696, 'duration': 10.564}], 'summary': 'Reinforcement learning is in its early stages but has significant demand in machine learning.', 'duration': 28.478, 'max_score': 1267.782, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY581267782.jpg'}], 'start': 5.251, 'title': 'Machine learning fundamentals', 'summary': "Provides a comprehensive overview of a complete machine learning course, covering various algorithms including linear regression, logistic regression, k-means clustering, decision tree, random forest, knn, svm, naive bayes, and python implementation. it also offers insights from industry experts with over 10 years of experience and includes a collection of 30 important machine learning interview questions. additionally, it explains the basics of machine learning, introduces supervised and unsupervised learning, and discusses the applications and advancements in the field, as well as facebook's use of machine learning in combating engagement bait.", 'chapters': [{'end': 117.01, 'start': 5.251, 'title': 'Complete machine learning course overview', 'summary': 'Covers a complete machine learning course, including various algorithms like linear regression, logistic regression, k-means clustering, decision tree, random forest, knn algorithm, svm, naive bayes classifier, and python implementation, as well as popular applications of machine learning and guidance on becoming a machine learning engineer, presented by industry experts with over 10 years of experience, and includes a collection of 30 important machine learning interview questions.', 'duration': 111.759, 'highlights': ['The chapter covers various important concepts of machine learning, including algorithms like linear regression, logistic regression, k-means clustering, decision tree, random forest, KNN algorithm, SVM, Naive Bayes classifier, and their practical implementation using Python.', 'The training includes guidance on popular applications of machine learning and how to become a machine learning engineer.', 'Industry experts with over 10 years of experience in data science and machine learning, Richard and Mohan, will be leading the course.', 'The course collection includes 30 important questions that might be faced in a machine learning interview.', 'The basics of machine learning are explained, including the idea of training machines to learn from past data and perform tasks much faster than humans.']}, {'end': 352.949, 'start': 117.07, 'title': 'Machine learning basics', 'summary': "Explains paul's music preferences based on tempo and intensity, introduces the k-nearest neighbors algorithm, and provides an overview of supervised and unsupervised learning in machine learning, emphasizing the importance of labeled data for training models.", 'duration': 235.879, 'highlights': ["Paul's music preferences are determined by fast tempo and soaring intensity, while he dislikes songs with relaxed tempo and light intensity.", "The k-nearest neighbors algorithm is employed to predict Paul's song preferences based on past choices, with a demonstration of using majority votes for prediction.", 'Supervised learning uses labeled data to train the model, where the machine learns the features of an object and the associated labels, demonstrated through predicting the currency of coins based on their weight.', 'Unsupervised learning involves identifying patterns in data without labeled information, exemplified by clustering cricket players into batsmen and bowlers based on their performance data.']}, {'end': 624.252, 'start': 353.189, 'title': 'Introduction to machine learning', 'summary': 'Introduces the concepts of supervised, unsupervised, and reinforcement learning, and discusses the applications of machine learning in various sectors, emphasizing the abundance of data and computational capabilities as the driving force behind the advancement of machine learning.', 'duration': 271.063, 'highlights': ['The abundance of data and enhanced computational capabilities are the key factors driving the advancement of machine learning, with numerous applications in healthcare, finance, e-commerce, and transportation industries.', 'Reinforcement learning is exemplified through the process of providing feedback to a machine learning system, such as in the case of image recognition, where negative feedback prompts the system to improve its classification abilities.', "The distinction between supervised and unsupervised learning is illustrated through real-world scenarios, including Facebook's image recognition and Netflix's movie recommendations based on past choices.", "Machine learning's impact on various sectors is highlighted, including predictive diagnostics in healthcare, sentiment analysis on social media, fraud detection in finance, and surge pricing in transportation, showcasing its diverse applications and real-world significance."]}, {'end': 1318.419, 'start': 624.572, 'title': "Facebook's fight against engagement bait", 'summary': "Discusses facebook's battle against engagement bait, using machine learning to categorize and eliminate spam posts, and the impact of reinforcement learning in the future of machine learning.", 'duration': 693.847, 'highlights': ["Facebook's use of machine learning to detect and eliminate engagement bait spam posts Facebook reviewed and categorized hundreds of thousands of posts to train a machine learning model that detects different types of engagement bait, leading to a more enjoyable user experience.", 'Impact of reinforcement learning in the future of machine learning Reinforcement learning, though still in its infant stages, is expected to play a crucial role in the future of machine learning, enabling agents to learn how to behave in an environment by performing actions and observing the results.', 'Explanation of supervised, unsupervised, and reinforcement learning The chapter provides a comprehensive explanation of supervised learning, where machines classify and predict based on labeled data, unsupervised learning, which finds hidden patterns in unlabeled data, and reinforcement learning, which involves learning from action and result feedback.']}], 'duration': 1313.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY585251.jpg', 'highlights': ['The chapter covers various important concepts of machine learning, including algorithms like linear regression, logistic regression, k-means clustering, decision tree, random forest, KNN algorithm, SVM, Naive Bayes classifier, and their practical implementation using Python.', 'The course collection includes 30 important questions that might be faced in a machine learning interview.', 'The basics of machine learning are explained, including the idea of training machines to learn from past data and perform tasks much faster than humans.', 'The abundance of data and enhanced computational capabilities are the key factors driving the advancement of machine learning, with numerous applications in healthcare, finance, e-commerce, and transportation industries.', "Facebook's use of machine learning to detect and eliminate engagement bait spam posts Facebook reviewed and categorized hundreds of thousands of posts to train a machine learning model that detects different types of engagement bait, leading to a more enjoyable user experience.", 'Reinforcement learning, though still in its infant stages, is expected to play a crucial role in the future of machine learning, enabling agents to learn how to behave in an environment by performing actions and observing the results.']}, {'end': 2238.89, 'segs': [{'end': 1368.412, 'src': 'embed', 'start': 1318.459, 'weight': 0, 'content': [{'end': 1321.682, 'text': "They went a different direction and now the baby's happy and laughing and playing.", 'start': 1318.459, 'duration': 3.223}, {'end': 1327.645, 'text': "Reinforcement learning is very easy to understand because that's how, as humans, that's one of the ways we learn.", 'start': 1322.122, 'duration': 5.523}, {'end': 1331.688, 'text': "We learn whether it is you burn yourself on the stove, don't do that anymore.", 'start': 1327.765, 'duration': 3.923}, {'end': 1332.628, 'text': "Don't touch the stove.", 'start': 1331.748, 'duration': 0.88}, {'end': 1338.992, 'text': 'In the big picture, being able to have a machine learning program or an AI be able to do this is huge,', 'start': 1333.088, 'duration': 5.904}, {'end': 1342.014, 'text': "because now we're starting to learn how to learn.", 'start': 1338.992, 'duration': 3.022}, {'end': 1345.756, 'text': "That's a big jump in the world of computer and machine learning.", 'start': 1342.294, 'duration': 3.462}, {'end': 1350.98, 'text': "And we're going to go back and just kind of go back over supervised versus unsupervised learning.", 'start': 1346.036, 'duration': 4.944}, {'end': 1355.783, 'text': "Understanding this is huge because this is going to come up in any project you're working on.", 'start': 1351.26, 'duration': 4.523}, {'end': 1359.585, 'text': 'We have in supervised learning, we have labeled data.', 'start': 1356.343, 'duration': 3.242}, {'end': 1361.227, 'text': 'We have direct feedback.', 'start': 1359.866, 'duration': 1.361}, {'end': 1364.429, 'text': "So someone's already gone in there and said, yes, that's a triangle.", 'start': 1361.547, 'duration': 2.882}, {'end': 1365.73, 'text': "No, that's not a triangle.", 'start': 1364.629, 'duration': 1.101}, {'end': 1367.211, 'text': 'And then you predict an outcome.', 'start': 1366.01, 'duration': 1.201}, {'end': 1368.412, 'text': 'So you have a nice prediction.', 'start': 1367.371, 'duration': 1.041}], 'summary': 'Reinforcement learning is crucial in machine learning, with supervised learning involving labeled data and direct feedback.', 'duration': 49.953, 'max_score': 1318.459, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY581318459.jpg'}, {'end': 1435.596, 'src': 'embed', 'start': 1408.116, 'weight': 3, 'content': [{'end': 1412.979, 'text': "now you can take that label data and program something to predict what's in the picture,", 'start': 1408.116, 'duration': 4.863}, {'end': 1418.703, 'text': 'so you can see how they go back and forth and you can start connecting all these different tools together to make a bigger picture.', 'start': 1412.979, 'duration': 5.724}, {'end': 1422.29, 'text': "Let's look at an example of a common use for linear regression.", 'start': 1418.988, 'duration': 3.302}, {'end': 1424.371, 'text': 'Profit estimation of a company.', 'start': 1422.55, 'duration': 1.821}, {'end': 1429.093, 'text': 'If I was going to invest in a company, I would like to know how much money I could expect to make.', 'start': 1424.731, 'duration': 4.362}, {'end': 1435.596, 'text': "So we'll take a look at a venture capitalist firm and try to understand which companies they should invest in.", 'start': 1429.473, 'duration': 6.123}], 'summary': 'Using labeled data to predict image content and estimate company profits for investment decisions.', 'duration': 27.48, 'max_score': 1408.116, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY581408116.jpg'}, {'end': 1505.111, 'src': 'embed', 'start': 1481.73, 'weight': 4, 'content': [{'end': 1490.739, 'text': "So we take our R&D and we're plotting the profit based on the R&D expenditure how much money they put into the research and development and then we look at the profit that goes with that.", 'start': 1481.73, 'duration': 9.009}, {'end': 1493.602, 'text': 'We can predict a line to estimate the profit.', 'start': 1491.06, 'duration': 2.542}, {'end': 1495.324, 'text': 'So we draw a line right through the data.', 'start': 1493.802, 'duration': 1.522}, {'end': 1500.99, 'text': "When you look at that, you can see how much they invest in the R&D is a good marker as to how much profit they're going to have.", 'start': 1495.564, 'duration': 5.426}, {'end': 1505.111, 'text': 'We can also note that companies spending more on R&D make good profit.', 'start': 1501.23, 'duration': 3.881}], 'summary': 'Analyzing r&d spending helps predict and increase profits for companies.', 'duration': 23.381, 'max_score': 1481.73, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY581481730.jpg'}, {'end': 1574.575, 'src': 'embed', 'start': 1534.15, 'weight': 5, 'content': [{'end': 1537.294, 'text': 'In our example, rainfall is the independent variable.', 'start': 1534.15, 'duration': 3.144}, {'end': 1543.36, 'text': "This is a wonderful example because you can easily see that we can't control the rain, but the rain does control the crop.", 'start': 1537.374, 'duration': 5.986}, {'end': 1547.244, 'text': 'So we talk about the independent variable controlling the dependent variable.', 'start': 1543.78, 'duration': 3.464}, {'end': 1554.611, 'text': "Let's define dependent variable as a variable whose value change when there is any manipulation of the values of the independent variables.", 'start': 1547.484, 'duration': 7.127}, {'end': 1556.373, 'text': 'It is often denoted as Y.', 'start': 1554.972, 'duration': 1.401}, {'end': 1561.899, 'text': 'And you can see here our crop yield is dependent variable and it is dependent on the amount of rainfall received.', 'start': 1556.373, 'duration': 5.526}, {'end': 1565.173, 'text': "Now that we've taken a look at a real life example,", 'start': 1562.372, 'duration': 2.801}, {'end': 1572.115, 'text': "let's go a little bit into the theory and some definitions on machine learning and see how that fits together with linear regression.", 'start': 1565.173, 'duration': 6.942}, {'end': 1574.575, 'text': 'Numerical and categorical values.', 'start': 1572.355, 'duration': 2.22}], 'summary': 'Rainfall is the independent variable controlling crop yield.', 'duration': 40.425, 'max_score': 1534.15, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY581534150.jpg'}, {'end': 1661.9, 'src': 'embed', 'start': 1620.868, 'weight': 6, 'content': [{'end': 1625.872, 'text': 'Housing sales, to estimate the number of houses a builder would sell and what price in the coming months.', 'start': 1620.868, 'duration': 5.004}, {'end': 1633.438, 'text': 'Score predictions, cricket fever, to predict the number of runs a player would score in the coming matches based on the previous performance.', 'start': 1626.492, 'duration': 6.946}, {'end': 1638.342, 'text': "I'm sure you can figure out other applications you could use linear regression for.", 'start': 1634.039, 'duration': 4.303}, {'end': 1643.467, 'text': "So let's jump in and let's understand linear regression and dig into the theory.", 'start': 1638.543, 'duration': 4.924}, {'end': 1645.669, 'text': 'Understanding linear regression.', 'start': 1644.127, 'duration': 1.542}, {'end': 1655.256, 'text': 'Linear regression is the statistical model used to predict the relationship between independent and dependent variables by examining two factors.', 'start': 1646.329, 'duration': 8.927}, {'end': 1661.9, 'text': 'The first important one is which variables in particular are significant predictors of the outcome variable.', 'start': 1655.936, 'duration': 5.964}], 'summary': 'Linear regression predicts housing sales and cricket scores.', 'duration': 41.032, 'max_score': 1620.868, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY581620868.jpg'}, {'end': 1710.851, 'src': 'embed', 'start': 1683.916, 'weight': 8, 'content': [{'end': 1694.644, 'text': 'The simplest form of a simple linear regression equation with one dependent and one independent variable is represented by y equals m times x plus c.', 'start': 1683.916, 'duration': 10.728}, {'end': 1697.366, 'text': 'And if you look at our model here, we plotted two points on here.', 'start': 1694.644, 'duration': 2.722}, {'end': 1701.549, 'text': 'X1 and Y1, X2 and Y2.', 'start': 1698.048, 'duration': 3.501}, {'end': 1708.45, 'text': 'Y being the dependent variable, remember that from before, and X being the independent variable.', 'start': 1702.349, 'duration': 6.101}, {'end': 1710.851, 'text': 'So Y depends on whatever X is.', 'start': 1708.83, 'duration': 2.021}], 'summary': 'Simple linear regression has 1 dependent variable (y) and 1 independent variable (x).', 'duration': 26.935, 'max_score': 1683.916, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY581683916.jpg'}, {'end': 2116.88, 'src': 'embed', 'start': 2083.117, 'weight': 11, 'content': [{'end': 2090.06, 'text': "That's where we get the 0.64 plus the 0.36 plus 1 all the way down until we have a summation equals 2.4.", 'start': 2083.117, 'duration': 6.943}, {'end': 2094.222, 'text': 'So the sum of squared errors for this regression line is 2.4.', 'start': 2090.06, 'duration': 4.162}, {'end': 2099.024, 'text': 'We check this error for each line and conclude the best fit line having the least e square value.', 'start': 2094.222, 'duration': 4.802}, {'end': 2102.046, 'text': 'In a nice graphical representation.', 'start': 2099.424, 'duration': 2.622}, {'end': 2111.27, 'text': 'we can see here where we keep moving this line through the data points to make sure the best fit line has the least square distance between the data points and the regression line.', 'start': 2102.046, 'duration': 9.224}, {'end': 2116.88, 'text': 'Now we only looked at the most commonly used formula for minimizing the distance.', 'start': 2111.755, 'duration': 5.125}], 'summary': 'Regression analysis found the best fit line with a sum of squared errors of 2.4.', 'duration': 33.763, 'max_score': 2083.117, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY582083117.jpg'}, {'end': 2225.851, 'src': 'embed', 'start': 2199.543, 'weight': 12, 'content': [{'end': 2209.534, 'text': 'So you have y equals m1 times x1 plus m2 times x2, so on all the way to m to the nth, x to the nth, and then you add your coefficient on there.', 'start': 2199.543, 'duration': 9.991}, {'end': 2212.017, 'text': 'Implementation of linear regression.', 'start': 2209.754, 'duration': 2.263}, {'end': 2213.679, 'text': 'Now we get into my favorite part.', 'start': 2212.157, 'duration': 1.522}, {'end': 2219.004, 'text': "Let's understand how multiple linear regression works by implementing it in Python.", 'start': 2214.079, 'duration': 4.925}, {'end': 2225.851, 'text': 'If you remember before, we were looking at a company and just based on its R&D, trying to figure out its profit.', 'start': 2219.244, 'duration': 6.607}], 'summary': 'Implementation of multiple linear regression in python for predicting company profit based on r&d.', 'duration': 26.308, 'max_score': 2199.543, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY582199543.jpg'}], 'start': 1318.459, 'title': 'Machine learning basics & applications', 'summary': 'Covers the basics of reinforcement learning, supervised and unsupervised learning, emphasizing the significance of machine learning in understanding and predicting outcomes, especially in profit estimation, along with discussions on predicting profit based on r&d expenditure and the relationship between rainfall and crop yield, and applications of linear regression in economic growth prediction, product price forecasting, housing sales estimation, and cricket score prediction, as well as the understanding of linear regression in python.', 'chapters': [{'end': 1424.371, 'start': 1318.459, 'title': 'Machine learning basics & applications', 'summary': 'Explains the basics of reinforcement learning, supervised and unsupervised learning, highlighting the significance of machine learning in understanding and predicting outcomes, especially in the context of profit estimation.', 'duration': 105.912, 'highlights': ['Reinforcement learning is significant as it emulates human learning processes, facilitating the understanding of machine learning. (Relevance: 5)', 'Supervised learning involves labeled data and direct feedback, enabling accurate predictions, while unsupervised learning involves finding hidden structure in unlabelled data. (Relevance: 4)', 'The ability to connect various machine learning tools to make a bigger picture is emphasized, demonstrating the practical application of learned concepts. (Relevance: 3)', 'The chapter illustrates the application of linear regression in profit estimation for a company, showcasing a practical use case of machine learning. (Relevance: 2)']}, {'end': 1600.109, 'start': 1424.731, 'title': 'Predicting profit and crop yield', 'summary': 'Discusses predicting profit based on r&d expenditure and the relationship between rainfall and crop yield, emphasizing the use of independent and dependent variables in machine learning, with a focus on numerical and categorical values.', 'duration': 175.378, 'highlights': ['The relationship between R&D expenditure and profit is discussed, with a focus on investing in companies that spend more on R&D to generate good profit. The chapter emphasizes the correlation between R&D expenditure and profit, suggesting that companies spending more on R&D tend to make good profit.', 'The concept of independent and dependent variables is explained using the example of rainfall as an independent variable controlling the crop yield as a dependent variable. The example of rainfall as an independent variable controlling the crop yield as a dependent variable is used to illustrate the concept of independent and dependent variables.', 'The distinction between numerical and categorical values in data is outlined, highlighting that numerical values represent a range of information while categorical values are specific items. The chapter distinguishes between numerical and categorical values, stating that numerical values represent a range of information while categorical values are specific items.']}, {'end': 1772.644, 'start': 1600.449, 'title': 'Applications of linear regression', 'summary': 'Discusses the applications of linear regression in economic growth prediction, product price forecasting, housing sales estimation, and cricket score prediction, as well as the key factors and equations involved in linear regression.', 'duration': 172.195, 'highlights': ['Linear regression is used for economic growth prediction, product price forecasting, housing sales estimation, and cricket score prediction, with the ability to determine significant predictors and regression line accuracy.', 'The statistical model of linear regression examines the relationship between independent and dependent variables by determining significant predictors and regression line accuracy.', 'The linear regression equation y = mx + c represents the relationship between the dependent and independent variables, with m denoting the slope and c representing the coefficient of the line.', 'The chapter illustrates the use of linear regression in predicting crop yield based on rainfall, highlighting the ability to estimate crop yield by drawing a line through the data and creating a mathematical formula.']}, {'end': 2238.89, 'start': 1773.064, 'title': 'Understanding linear regression in python', 'summary': 'Covers the math behind linear regression, including the intuition behind the regression line, computing the slope and coefficient, predicting values of y for corresponding x, minimizing the sum of squares of errors, and introduction to multiple linear regression.', 'duration': 465.826, 'highlights': ['The chapter covers the math behind linear regression, including the intuition behind the regression line, computing the slope and coefficient, predicting values of y for corresponding x, minimizing the sum of squares of errors, and introduction to multiple linear regression. It provides an overview of the key concepts including the intuition behind the regression line, computing the slope and coefficient, predicting values of y for corresponding x, minimizing the sum of squares of errors, and an introduction to multiple linear regression.', 'The best fit line should have the least sum of squares of these errors, also known as e-square. Emphasizes the importance of minimizing the sum of squares of errors to obtain the best fit line for linear regression.', "Let's understand how multiple linear regression works by implementing it in Python. Introduces the implementation of multiple linear regression in Python, expanding from simple linear regression to handling multiple variables and their respective slopes.", "We're only going to do five rows, because if we did like the rainfall with hundreds of points of data, that would be very hard to see what's going on with the mathematics. Highlights the use of a small dataset for better visualization and understanding of the mathematics behind linear regression."]}], 'duration': 920.431, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY581318459.jpg', 'highlights': ['Reinforcement learning emulates human learning processes, facilitating the understanding of machine learning.', 'Supervised learning involves labeled data and direct feedback, enabling accurate predictions.', 'The ability to connect various machine learning tools to make a bigger picture is emphasized.', 'The chapter illustrates the application of linear regression in profit estimation for a company.', 'The relationship between R&D expenditure and profit is discussed, emphasizing the correlation between the two.', 'The concept of independent and dependent variables is explained using the example of rainfall and crop yield.', 'Linear regression is used for economic growth prediction, product price forecasting, housing sales estimation, and cricket score prediction.', 'The statistical model of linear regression examines the relationship between independent and dependent variables.', 'The linear regression equation y = mx + c represents the relationship between the dependent and independent variables.', 'The chapter illustrates the use of linear regression in predicting crop yield based on rainfall.', 'The chapter covers the math behind linear regression, including the intuition behind the regression line, computing the slope and coefficient, predicting values of y for corresponding x, minimizing the sum of squares of errors, and introduction to multiple linear regression.', 'The best fit line should have the least sum of squares of these errors, also known as e-square.', "Let's understand how multiple linear regression works by implementing it in Python.", 'Highlights the use of a small dataset for better visualization and understanding of the mathematics behind linear regression.']}, {'end': 3462.503, 'segs': [{'end': 2330.149, 'src': 'embed', 'start': 2292.864, 'weight': 0, 'content': [{'end': 2296.687, 'text': 'And then I want you to skip one line and look at import pandas as pd.', 'start': 2292.864, 'duration': 3.823}, {'end': 2300.249, 'text': 'These are very common tools that you need with most of your linear regression.', 'start': 2296.967, 'duration': 3.282}, {'end': 2307.554, 'text': 'The numpy, which stands for number Python, is usually denoted as np, and you have to almost have that for your sklearn toolbox.', 'start': 2300.549, 'duration': 7.005}, {'end': 2309.235, 'text': 'You always import that right off the beginning.', 'start': 2307.594, 'duration': 1.641}, {'end': 2313.137, 'text': "Pandas, although you don't have to have it for your sklearn libraries.", 'start': 2309.395, 'duration': 3.742}, {'end': 2319.422, 'text': 'it does such a wonderful job of importing data, setting it up into a data frame so we can manipulate it rather easily,', 'start': 2313.137, 'duration': 6.285}, {'end': 2321.743, 'text': 'and it has a lot of tools also in addition to that.', 'start': 2319.422, 'duration': 2.321}, {'end': 2325.426, 'text': "So we usually like to use the pandas when we can, and I'll show you what that looks like.", 'start': 2321.923, 'duration': 3.503}, {'end': 2330.149, 'text': 'The other three lines are for us to get a visual of this data and take a look at it.', 'start': 2325.746, 'duration': 4.403}], 'summary': 'Common tools for linear regression: numpy (np) and pandas for data manipulation and visualization.', 'duration': 37.285, 'max_score': 2292.864, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY582292864.jpg'}, {'end': 2408.656, 'src': 'embed', 'start': 2383.243, 'weight': 1, 'content': [{'end': 2388.608, 'text': 'The next step is load the data set and extract independent and dependent variables.', 'start': 2383.243, 'duration': 5.365}, {'end': 2398.37, 'text': "Now, Here in the slide you'll see companies equals pd.read.csv and it has a long line there with the file at the end, 1000companies.csv.", 'start': 2388.989, 'duration': 9.381}, {'end': 2401.592, 'text': "You're going to have to change this to fit whatever setup you have.", 'start': 2398.53, 'duration': 3.062}, {'end': 2404.395, 'text': 'And the file itself you can request.', 'start': 2402.073, 'duration': 2.322}, {'end': 2408.656, 'text': 'Just go down to the commentary below this video and put a note in there,', 'start': 2404.875, 'duration': 3.781}], 'summary': 'The next step is to load the data set, extract variables, and adjust the file setup.', 'duration': 25.413, 'max_score': 2383.243, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY582383243.jpg'}, {'end': 2735.434, 'src': 'embed', 'start': 2704.463, 'weight': 3, 'content': [{'end': 2708.805, 'text': "Now that we've taken a look at the visualization of this data, we're going to move on to the next step.", 'start': 2704.463, 'duration': 4.342}, {'end': 2714.569, 'text': 'Instead of just having a pretty picture, we need to generate some hard data, some hard values.', 'start': 2709.086, 'duration': 5.483}, {'end': 2716.35, 'text': "So let's see what that looks like.", 'start': 2715.009, 'duration': 1.341}, {'end': 2719.791, 'text': "We're going to set up our linear regression model in two steps.", 'start': 2716.59, 'duration': 3.201}, {'end': 2724.272, 'text': 'The first one is we need to prepare some of our data so it fits correctly.', 'start': 2720.111, 'duration': 4.161}, {'end': 2727.052, 'text': "And let's go ahead and paste this code into our Jupyter notebook.", 'start': 2724.512, 'duration': 2.54}, {'end': 2735.434, 'text': "And what we're bringing in is we're going to bring in the sklearn preprocessing where we're going to import the label encoder and the one hot encoder.", 'start': 2727.272, 'duration': 8.162}], 'summary': 'Moving from visualization to generating hard data using linear regression model with sklearn preprocessing.', 'duration': 30.971, 'max_score': 2704.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY582704463.jpg'}, {'end': 3207.441, 'src': 'heatmap', 'start': 2975.734, 'weight': 1, 'content': [{'end': 2978.135, 'text': "That's what we're going to shoot for in this tutorial.", 'start': 2975.734, 'duration': 2.401}, {'end': 2983.357, 'text': 'The next part, train test split, we take X and we take Y.', 'start': 2978.275, 'duration': 5.082}, {'end': 2984.417, 'text': "We've already created those.", 'start': 2983.357, 'duration': 1.06}, {'end': 2989.099, 'text': 'X has the columns with the data in it and Y has a column with profit in it.', 'start': 2984.697, 'duration': 4.402}, {'end': 2992.84, 'text': "And then we're going to set the test size equals 0.2.", 'start': 2989.279, 'duration': 3.561}, {'end': 2995.261, 'text': 'That basically means 20%.', 'start': 2992.84, 'duration': 2.421}, {'end': 2997.261, 'text': 'So 20% of the rows are going to be tested.', 'start': 2995.261, 'duration': 2}, {'end': 2998.882, 'text': "We're going to put them off to the side.", 'start': 2997.281, 'duration': 1.601}, {'end': 3005.764, 'text': "So since we're using a thousand lines of data, that means that 200 of those lines we're going to hold off to the side to test for later.", 'start': 2999.022, 'duration': 6.742}, {'end': 3007.644, 'text': 'And then the random state equals zero.', 'start': 3006.024, 'duration': 1.62}, {'end': 3011.306, 'text': "We're going to randomize which ones it picks to hold off to the side.", 'start': 3007.705, 'duration': 3.601}, {'end': 3012.646, 'text': "We'll go ahead and run this.", 'start': 3011.566, 'duration': 1.08}, {'end': 3015.547, 'text': "It's not overly exciting because it's setting up our variables.", 'start': 3013.046, 'duration': 2.501}, {'end': 3019.308, 'text': 'But the next step is, the next step we actually create our linear regression model.', 'start': 3015.867, 'duration': 3.441}, {'end': 3025.974, 'text': "Now that we got to the linear regression model, we get that next piece of the puzzle, let's go ahead and put that code in there and walk through it.", 'start': 3019.608, 'duration': 6.366}, {'end': 3026.914, 'text': 'So here we go.', 'start': 3026.334, 'duration': 0.58}, {'end': 3032.639, 'text': "we're going to paste it in there and let's go ahead, and since this is a shorter line of code, let's zoom up there so we can get a good look.", 'start': 3026.914, 'duration': 5.725}, {'end': 3038.324, 'text': "And we have from the sklearn.linear underscore model, we're going to import linear regression.", 'start': 3032.9, 'duration': 5.424}, {'end': 3044.51, 'text': "Now I don't know if you recall from earlier, when we were doing all the math, let's go ahead and flip back there and take a look at that.", 'start': 3038.545, 'duration': 5.965}, {'end': 3049.262, 'text': 'Do you remember this, where we had this long formula on the bottom and we were doing all this sumization?', 'start': 3044.838, 'duration': 4.424}, {'end': 3053.526, 'text': 'And then we also looked at setting it up with the different lines.', 'start': 3049.562, 'duration': 3.964}, {'end': 3059.351, 'text': "And then we also looked all the way down to multiple linear regression where we're adding all those formulas together.", 'start': 3053.806, 'duration': 5.545}, {'end': 3062.393, 'text': 'All of that is wrapped up in this one section.', 'start': 3059.711, 'duration': 2.682}, {'end': 3065.917, 'text': "So what's going on here is I'm going to create a variable called regressor.", 'start': 3062.494, 'duration': 3.423}, {'end': 3068.599, 'text': 'And the regressor equals the linear regression.', 'start': 3066.237, 'duration': 2.362}, {'end': 3071.902, 'text': "That's a linear regression model that has all that math built in.", 'start': 3068.699, 'duration': 3.203}, {'end': 3075.223, 'text': "So we don't have to have it all memorized or have to compute it individually.", 'start': 3072.321, 'duration': 2.902}, {'end': 3077.204, 'text': 'And then we do the regressor.fit.', 'start': 3075.423, 'duration': 1.781}, {'end': 3085.59, 'text': "In this case, we do X train and Y train because we're using the training data, X being the data in and Y being profit, what we're looking at.", 'start': 3077.644, 'duration': 7.946}, {'end': 3087.511, 'text': 'And this does all that math for us.', 'start': 3085.87, 'duration': 1.641}, {'end': 3091.874, 'text': "So within one click and one line, we've created the whole linear regression model.", 'start': 3087.831, 'duration': 4.043}, {'end': 3094.756, 'text': 'And we fit the data to the linear regression model.', 'start': 3092.154, 'duration': 2.602}, {'end': 3099.318, 'text': 'And you can see that when I run the regressor It gives an output linear regression.', 'start': 3094.916, 'duration': 4.402}, {'end': 3101.259, 'text': 'it says copy x equals true.', 'start': 3099.318, 'duration': 1.941}, {'end': 3102.759, 'text': 'fit intercept equals true.', 'start': 3101.259, 'duration': 1.5}, {'end': 3103.859, 'text': 'in jobs equals one.', 'start': 3102.759, 'duration': 1.1}, {'end': 3104.82, 'text': 'normalize equals false.', 'start': 3103.859, 'duration': 0.961}, {'end': 3109.241, 'text': "It's just giving you some general information on what's going on with that regressor model.", 'start': 3105.18, 'duration': 4.061}, {'end': 3113.323, 'text': "Now that we've created our linear regression model, let's go ahead and use it.", 'start': 3109.522, 'duration': 3.801}, {'end': 3116.399, 'text': 'And if you remember, we kept a bunch of data aside.', 'start': 3113.837, 'duration': 2.562}, {'end': 3121.443, 'text': "So we're going to do a Y predict variable, and we're going to put in the X test.", 'start': 3116.779, 'duration': 4.664}, {'end': 3123.084, 'text': "And let's see what that looks like.", 'start': 3121.823, 'duration': 1.261}, {'end': 3125.646, 'text': 'Scroll up a little bit, paste that in here.', 'start': 3123.484, 'duration': 2.162}, {'end': 3127.827, 'text': 'Predicting the test set results.', 'start': 3125.906, 'duration': 1.921}, {'end': 3135.493, 'text': 'So here we have Y predict equals regressor.predict, X test going in, and this gives us Y predict.', 'start': 3128.308, 'duration': 7.185}, {'end': 3139.536, 'text': "Now because I'm in Jupyter inline, I can just put the variable up there.", 'start': 3135.873, 'duration': 3.663}, {'end': 3142.807, 'text': "And when I hit the Run button, it'll print that array out.", 'start': 3139.946, 'duration': 2.861}, {'end': 3146.347, 'text': 'I could have just as easily done print ypredict.', 'start': 3143.167, 'duration': 3.18}, {'end': 3152.749, 'text': "So if you're in a different IDE that's not an inline setup like the Jupyter Notebook, you can do it this way, print ypredict.", 'start': 3146.687, 'duration': 6.062}, {'end': 3159.43, 'text': "And you'll see that for the 200 different test variables we kept off to the side, it's going to produce 200 answers.", 'start': 3153.049, 'duration': 6.381}, {'end': 3162.351, 'text': 'This is what it says the profit are for those 200 predictions.', 'start': 3159.69, 'duration': 2.661}, {'end': 3163.912, 'text': "But let's don't stop there.", 'start': 3162.571, 'duration': 1.341}, {'end': 3166.334, 'text': "Let's keep going and take a couple look.", 'start': 3164.212, 'duration': 2.122}, {'end': 3171.037, 'text': "We're going to take just a short detail here and calculating the coefficients and the intercepts.", 'start': 3166.394, 'duration': 4.643}, {'end': 3174.579, 'text': "This gives us a quick flash at what's going on behind the line.", 'start': 3171.377, 'duration': 3.202}, {'end': 3179.703, 'text': "We're going to take a short detour here and we're going to be calculating the coefficient and intercepts.", 'start': 3174.819, 'duration': 4.884}, {'end': 3181.304, 'text': 'So you can see what those look like.', 'start': 3179.963, 'duration': 1.341}, {'end': 3186.847, 'text': "What's really nice about our regressor we created is it already has a coefficient for us.", 'start': 3181.804, 'duration': 5.043}, {'end': 3191.01, 'text': 'We can simply just print regressor.coefficient underscore.', 'start': 3187.088, 'duration': 3.922}, {'end': 3193.632, 'text': "When I run this, you'll see our coefficients here.", 'start': 3191.21, 'duration': 2.422}, {'end': 3199.115, 'text': 'And if we can do the regressor coefficient, we can also do the regressor intercept.', 'start': 3193.852, 'duration': 5.263}, {'end': 3201.257, 'text': "Let's run that and take a look at that.", 'start': 3199.656, 'duration': 1.601}, {'end': 3203.798, 'text': 'This all came from the multiple regression model.', 'start': 3201.497, 'duration': 2.301}, {'end': 3207.441, 'text': "And we'll flip over so you can remember where this is going into and where it's coming from.", 'start': 3204.019, 'duration': 3.422}], 'summary': 'Using linear regression model to predict profit from training data with 20% held for testing.', 'duration': 231.707, 'max_score': 2975.734, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY582975734.jpg'}, {'end': 3026.914, 'src': 'embed', 'start': 2995.261, 'weight': 4, 'content': [{'end': 2997.261, 'text': 'So 20% of the rows are going to be tested.', 'start': 2995.261, 'duration': 2}, {'end': 2998.882, 'text': "We're going to put them off to the side.", 'start': 2997.281, 'duration': 1.601}, {'end': 3005.764, 'text': "So since we're using a thousand lines of data, that means that 200 of those lines we're going to hold off to the side to test for later.", 'start': 2999.022, 'duration': 6.742}, {'end': 3007.644, 'text': 'And then the random state equals zero.', 'start': 3006.024, 'duration': 1.62}, {'end': 3011.306, 'text': "We're going to randomize which ones it picks to hold off to the side.", 'start': 3007.705, 'duration': 3.601}, {'end': 3012.646, 'text': "We'll go ahead and run this.", 'start': 3011.566, 'duration': 1.08}, {'end': 3015.547, 'text': "It's not overly exciting because it's setting up our variables.", 'start': 3013.046, 'duration': 2.501}, {'end': 3019.308, 'text': 'But the next step is, the next step we actually create our linear regression model.', 'start': 3015.867, 'duration': 3.441}, {'end': 3025.974, 'text': "Now that we got to the linear regression model, we get that next piece of the puzzle, let's go ahead and put that code in there and walk through it.", 'start': 3019.608, 'duration': 6.366}, {'end': 3026.914, 'text': 'So here we go.', 'start': 3026.334, 'duration': 0.58}], 'summary': '20% of the data rows will be held off for testing in a linear regression model using 1000 lines of data.', 'duration': 31.653, 'max_score': 2995.261, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY582995261.jpg'}, {'end': 3326.968, 'src': 'embed', 'start': 3297.424, 'weight': 6, 'content': [{'end': 3299.845, 'text': 'And when we go ahead and run this, we see we get a .', 'start': 3297.424, 'duration': 2.421}, {'end': 3300.806, 'text': "9352 That's the R2 score.", 'start': 3299.845, 'duration': 0.961}, {'end': 3310.912, 'text': "Now, it's not exactly a straight percentage, so it's not saying it's 93% correct, but you do want that in the upper 90s.", 'start': 3303.647, 'duration': 7.265}, {'end': 3316.136, 'text': 'Oh, and higher shows that this is a very valid prediction based on the R2 score.', 'start': 3311.473, 'duration': 4.663}, {'end': 3320.94, 'text': 'And if R squared value of 0.91 or 9.2 as we got on our model.', 'start': 3316.456, 'duration': 4.484}, {'end': 3323.183, 'text': 'Remember, it does have a random generation involved.', 'start': 3321, 'duration': 2.183}, {'end': 3326.968, 'text': 'This proves the model is a good model, which means success.', 'start': 3323.323, 'duration': 3.645}], 'summary': "Model's r2 score of 0.92 indicates a successful prediction with high validity.", 'duration': 29.544, 'max_score': 3297.424, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY583297424.jpg'}], 'start': 2239.111, 'title': 'Linear regression and data analysis', 'summary': 'Covers linear regression data analysis, pandas data manipulation, visualization and data preparation for linear regression, and training a linear regression model with an r squared value of 0.9352, achieving successful profit estimation.', 'chapters': [{'end': 2413.578, 'start': 2239.111, 'title': 'Linear regression data analysis', 'summary': 'Discusses importing essential libraries for linear regression, including numpy, pandas, matplotlib, and seaborn for data manipulation and visualization, and demonstrates loading the dataset and extracting independent and dependent variables for analysis.', 'duration': 174.467, 'highlights': ['The chapter discusses the process of importing essential libraries for linear regression, including numpy, pandas, matplotlib and seaborn, which are commonly used tools for data manipulation and visualization. It emphasizes the importance of these libraries for linear regression, highlighting numpy and pandas as essential tools for the sklearn toolbox, and explains the role of matplotlib and seaborn in visualizing the data.', "The chapter demonstrates the process of loading the dataset and extracting independent and dependent variables for analysis, using the 'pd.read.csv' function to read the '1000companies.csv' file. It provides guidance on accessing the dataset and adapting the code to fit individual setups, while also offering viewers the opportunity to request the file for personal use from Simply Learn.", "The chapter introduces the Anaconda Jupyter Notebook as the chosen IDE for the coding demonstration, highlighting its visual appeal and user-friendly interface for data analysis. It recommends using the Anaconda Jupyter Notebook as the preferred IDE for the demonstration, emphasizing its suitability for visualizing data and demonstrating the 'matplotlib inline' command for inline plotting."]}, {'end': 2555.206, 'start': 2413.938, 'title': 'Pandas data analysis', 'summary': 'Demonstrates how to use pandas to read and manipulate csv data, accessing specific rows and columns, and explains the advantages of pandas for data analysis.', 'duration': 141.268, 'highlights': ["Pandas allows for easy reading and manipulation of CSV data, using notation to access specific rows and columns, such as 'take every row except for the last column', and easily extracting data from the dataset.", 'The chapter showcases the use of companies.head to display the first five rows of data and illustrates how pandas organizes the data similar to an Excel spreadsheet, with rows and columns starting from 0.', 'The advantages of using pandas for data analysis are highlighted, emphasizing its ability to recognize column names and easily interpret CSV data, compared to reading raw CSV files or using Excel for data understanding.']}, {'end': 3012.646, 'start': 2569.679, 'title': 'Visualizing and preparing data for linear regression', 'summary': 'Discusses visualizing data with seaborn and preparing it for linear regression, including transforming categorical data into numerical format and splitting the data into training and testing sets.', 'duration': 442.967, 'highlights': ['The chapter discusses visualizing data with Seaborn and preparing it for linear regression. Visualizing data, Preparing for linear regression', 'The chapter explains the process of transforming categorical data into numerical format for linear regression. Transforming categorical data to numerical format', 'The process of splitting the data into training and testing sets is detailed, with 20% of the rows being held for testing. Splitting data into training and testing sets, 20% testing data']}, {'end': 3462.503, 'start': 3013.046, 'title': 'Linear regression model training', 'summary': "Describes the process of creating and using a linear regression model, including importing the linear regression model, fitting the data, predicting test set results, calculating coefficients and intercepts, and evaluating the model's performance with an r squared value of 0.9352, ultimately successfully training the model for profit estimation.", 'duration': 449.457, 'highlights': ['A detailed walk-through of creating and using a linear regression model, including importing the model, fitting the data, and predicting test set results, is provided, contributing to a comprehensive understanding of the process.', "The process of calculating coefficients and intercepts for the linear regression model is explained, offering insights into the model's underlying mathematical operations and providing a deeper understanding of the model's functionality.", "The evaluation of the model's performance using an R squared value of 0.9352 demonstrates a high level of accuracy and validity, indicating successful training for profit estimation and model effectiveness.", 'The session concludes with the successful training of the linear regression model for profit estimation, setting the stage for further exploration of machine learning algorithms.', 'The introduction to logistic regression and its application in binary classification is presented as the next topic of discussion, signaling a seamless transition to exploring additional machine learning algorithms.']}], 'duration': 1223.392, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY582239111.jpg', 'highlights': ['The chapter emphasizes the importance of numpy and pandas as essential tools for the sklearn toolbox.', 'The chapter provides guidance on accessing the dataset and adapting the code to fit individual setups.', 'Pandas allows for easy reading and manipulation of CSV data, emphasizing its ability to recognize column names and easily interpret CSV data.', 'The chapter discusses visualizing data with Seaborn and preparing it for linear regression.', 'The process of splitting the data into training and testing sets is detailed, with 20% of the rows being held for testing.', 'A detailed walk-through of creating and using a linear regression model is provided, contributing to a comprehensive understanding of the process.', "The evaluation of the model's performance using an R squared value of 0.9352 demonstrates a high level of accuracy and validity, indicating successful training for profit estimation and model effectiveness."]}, {'end': 5171.402, 'segs': [{'end': 3583.856, 'src': 'embed', 'start': 3556.961, 'weight': 0, 'content': [{'end': 3562.905, 'text': "So an output of, let's say, 0.8 will mean that the car will break down.", 'start': 3556.961, 'duration': 5.944}, {'end': 3565.647, 'text': 'So that is considered as an output of 1.', 'start': 3562.925, 'duration': 2.722}, {'end': 3572.711, 'text': "And let's say an output of 0.29 is considered as 0, which means that the car will not break down.", 'start': 3565.647, 'duration': 7.064}, {'end': 3575.033, 'text': "So that's the way logistic regression works.", 'start': 3572.931, 'duration': 2.102}, {'end': 3583.856, 'text': "Now let's do a quick comparison between logistic regression and linear regression because they both have the term regression in them.", 'start': 3575.433, 'duration': 8.423}], 'summary': 'Logistic regression predicts car breakdown with 0.8 output; 0.29 means no breakdown. comparison with linear regression.', 'duration': 26.895, 'max_score': 3556.961, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY583556961.jpg'}, {'end': 3888.355, 'src': 'embed', 'start': 3863.708, 'weight': 2, 'content': [{'end': 3871.234, 'text': 'If we take the odds equation and take a log of both sides, then this would look somewhat like this,', 'start': 3863.708, 'duration': 7.526}, {'end': 3876.479, 'text': 'and the term logistic is actually derived from the fact that we are doing this.', 'start': 3871.234, 'duration': 5.245}, {'end': 3879.844, 'text': 'We take a log of px by 1 minus px.', 'start': 3876.56, 'duration': 3.284}, {'end': 3883.529, 'text': 'This is an extension of the calculation of odds that we have seen right?', 'start': 3880.084, 'duration': 3.445}, {'end': 3888.355, 'text': 'And that is equal to beta 0 plus beta 1x, which is the equation of the straight line.', 'start': 3883.829, 'duration': 4.526}], 'summary': 'Using the odds equation, the logistic term is derived from taking the log of px by 1 minus px, extending the calculation of odds to the equation of a straight line.', 'duration': 24.647, 'max_score': 3863.708, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY583863708.jpg'}, {'end': 3960.968, 'src': 'embed', 'start': 3935.474, 'weight': 1, 'content': [{'end': 3945.159, 'text': 'So linear regression is solved or used to solve regression problems and logistic regression is used to solve classification problems.', 'start': 3935.474, 'duration': 9.685}, {'end': 3953.623, 'text': 'So both are called regression, but linear regression is used for solving regression problems where we predict continuous values,', 'start': 3945.399, 'duration': 8.224}, {'end': 3960.968, 'text': 'whereas logistic regression is used for solving classification problems where we have to predict discrete values.', 'start': 3953.623, 'duration': 7.345}], 'summary': 'Linear regression for continuous values, logistic regression for discrete values.', 'duration': 25.494, 'max_score': 3935.474, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY583935474.jpg'}, {'end': 4058.073, 'src': 'embed', 'start': 4026.828, 'weight': 4, 'content': [{'end': 4029.41, 'text': 'But these are some examples of logistic regression.', 'start': 4026.828, 'duration': 2.582}, {'end': 4035.195, 'text': "So we want to find out whether it's going to rain or not, whether it's going to be sunny or not, whether it's going to snow or not.", 'start': 4029.53, 'duration': 5.665}, {'end': 4037.477, 'text': 'These are all logistic regression examples.', 'start': 4035.255, 'duration': 2.222}, {'end': 4039.218, 'text': 'A few more examples.', 'start': 4037.977, 'duration': 1.241}, {'end': 4040.84, 'text': 'Classification of objects.', 'start': 4039.439, 'duration': 1.401}, {'end': 4045.043, 'text': 'This is again another example of logistic regression.', 'start': 4041.08, 'duration': 3.963}, {'end': 4050.267, 'text': 'Now here of course one distinction is that these are multi-class classification.', 'start': 4045.223, 'duration': 5.044}, {'end': 4058.073, 'text': 'So logistic regression is not used in its original form but it is used in a slightly different form.', 'start': 4050.447, 'duration': 7.626}], 'summary': 'Logistic regression used for weather prediction and object classification.', 'duration': 31.245, 'max_score': 4026.828, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY584026828.jpg'}, {'end': 4255.346, 'src': 'embed', 'start': 4227.653, 'weight': 6, 'content': [{'end': 4232.616, 'text': 'In our example we get an accuracy of about 0.94 which is pretty good or 94% which is pretty good.', 'start': 4227.653, 'duration': 4.963}, {'end': 4236.938, 'text': 'Alright. so what is a confusion matrix?', 'start': 4234.637, 'duration': 2.301}, {'end': 4248.283, 'text': 'This is an example of a confusion matrix, and this is used for identifying the accuracy of a classification model, like a logistic regression model.', 'start': 4236.998, 'duration': 11.285}, {'end': 4255.346, 'text': 'So the most important part in a confusion matrix is that, first of all, as you can see, this is a matrix,', 'start': 4248.483, 'duration': 6.863}], 'summary': 'Accuracy of the model is 94% and confusion matrix identifies classification accuracy.', 'duration': 27.693, 'max_score': 4227.653, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY584227653.jpg'}, {'end': 4467.463, 'src': 'embed', 'start': 4439.257, 'weight': 5, 'content': [{'end': 4450.263, 'text': "In this particular demo, what we are going to do is train our model to recognize digits, which are the images which have digits from, let's say,", 'start': 4439.257, 'duration': 11.006}, {'end': 4453.936, 'text': '0 to 5 or 0 to 9..', 'start': 4450.263, 'duration': 3.673}, {'end': 4460.139, 'text': 'And then we will see how well it is trained and whether it is able to predict these numbers correctly or not.', 'start': 4453.936, 'duration': 6.203}, {'end': 4461.3, 'text': "So let's get started.", 'start': 4460.379, 'duration': 0.921}, {'end': 4467.463, 'text': 'So the first part is as usual, we are importing some libraries that are required.', 'start': 4461.44, 'duration': 6.023}], 'summary': 'Demonstration of training a model to recognize digits from 0 to 9 and evaluating its prediction accuracy.', 'duration': 28.206, 'max_score': 4439.257, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY584439257.jpg'}, {'end': 4645.827, 'src': 'embed', 'start': 4617.854, 'weight': 8, 'content': [{'end': 4621.296, 'text': 'In our case here, we are splitting in the form of 23 and 77.', 'start': 4617.854, 'duration': 3.442}, {'end': 4627.92, 'text': 'So when we say test size as 0.23, that means 23% of that entire data is used for testing and the remaining 77% is used for training.', 'start': 4621.296, 'duration': 6.624}, {'end': 4642.286, 'text': 'So there is a readily available function which is called train test split.', 'start': 4636.425, 'duration': 5.861}, {'end': 4645.827, 'text': "So we don't have to write any special code for the splitting.", 'start': 4642.346, 'duration': 3.481}], 'summary': 'Data split into 23% testing and 77% training using train_test_split function.', 'duration': 27.973, 'max_score': 4617.854, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY584617854.jpg'}, {'end': 5151.52, 'src': 'embed', 'start': 5121.909, 'weight': 7, 'content': [{'end': 5126.784, 'text': 'Okay, so this is the confusion matrix for logistic regression.', 'start': 5121.909, 'duration': 4.875}, {'end': 5137.773, 'text': "Alright, so now that we have seen the confusion matrix, let's take a quick sample and see how well the system has classified.", 'start': 5128.188, 'duration': 9.585}, {'end': 5141.415, 'text': 'And we will take a few examples of the data.', 'start': 5138.213, 'duration': 3.202}, {'end': 5145.357, 'text': 'So if we see here, we picked up randomly a few of them.', 'start': 5141.475, 'duration': 3.882}, {'end': 5150.819, 'text': 'So this is number four, which is the actual value and also the predicted value.', 'start': 5145.757, 'duration': 5.062}, {'end': 5151.52, 'text': 'Both are four.', 'start': 5150.859, 'duration': 0.661}], 'summary': "Examining logistic regression model's confusion matrix and sample data, revealing accurate classification for value four.", 'duration': 29.611, 'max_score': 5121.909, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY585121909.jpg'}], 'start': 3462.563, 'title': 'Logistic regression applications', 'summary': 'Covers logistic regression in predicting car breakdown probability, differences with linear regression, use cases, and live demos achieving 94% accuracy in digit and image recognition with confusion matrix visualization.', 'chapters': [{'end': 3556.961, 'start': 3462.563, 'title': 'Logistic regression and classification algorithm', 'summary': 'Discusses the application of logistic regression in determining the probability of car breakdown based on the number of years, with a threshold of 0.5 used to determine the outcome.', 'duration': 94.398, 'highlights': ['The logistic regression curve is used to determine the probability of car breakdown, with a threshold of 0.5 used to classify outcomes as 0 or 1.', 'As the number of years increases, there is almost a certainty that the car is going to break down, as shown in the graph.']}, {'end': 3915.287, 'start': 3556.961, 'title': 'Logistic regression vs linear regression', 'summary': 'Explains the differences between logistic regression and linear regression, clarifying their use cases, and the mathematical concepts behind logistic regression and linear regression, emphasizing the need for logistic regression in cases where the output is discrete, and the gradual process of probability calculation in logistic regression.', 'duration': 358.326, 'highlights': ['Logistic regression is used for cases where the output is discrete, such as yes or no, and involves calculating the odds for a particular event happening using the formula P/(1-P), with values ranging from 0 to infinity.', 'Linear regression is used for cases where the output is continuous, such as predicting real estate prices or salary hikes, and involves determining a continuous value using an algorithm for supervised learning.', "The equation of a straight line in logistic regression is represented as 'y = β0 + β1x', and the equation for calculating the probability 'px' is 'px = 1 / (1 + e^(-β0 + β1x))', demonstrating the mathematical concepts behind logistic regression.", "In cases where the data involves discrete outputs like yes or no, and the plotted straight line does not fit the data well, logistic regression provides a better approach by using the probability equation 'px = 1 / (1 + e^(-β0 + β1x))', resulting in a gradual process of probability calculation for predicting the discrete output."]}, {'end': 4518.986, 'start': 3916.067, 'title': 'Logistic regression overview', 'summary': 'Discusses the differences between linear and logistic regression, with logistic regression being used for classification problems and examples including weather prediction, object classification, and healthcare. additionally, it presents a live demo showcasing the application of logistic regression in predicting digits, achieving an accuracy of 94% using a confusion matrix.', 'duration': 602.919, 'highlights': ['The chapter discusses the differences between linear and logistic regression, with logistic regression being used for classification problems. Logistic regression is used for solving classification problems, where we have to predict discrete values, as opposed to linear regression which is used for solving regression problems to predict continuous values.', 'Examples of logistic regression applications include weather prediction, object classification, and healthcare. Logistic regression is utilized in weather prediction for discrete values (e.g., rain or not rain), object classification (including multi-class classification), and healthcare to predict the survival rate of a patient using multiple parameters.', "A live demo showcases the application of logistic regression in predicting digits, achieving an accuracy of 94% using a confusion matrix. The live demo demonstrates the training, testing, and deployment of a logistic regression model to predict digits, achieving a 94% accuracy using a confusion matrix to assess the model's performance."]}, {'end': 5171.402, 'start': 4519.066, 'title': 'Logistic regression model for image recognition', 'summary': 'Discusses the process of training and testing a logistic regression model for image recognition, achieving an accuracy of 94% and visualizing accuracy using a confusion matrix.', 'duration': 652.336, 'highlights': ["The logistic regression model achieved an accuracy of 94% during testing. The model's accuracy is quantified at 94%, indicating its effectiveness in recognizing and classifying images.", "The process of visualizing accuracy using a confusion matrix is demonstrated, emphasizing the importance of diagonal numbers for accuracy assessment. The confusion matrix is used to visually evaluate the model's accuracy, with a focus on diagonal numbers indicating correct predictions and the total sum providing percentage accuracy.", 'The split of training and test data set is explained, with 23% allocated for testing and 77% for training. The data is split into training and test sets, with 23% allocated for testing and 77% for training, enabling the model to be trained and evaluated effectively.']}], 'duration': 1708.839, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY583462563.jpg', 'highlights': ['Logistic regression curve determines car breakdown probability with 0.5 threshold.', 'Logistic regression used for discrete outputs, linear regression for continuous.', "Logistic regression equation 'px = 1 / (1 + e^(-β0 + β1x))' for probability calculation.", 'Logistic regression used for classification problems, linear regression for regression.', 'Logistic regression applications: weather prediction, object classification, healthcare.', 'Live demo achieves 94% accuracy in predicting digits using logistic regression.', 'Logistic regression model achieves 94% accuracy in recognizing and classifying images.', "Confusion matrix used to visually evaluate model's accuracy in logistic regression.", 'Data split into 23% testing and 77% training sets for logistic regression model.']}, {'end': 6760.596, 'segs': [{'end': 5274.222, 'src': 'embed', 'start': 5200.259, 'weight': 0, 'content': [{'end': 5207.042, 'text': 'which means objects that are similar in nature, similar in characteristics, need to be put together.', 'start': 5200.259, 'duration': 6.783}, {'end': 5210.464, 'text': "so that's what k-means clustering is all about.", 'start': 5207.042, 'duration': 3.422}, {'end': 5217.789, 'text': 'the term k is basically is a number, so we need to tell the system how many clusters we need to perform.', 'start': 5210.464, 'duration': 7.325}, {'end': 5220.092, 'text': 'so if k is equal to two, there will be two clusters.', 'start': 5217.789, 'duration': 2.303}, {'end': 5223.336, 'text': 'if k is equal to three, three clusters, and so on and so forth.', 'start': 5220.092, 'duration': 3.244}, {'end': 5225.278, 'text': "that's what the k stands for.", 'start': 5223.336, 'duration': 1.942}, {'end': 5231.265, 'text': 'and of course, there is a way of finding out what is the best or optimum value of k for a given data.', 'start': 5225.278, 'duration': 5.987}, {'end': 5232.446, 'text': 'we will look at that.', 'start': 5231.265, 'duration': 1.181}, {'end': 5234.489, 'text': 'so that is k means cluster.', 'start': 5232.446, 'duration': 2.043}, {'end': 5237.632, 'text': "so let's take an example.", 'start': 5235.109, 'duration': 2.523}, {'end': 5240.575, 'text': 'k-means clustering is used in many, many scenarios.', 'start': 5237.632, 'duration': 2.943}, {'end': 5244.499, 'text': "but let's take an example of cricket, the game of cricket.", 'start': 5240.575, 'duration': 3.924}, {'end': 5252.066, 'text': "let's say you received data of a lot of players from maybe all over the country or all over the world,", 'start': 5244.499, 'duration': 7.567}, {'end': 5263.711, 'text': 'and this data has information about the runs scored by the people or by the player and the wickets taken by the player and based on this information,', 'start': 5252.066, 'duration': 11.645}, {'end': 5269.837, 'text': 'we need to cluster this data into two clusters batsmen and bowlers.', 'start': 5263.711, 'duration': 6.126}, {'end': 5271.799, 'text': 'so this is an interesting example.', 'start': 5269.837, 'duration': 1.962}, {'end': 5274.222, 'text': "let's see how we can perform this.", 'start': 5271.799, 'duration': 2.423}], 'summary': 'K-means clustering groups similar objects into k clusters, e.g., batsmen and bowlers in cricket.', 'duration': 73.963, 'max_score': 5200.259, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY585200259.jpg'}, {'end': 5599.545, 'src': 'embed', 'start': 5569.294, 'weight': 4, 'content': [{'end': 5575.32, 'text': 'There are primarily two categories of clustering hierarchical clustering and then partitional clustering.', 'start': 5569.294, 'duration': 6.026}, {'end': 5584.489, 'text': 'And each of these categories are further subdivided into agglomerative and divisive clustering and k-means and fuzzy c-means clustering.', 'start': 5575.92, 'duration': 8.569}, {'end': 5588.573, 'text': "Let's take a quick look at what each of these types of clustering are.", 'start': 5585.049, 'duration': 3.524}, {'end': 5599.545, 'text': 'In hierarchical clustering, the clusters have a tree-like structure and hierarchical clustering is further divided into agglomerative and divisive.', 'start': 5589.511, 'duration': 10.034}], 'summary': 'Two types of clustering: hierarchical (agglomerative and divisive) and partitional (k-means and fuzzy c-means).', 'duration': 30.251, 'max_score': 5569.294, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY585569294.jpg'}, {'end': 5967.322, 'src': 'embed', 'start': 5924.678, 'weight': 3, 'content': [{'end': 5932.36, 'text': 'So, once we have the value of k, we specify that and then the system will assign that many centroids.', 'start': 5924.678, 'duration': 7.682}, {'end': 5936.902, 'text': 'So it picks randomly that to start with randomly that many points.', 'start': 5932.42, 'duration': 4.482}, {'end': 5941.225, 'text': 'that are considered to be the centroids of these clusters,', 'start': 5937.282, 'duration': 3.943}, {'end': 5954.053, 'text': 'and then it measures the distance of each of the data points from these centroids and assigns those points to the corresponding centroid from which the distance is minimum.', 'start': 5941.225, 'duration': 12.828}, {'end': 5964.48, 'text': 'so each data point will be assigned to the centroid which is closest to it and thereby we have k number of initial clusters.', 'start': 5954.053, 'duration': 10.427}, {'end': 5967.322, 'text': 'However, this is not the final clusters.', 'start': 5965.02, 'duration': 2.302}], 'summary': 'The k-means algorithm assigns data points to k initial clusters based on minimum distance from centroids.', 'duration': 42.644, 'max_score': 5924.678, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY585924678.jpg'}, {'end': 6146.828, 'src': 'embed', 'start': 6119.98, 'weight': 6, 'content': [{'end': 6128.364, 'text': 'so we have on the y-axis the within sum of squares or wss, And on the x-axis we have the number of clusters.', 'start': 6119.98, 'duration': 8.384}, {'end': 6137.986, 'text': 'So, as you can imagine, if you have k is equal to 1,, which means all the data points are in a single cluster, the within-SS value will be very high,', 'start': 6128.504, 'duration': 9.482}, {'end': 6140.287, 'text': 'because they are probably scattered all over.', 'start': 6137.986, 'duration': 2.301}, {'end': 6146.828, 'text': 'The moment you split it into two, there will be a drastic fall in the within-SS value.', 'start': 6140.587, 'duration': 6.241}], 'summary': 'K-means clustering reduces within-ss as clusters increase.', 'duration': 26.848, 'max_score': 6119.98, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY586119980.jpg'}, {'end': 6760.596, 'src': 'embed', 'start': 6735.519, 'weight': 9, 'content': [{'end': 6745.065, 'text': 'Walmart wants to open a chain of stores across the state of Florida and it wants to find the optimal store locations.', 'start': 6735.519, 'duration': 9.546}, {'end': 6752.59, 'text': 'Now, the issue here is if they open too many stores close to each other, obviously they will not make profit.', 'start': 6745.485, 'duration': 7.105}, {'end': 6758.235, 'text': 'If the stores are too far apart, then they will not have enough sales.', 'start': 6754.071, 'duration': 4.164}, {'end': 6760.596, 'text': 'So how do they optimize this?', 'start': 6758.895, 'duration': 1.701}], 'summary': 'Walmart aims to open stores in florida, optimizing locations for profit and sales.', 'duration': 25.077, 'max_score': 6735.519, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY586735519.jpg'}], 'start': 5171.422, 'title': 'K-means clustering in cricket', 'summary': "Illustrates the concept and application of k-means clustering in cricket, including clustering players into batsmen and bowlers based on runs and wickets, and explaining the iterative process and determination of the number of clusters with detailed examples. additionally, it covers the application of k-means clustering in scenarios like the game of cricket and walmart's store location optimization in florida.", 'chapters': [{'end': 5244.499, 'start': 5171.422, 'title': 'Logistic regression and k-means clustering', 'summary': 'Highlighted the concept of k-means clustering, an unsupervised learning algorithm for grouping similar data into clusters, and its application in scenarios like the game of cricket.', 'duration': 73.077, 'highlights': ['K-means clustering is an unsupervised learning algorithm used to group similar data into clusters, with the number of clusters (k) being a key parameter, and there are methods to determine the optimum value of k for a given dataset.', 'The process of k-means clustering involves putting objects with similar characteristics into clusters, and it is used in various scenarios, including the game of cricket.']}, {'end': 5874.172, 'start': 5244.499, 'title': 'Clustering players into batsmen and bowlers', 'summary': 'Discusses the application of k-means clustering to cluster cricket players into batsmen and bowlers based on runs scored and wickets taken, with k-means clustering explained in detail and the types of clustering and distance measures also covered.', 'duration': 629.673, 'highlights': ['The chapter discusses the application of k-means clustering to cluster cricket players into batsmen and bowlers based on runs scored and wickets taken, with k-means clustering explained in detail and the types of clustering and distance measures also covered.', 'K-means clustering is used to divide objects into a specified number of clusters, such as dividing cricket players into batsmen and bowlers based on their characteristics like runs and wickets.', 'The chapter explains the process of k-means clustering, including the allocation of centroids, distance measurement of data points from centroids, and the repositioning of centroids to determine the actual centroid of the clusters.', 'The types of clustering are hierarchical clustering (agglomerative and divisive) and partitional clustering, further divided into k-means clustering and fuzzy c-means clustering, with each type explained in detail.', 'The chapter also covers the distance measures supported by k-means clustering, including euclidean distance, squared euclidean distance, manhattan distance, and cosine distance, with each measure described and its application in clustering explained.']}, {'end': 6173.3, 'start': 5874.172, 'title': 'Understanding k-means clustering', 'summary': 'Explains the working of k-means clustering, including the determination of the number of clusters, assignment of centroids, iterative process of centroid movement, and the use of the elbow method to find the optimum number of clusters.', 'duration': 299.128, 'highlights': ['K-means clustering involves specifying the number of clusters, assigning centroids, measuring distances of data points from centroids, and reallocating points based on centroid movement.', 'The Elbow method is used to determine the optimum number of clusters by measuring the within sum of squares (WSS) for different values of k.', 'A diagrammatic representation of the WSS for different numbers of clusters helps visualize the optimum value of k based on the rate of decrease in WSS.', 'The process involves an iterative calculation of new centroid positions until convergence is achieved, indicated by centroids no longer moving and final allocation of data points to the closest centroid.']}, {'end': 6760.596, 'start': 6173.3, 'title': 'Understanding k-means clustering', 'summary': 'Explains the iterative process of k-means clustering, involving the random selection of centroids, assignment of data points to the nearest centroid, recalculation of centroids based on the mean position of data points, and iterative reallocation of data points until centroids stop changing, illustrated with the example of walmart finding optimal store locations in florida.', 'duration': 587.296, 'highlights': ['Iterative Process of K-means Clustering The chapter explains the iterative process of K-means clustering, involving the random selection of centroids, assignment of data points to the nearest centroid, recalculation of centroids based on the mean position of data points, and iterative reallocation of data points until centroids stop changing.', 'Random Selection of Centroids The process begins with the random selection of k cluster centers, which are initially named c1, c2 up to ck, serving as the initial centroids for the clustering process.', 'Assignment of Data Points to Centroids The distance of each data point from each centroid is calculated, and the data point is assigned to the centroid with the lowest distance, forming the initial assignment of data points to the closest centroids.', 'Recalculation of Centroids After the initial assignment, the actual centroid for each group is calculated based on the mean position of the data points, and the process of recalculating distances and potentially reallocating data points is repeated until the centroids stop changing, signifying convergence.', 'Application to Walmart Store Locations The chapter illustrates the application of K-means clustering to the problem of finding optimal store locations for Walmart in Florida, highlighting the need to balance proximity and profitability when opening multiple stores.']}], 'duration': 1589.174, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY585171422.jpg', 'highlights': ['K-means clustering groups similar data into clusters, with methods to determine the optimum value of k.', 'K-means clustering is used in various scenarios, including the game of cricket.', 'The chapter discusses the application of k-means clustering to cluster cricket players into batsmen and bowlers based on runs scored and wickets taken.', 'The process of k-means clustering involves the allocation of centroids, distance measurement of data points from centroids, and the repositioning of centroids.', 'The chapter explains the types of clustering, including hierarchical clustering and partitional clustering, with each type explained in detail.', 'K-means clustering involves specifying the number of clusters, assigning centroids, measuring distances of data points from centroids, and reallocating points based on centroid movement.', 'The Elbow method is used to determine the optimum number of clusters by measuring the within sum of squares (WSS) for different values of k.', 'The iterative process of K-means clustering involves the random selection of centroids, assignment of data points to the nearest centroid, recalculation of centroids based on the mean position of data points, and iterative reallocation of data points until centroids stop changing.', 'The process begins with the random selection of k cluster centers, which are initially named c1, c2 up to ck, serving as the initial centroids for the clustering process.', 'The chapter illustrates the application of K-means clustering to the problem of finding optimal store locations for Walmart in Florida.']}, {'end': 8033.524, 'segs': [{'end': 6792.159, 'src': 'embed', 'start': 6761.117, 'weight': 0, 'content': [{'end': 6771.225, 'text': 'Now, for an organization like Walmart, which is an e-commerce giant, they already have the addresses of their customers in their database.', 'start': 6761.117, 'duration': 10.108}, {'end': 6780.412, 'text': 'So they can actually use this information or this data and use k-means clustering to find the optimal location.', 'start': 6771.525, 'duration': 8.887}, {'end': 6792.159, 'text': 'Now, before we go into the python notebook and show you the live code, i wanted to take you through very quickly a summary of the code in the slides,', 'start': 6780.893, 'duration': 11.266}], 'summary': 'Walmart uses k-means clustering to find optimal locations for customers.', 'duration': 31.042, 'max_score': 6761.117, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY586761117.jpg'}, {'end': 7267.455, 'src': 'embed', 'start': 7239.599, 'weight': 1, 'content': [{'end': 7250.405, 'text': 'So as explained in the slides, the first step that is done in case of k-means clustering is to randomly assign some centroids.', 'start': 7239.599, 'duration': 10.806}, {'end': 7259.596, 'text': 'so, as a first step, we randomly allocate a couple of centroids, which we call here we are calling as centers,', 'start': 7250.985, 'duration': 8.611}, {'end': 7267.455, 'text': 'and then we put this in a loop and we take it through an iterative process For each of the data points.', 'start': 7259.596, 'duration': 7.859}], 'summary': 'K-means clustering begins with random centroid allocation and iterative process for data points.', 'duration': 27.856, 'max_score': 7239.599, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY587239599.jpg'}, {'end': 7475.639, 'src': 'embed', 'start': 7446.254, 'weight': 2, 'content': [{'end': 7457.303, 'text': 'And next we will move on to see a couple of examples of how k-means clustering is used in maybe some real life scenarios or use cases.', 'start': 7446.254, 'duration': 11.049}, {'end': 7465.911, 'text': 'In the next example or demo, We are going to see how we can use k-means clustering to perform color compression.', 'start': 7457.624, 'duration': 8.287}, {'end': 7475.639, 'text': 'We will take a couple of images so there will be two examples and we will try to use k-means clustering to compress the colors.', 'start': 7466.491, 'duration': 9.148}], 'summary': 'Demonstrating k-means clustering in color compression for real-life scenarios.', 'duration': 29.385, 'max_score': 7446.254, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY587446254.jpg'}, {'end': 7922.508, 'src': 'embed', 'start': 7894.352, 'weight': 3, 'content': [{'end': 7902.358, 'text': 'Now we will reduce that to 16 colors using k-means clustering and we do the same process like before.', 'start': 7894.352, 'duration': 8.006}, {'end': 7910.343, 'text': 'We reshape it and then we cluster the colors to 16 and then we render the image once again.', 'start': 7902.378, 'duration': 7.965}, {'end': 7916.485, 'text': 'And we will see that the color, the quality of the image slightly deteriorates.', 'start': 7911.003, 'duration': 5.482}, {'end': 7922.508, 'text': 'As you can see here, this has much finer details in this which are probably missing here.', 'start': 7916.565, 'duration': 5.943}], 'summary': 'Using k-means clustering, we reduced the image to 16 colors, resulting in a slight deterioration in quality.', 'duration': 28.156, 'max_score': 7894.352, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY587894352.jpg'}], 'start': 6761.117, 'title': 'K-means clustering implementation and color compression', 'summary': "Discusses the implementation of k-means clustering with a focus on centroid convergence, color compression using k-means clustering to reduce an image's 16 million colors to 16, and the process of reducing color images to 16 colors while maintaining high-level information, with examples showing minimal information loss and slight deterioration in image quality.", 'chapters': [{'end': 7056.561, 'start': 6761.117, 'title': 'K-means clustering for walmart store locations', 'summary': 'Discusses how walmart can use k-means clustering to find optimal store locations in florida using customer addresses, demonstrating the process in a python notebook and emphasizing the creation of four distinct clusters.', 'duration': 295.444, 'highlights': ['Using k-means clustering, Walmart can determine the best store locations based on customer addresses in their database, as demonstrated in a Python notebook.', 'The chapter demonstrates the process of importing required libraries, loading data, and visually identifying distinct clusters through scatter plots, leading to the creation of four distinct clusters in Florida.', "The functionality of 'make blobs' in scikit-learn is highlighted as a useful tool to create test data with specified clusters, offering a visual representation of the data distribution.", 'The k-means functionality in Python is explained, emphasizing the simplicity of implementing k-means clustering by specifying the number of clusters and using the fit method to train the model.']}, {'end': 7357.59, 'start': 7056.621, 'title': 'K-means clustering implementation', 'summary': 'Explains the process of creating clusters using k-means algorithm and its implementation, with a focus on randomly assigning centroids, assigning data points to clusters, and convergence criteria, based on a sample data of 300 observations and 4 clusters.', 'duration': 300.969, 'highlights': ["The process involves randomly assigning centroids, then iteratively assigning data points to the closest centroids and updating the centroids' positions until convergence, with a sample data of 300 observations and 4 clusters.", 'The k-means algorithm involves calculating the distance of each data point from predefined centroids and assigning the data point to the closest centroid based on the lowest distance, using a standard function.', "The implementation includes a basic iterative process of assigning data points to centroids, calculating new centroids, and checking for convergence based on the positions of the centroids, with a convergence criteria of centroids' positions remaining unchanged."]}, {'end': 7797.845, 'start': 7357.59, 'title': 'K-means clustering: implementation and color compression', 'summary': "Discusses the implementation of k-means clustering with a focus on centroid convergence and showcases color compression using k-means clustering to reduce an image's 16 million colors to 16, preserving most of the image quality.", 'duration': 440.255, 'highlights': ["Color compression using k-means clustering reduces an image's 16 million colors to 16, preserving most of the image quality. The method of color compression using k-means clustering is demonstrated, reducing an image's 16 million colors to 16, with minimal loss of information.", "Centroid convergence is achieved by setting a change threshold of 0.0001, ensuring the algorithm's termination. Centroid convergence is determined by setting a change threshold of 0.0001, ensuring the algorithm's termination to prevent indefinite execution.", 'Mini batch k-means is utilized to process large image data by breaking it into smaller batches for efficient clustering. Mini batch k-means is employed to process large image data by breaking it into smaller batches, ensuring efficient clustering of millions of colors.']}, {'end': 8033.524, 'start': 7797.845, 'title': 'Color reduction using k-means clustering', 'summary': 'Demonstrates the process of reducing high definition color images to 16 colors using k-means clustering, which maintains high-level information while compromising finer color details for rendering on less sophisticated devices, with examples showing minimal information loss and slight deterioration in image quality.', 'duration': 235.679, 'highlights': ['The process of reducing high definition color images to 16 colors using k-means clustering maintains high-level information while compromising finer color details, allowing rendering on less sophisticated devices.', 'Examples show minimal information loss and slight deterioration in image quality when applying the newly created colors to the images.', 'The compromise of losing finer color details in the reduced 16-color images is offset by the advantage of rendering on less sophisticated devices.']}], 'duration': 1272.407, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY586761117.jpg', 'highlights': ['Using k-means clustering, Walmart can determine the best store locations based on customer addresses in their database, as demonstrated in a Python notebook.', "The process involves randomly assigning centroids, then iteratively assigning data points to the closest centroids and updating the centroids' positions until convergence, with a sample data of 300 observations and 4 clusters.", "Color compression using k-means clustering reduces an image's 16 million colors to 16, preserving most of the image quality. The method of color compression using k-means clustering is demonstrated, reducing an image's 16 million colors to 16, with minimal loss of information.", 'The process of reducing high definition color images to 16 colors using k-means clustering maintains high-level information while compromising finer color details, allowing rendering on less sophisticated devices.']}, {'end': 8863.397, 'segs': [{'end': 8079.23, 'src': 'embed', 'start': 8033.524, 'weight': 0, 'content': [{'end': 8040.53, 'text': "it doesn't look as rich as this one, but nevertheless the information is not lost the shape and all that stuff,", 'start': 8033.524, 'duration': 7.006}, {'end': 8047.295, 'text': 'and this can be also rendered on a slightly a device which is probably not that sophisticated.', 'start': 8040.53, 'duration': 6.765}, {'end': 8049.866, 'text': "Okay, so that's pretty much it.", 'start': 8047.725, 'duration': 2.141}, {'end': 8056.251, 'text': 'So we have seen two examples of how color compression can be done using k-means clustering.', 'start': 8049.886, 'duration': 6.365}, {'end': 8066.097, 'text': 'And we have also seen in the previous examples of how to implement k-means, the code to roughly how to implement k-means clustering.', 'start': 8057.091, 'duration': 9.006}, {'end': 8072.842, 'text': 'And we use some sample data using blob to just execute the k-means clustering.', 'start': 8066.337, 'duration': 6.505}, {'end': 8073.923, 'text': 'Thank you, Mohan.', 'start': 8073.202, 'duration': 0.721}, {'end': 8075.604, 'text': 'That was really informative.', 'start': 8074.203, 'duration': 1.401}, {'end': 8079.23, 'text': 'Now on to Richard to help you learn more algorithms.', 'start': 8076.368, 'duration': 2.862}], 'summary': 'Color compression using k-means clustering demonstrated with examples and sample data, presented by mohan.', 'duration': 45.706, 'max_score': 8033.524, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY588033524.jpg'}, {'end': 8236.007, 'src': 'embed', 'start': 8173.163, 'weight': 3, 'content': [{'end': 8180.846, 'text': 'In classification, the classification tree will determine a set of logical if-then conditions to classify problems.', 'start': 8173.163, 'duration': 7.683}, {'end': 8185.567, 'text': 'For example, discriminating between three types of flowers based on certain features.', 'start': 8181.066, 'duration': 4.501}, {'end': 8191.749, 'text': 'In regression, a regression tree is used when the target variable is numerical or continuous in nature.', 'start': 8185.947, 'duration': 5.802}, {'end': 8196.611, 'text': 'We fit the regression model to the target variable using each of the independent variables.', 'start': 8191.849, 'duration': 4.762}, {'end': 8200.092, 'text': 'Each split is made based on the sum of squared error.', 'start': 8196.95, 'duration': 3.142}, {'end': 8204.969, 'text': 'Before we dig deeper into the mechanics of the decision tree,', 'start': 8201.087, 'duration': 3.882}, {'end': 8210.334, 'text': "let's take a look at the advantages of using a decision tree and we'll also take a glimpse at the disadvantages.", 'start': 8204.969, 'duration': 5.365}, {'end': 8216.159, 'text': "The first thing you'll notice is that it's simple to understand, interpret, and visualize.", 'start': 8210.695, 'duration': 5.464}, {'end': 8220.281, 'text': "It really shines here because you can see exactly what's going on in a decision tree.", 'start': 8216.539, 'duration': 3.742}, {'end': 8225.003, 'text': "Little effort is required for data preparation, so you don't have to do special scaling.", 'start': 8220.46, 'duration': 4.543}, {'end': 8228.044, 'text': "There's a lot of things you don't have to worry about when using a decision tree.", 'start': 8225.022, 'duration': 3.022}, {'end': 8232.446, 'text': 'It can handle both numerical and categorical data, as we discovered earlier.', 'start': 8228.284, 'duration': 4.162}, {'end': 8236.007, 'text': "And nonlinear parameters don't affect its performance.", 'start': 8232.626, 'duration': 3.381}], 'summary': 'Decision trees: simple, versatile, handle diverse data, low data prep effort.', 'duration': 62.844, 'max_score': 8173.163, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY588173163.jpg'}, {'end': 8306.272, 'src': 'embed', 'start': 8278.995, 'weight': 5, 'content': [{'end': 8282.777, 'text': 'Before we dive in further, we need to look at some basic terms.', 'start': 8278.995, 'duration': 3.782}, {'end': 8288.481, 'text': "We need to have some definitions to go with our decision tree and the different parts we're going to be using.", 'start': 8283.037, 'duration': 5.444}, {'end': 8289.701, 'text': "We'll start with entropy.", 'start': 8288.54, 'duration': 1.161}, {'end': 8294.165, 'text': 'Entropy is a measure of randomness or unpredictability in the data set.', 'start': 8290.121, 'duration': 4.044}, {'end': 8296.746, 'text': 'For example, we have a group of animals.', 'start': 8294.504, 'duration': 2.242}, {'end': 8302.129, 'text': "In this picture, there's four different kinds of animals, and this data set is considered to have a high entropy.", 'start': 8297.066, 'duration': 5.063}, {'end': 8306.272, 'text': "You really can't pick out what kind of animal it is based on looking at just the four animals.", 'start': 8302.19, 'duration': 4.082}], 'summary': 'Entropy measures unpredictability in data sets, such as a group of animals with high entropy.', 'duration': 27.277, 'max_score': 8278.995, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY588278995.jpg'}], 'start': 8033.524, 'title': 'Color compression and decision tree', 'summary': 'Demonstrates color compression using k-means clustering with rough code implementation and sample data execution, and explains decision tree concept, its application, advantages, and demonstrates its implementation in python for loan repayment prediction.', 'chapters': [{'end': 8079.23, 'start': 8033.524, 'title': 'Color compression using k-means clustering', 'summary': 'Demonstrates two examples of color compression using k-means clustering, and also provides a rough code implementation and sample data execution for k-means clustering.', 'duration': 45.706, 'highlights': ['The chapter demonstrates two examples of color compression using k-means clustering.', 'Rough code implementation and sample data execution for k-means clustering is provided.', 'The information is not lost in the color compression process.']}, {'end': 8863.397, 'start': 8079.391, 'title': 'Decision tree in machine learning', 'summary': 'Explains the concept of a decision tree in machine learning, its application in classification and regression problems, the advantages and disadvantages of using a decision tree, and demonstrates its implementation in python for loan repayment prediction.', 'duration': 784.006, 'highlights': ['Decision Tree can solve problems in classification and regression, such as discriminating between types of flowers and predicting the next value in a series of numbers. It can be used for classification to determine logical if-then conditions for classifying problems, for example, discriminating between three types of flowers based on certain features. In regression, it is used to predict the next value in a series of numbers or a group of data.', 'Advantages of using a decision tree include its simplicity to understand, interpret, and visualize, and its ability to handle both numerical and categorical data. It is simple to understand, interpret, and visualize and requires little effort for data preparation. It can handle both numerical and categorical data and is not affected by nonlinear parameters.', 'The disadvantages of using a decision tree are overfitting, high variance, and low bias. The disadvantages include overfitting, high variance, and low bias, which make the model unstable due to small variations in data and difficult to work with new data.', 'Basic terms related to decision trees include entropy and information gain, which are used to measure randomness and decrease in entropy after splitting the dataset. Entropy is a measure of randomness or unpredictability in the dataset, while information gain measures the decrease in entropy after splitting the dataset. These terms are fundamental in understanding the mechanics of a decision tree.', 'The process of using a decision tree involves splitting the data based on conditions that result in the highest gain, ultimately leading to accurate classification. The process involves calculating the entropy for the current dataset, choosing conditions that give the highest gain, and splitting the data to achieve the highest gain, leading to accurate classification of the data.', 'The implementation of a decision tree in Python for loan repayment prediction involves importing necessary packages, loading and exploring the dataset, and addressing warnings and errors encountered during the coding process. The implementation includes importing necessary packages from Python, loading and exploring the dataset, and addressing warnings and errors encountered during the coding process, such as handling the deprecation warning for cross validation.']}], 'duration': 829.873, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY588033524.jpg', 'highlights': ['Rough code implementation and sample data execution for k-means clustering is provided.', 'The chapter demonstrates two examples of color compression using k-means clustering.', 'The information is not lost in the color compression process.', 'Decision Tree can solve problems in classification and regression, such as discriminating between types of flowers and predicting the next value in a series of numbers.', 'Advantages of using a decision tree include its simplicity to understand, interpret, and visualize, and its ability to handle both numerical and categorical data.', 'Basic terms related to decision trees include entropy and information gain, which are used to measure randomness and decrease in entropy after splitting the dataset.', 'The process of using a decision tree involves splitting the data based on conditions that result in the highest gain, ultimately leading to accurate classification.']}, {'end': 10497.19, 'segs': [{'end': 9061.718, 'src': 'embed', 'start': 9018.599, 'weight': 4, 'content': [{'end': 9021.824, 'text': "And again, we're going to utilize the tools in Panda.", 'start': 9018.599, 'duration': 3.225}, {'end': 9029.355, 'text': 'And since the balance underscore data was loaded as a Panda data frame, we can do a shape on it.', 'start': 9022.926, 'duration': 6.429}, {'end': 9032.24, 'text': "And let's go ahead and run the shape and see what that looks like.", 'start': 9029.636, 'duration': 2.604}, {'end': 9040.308, 'text': "What's nice about this shape is not only does it give me the length of the data, we have a thousand lines, it also tells me there's five columns.", 'start': 9034.005, 'duration': 6.303}, {'end': 9043.409, 'text': 'So when we were looking at the data, we had five columns of data.', 'start': 9040.548, 'duration': 2.861}, {'end': 9047.391, 'text': "And then let's take one more step to explore the data using Python.", 'start': 9043.629, 'duration': 3.762}, {'end': 9050.673, 'text': "And now that we've taken a look at the length and the shape,", 'start': 9047.691, 'duration': 2.982}, {'end': 9057.956, 'text': "let's go ahead and use the pandas module for head another beautiful thing in the data set that we can utilize.", 'start': 9050.673, 'duration': 7.283}, {'end': 9061.718, 'text': "So let's put that on our sheet here, and we have print data set.", 'start': 9058.276, 'duration': 3.442}], 'summary': 'Panda data frame has 1000 lines and 5 columns, explored using python.', 'duration': 43.119, 'max_score': 9018.599, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY589018599.jpg'}, {'end': 9596.094, 'src': 'embed', 'start': 9559.843, 'weight': 1, 'content': [{'end': 9561.843, 'text': 'We now have accuracy set up on here.', 'start': 9559.843, 'duration': 2}, {'end': 9568.025, 'text': 'And so we have created a model that uses the decision tree algorithm to predict whether a customer will repay the loan or not.', 'start': 9562.083, 'duration': 5.942}, {'end': 9571.2, 'text': 'The accuracy of the model is about 94.6%.', 'start': 9568.459, 'duration': 2.741}, {'end': 9577.084, 'text': 'The bank can now use this model to decide whether it should approve the loan request from a particular customer or not.', 'start': 9571.2, 'duration': 5.884}, {'end': 9579.505, 'text': 'And so this information is really powerful.', 'start': 9577.424, 'duration': 2.081}, {'end': 9584.768, 'text': 'We may not be able to, as individuals, understand all these numbers because they have thousands of numbers that come in.', 'start': 9579.785, 'duration': 4.983}, {'end': 9596.094, 'text': 'But you can see that this is a smart decision for the bank to use a tool like this to help them to predict how good their profit is going to be off of the loan balances and how many are going to default or not.', 'start': 9584.868, 'duration': 11.226}], 'summary': 'A decision tree model with 94.6% accuracy predicts loan repayments, aiding bank decisions.', 'duration': 36.251, 'max_score': 9559.843, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY589559843.jpg'}, {'end': 9682.917, 'src': 'embed', 'start': 9656.238, 'weight': 2, 'content': [{'end': 9659.999, 'text': 'And what it does is it tracks the body movements and it recreates it in the game.', 'start': 9656.238, 'duration': 3.761}, {'end': 9661.739, 'text': "And let's see what that looks like.", 'start': 9660.519, 'duration': 1.22}, {'end': 9664.319, 'text': 'We have a user who performs a step.', 'start': 9662.399, 'duration': 1.92}, {'end': 9666.76, 'text': 'In this case, it looks like Elvis Presley going there.', 'start': 9664.48, 'duration': 2.28}, {'end': 9671.052, 'text': 'That is then recorded so the Kinect registers the movement.', 'start': 9667.811, 'duration': 3.241}, {'end': 9674.574, 'text': 'And then it marks the user based on accuracy.', 'start': 9671.813, 'duration': 2.761}, {'end': 9679.756, 'text': 'And it looks like we have prints going on this one from Elvis Presley to Prince.', 'start': 9674.814, 'duration': 4.942}, {'end': 9680.216, 'text': "It's great.", 'start': 9679.816, 'duration': 0.4}, {'end': 9682.917, 'text': 'So it marks the user based on the accuracy.', 'start': 9681.196, 'duration': 1.721}], 'summary': 'A system tracks body movements and recreates them in a game, marking users based on accuracy, transitioning from elvis presley to prince.', 'duration': 26.679, 'max_score': 9656.238, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY589656238.jpg'}, {'end': 9828.476, 'src': 'embed', 'start': 9801.504, 'weight': 3, 'content': [{'end': 9805.426, 'text': 'Let us dig deep into the theory of exactly how it works.', 'start': 9801.504, 'duration': 3.922}, {'end': 9808.907, 'text': "And let's look at what is random forest.", 'start': 9806.086, 'duration': 2.821}, {'end': 9816.13, 'text': 'Random forest or random decision forest is a method that operates by constructing multiple decision trees.', 'start': 9809.407, 'duration': 6.723}, {'end': 9821.852, 'text': 'The decision of the majority of the trees is chosen by the random forest as the final decision.', 'start': 9816.67, 'duration': 5.182}, {'end': 9823.852, 'text': 'And we have some nice graphics here.', 'start': 9822.152, 'duration': 1.7}, {'end': 9824.933, 'text': 'We have a decision tree.', 'start': 9823.893, 'duration': 1.04}, {'end': 9828.476, 'text': 'And they actually use a real tree to denote the decision tree, which I love.', 'start': 9825.253, 'duration': 3.223}], 'summary': 'Random forest is a method using multiple decision trees to make final decisions.', 'duration': 26.972, 'max_score': 9801.504, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY589801504.jpg'}, {'end': 10333.465, 'src': 'embed', 'start': 10305.839, 'weight': 0, 'content': [{'end': 10308.259, 'text': "There's only one type of data in each one of those bowls.", 'start': 10305.839, 'duration': 2.42}, {'end': 10310.62, 'text': 'So we can predict a lemon with 100% accuracy.', 'start': 10308.68, 'duration': 1.94}, {'end': 10317.688, 'text': 'And we can predict the apple also with 100% accuracy, along with our grapes up there.', 'start': 10312.581, 'duration': 5.107}, {'end': 10326.298, 'text': "So we've looked at kind of a basic tree in our forest, but what we really want to know is how does a random forest work as a whole?", 'start': 10318.048, 'duration': 8.25}, {'end': 10333.465, 'text': "So to begin our random forest classifier, Let's say we already have built three trees.", 'start': 10326.658, 'duration': 6.807}], 'summary': 'With 100% accuracy, the classifier can predict lemons, apples, and grapes, and has three built trees.', 'duration': 27.626, 'max_score': 10305.839, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5810305839.jpg'}], 'start': 8865.359, 'title': 'Data exploration, decision tree model, kinect, and random forest', 'summary': 'Covers data exploration and preparation for machine learning with a dataset of 1000 lines and 5 columns, building and applying a decision tree model to predict loan repayments with 93.6% accuracy, application of kinect for body movement tracking in a game, and the use of random forest classifier to categorize fruits with 100% accuracy.', 'chapters': [{'end': 9237.998, 'start': 8865.359, 'title': 'Data exploration and preparation for machine learning', 'summary': "Explores how to analyze and prepare a dataset of 1000 lines and 5 columns using python's pandas module, including printing the length and shape of the data, and examining the first five rows, in preparation for training a decision tree model.", 'duration': 372.639, 'highlights': ["The dataset comprises 1000 lines and 5 columns, which is printed using the pandas module in Python. The 'shape' function in pandas reveals that the dataset contains 1000 lines and 5 columns, providing an overview of the dataset's size and structure.", "The 'head' function in pandas displays the first five rows of the dataset, offering a clean and readable view of the initial payment, last payment, credit scores, and house number. The 'head' function in pandas allows viewing the first five rows of the dataset, showcasing a clean and readable format of the initial payment, last payment, credit scores, and house number, assisting in data examination and analysis.", 'The process of separating the dataset into X and Y for training and testing is explained, where X represents the data and Y represents the target. The process of separating the dataset into X and Y is outlined, with X representing the data and Y representing the target, providing clarity on the source and target data for machine learning model preparation.']}, {'end': 9655.498, 'start': 9238.058, 'title': 'Decision tree model application', 'summary': 'Explains the process of building and applying a decision tree model to predict loan repayments, achieving an accuracy of 93.6%, and discusses the potential applications of random forest in various fields including remote sensing, object detection, and gaming consoles.', 'duration': 417.44, 'highlights': ['The decision tree model is built and applied to predict loan repayments, achieving an accuracy of 93.6%. The model successfully predicts whether a customer will repay the loan with an accuracy of 93.6%, allowing the bank to make informed decisions about loan approvals.', 'Random Forest is used in remote sensing, object detection, and gaming consoles, providing higher accuracy and faster training time compared to other machine learning tools. Random Forest is applied in various fields including remote sensing, object detection, and gaming consoles, offering improved accuracy and faster training time, making it a powerful tool in machine learning.', 'Random Forest is utilized in areas such as remote sensing for ETM devices, object detection for traffic, and as part of the game console for Connect. Random Forest is employed in remote sensing for ETM devices, object detection for traffic, and as part of the game console for Connect, showcasing its versatility and wide-ranging applications.']}, {'end': 10180.319, 'start': 9656.238, 'title': 'Kinect and random forest', 'summary': 'Discusses the use of kinect for tracking body movements in a game, with the kinect marking the user based on accuracy, and the application of random forest method which offers benefits such as no overfitting, high accuracy, efficient processing of large databases, and estimation of missing data.', 'duration': 524.081, 'highlights': ["The Kinect marks the user based on accuracy, tracking body movements for a game. The Kinect registers and records the user's movements, then marks the user based on accuracy while performing steps, such as dancing like Elvis Presley or Prince.", 'Random Forest method offers benefits such as no overfitting, high accuracy, efficient processing of large databases, and estimation of missing data. Random Forest method avoids overfitting, runs efficiently with large databases, and accurately predicts outcomes, even when a large proportion of the data is missing.', 'Random Forest operates by constructing multiple decision trees, with the majority decision being chosen as the final decision. Random Forest method constructs multiple decision trees and selects the majority decision as the final outcome, offering a reliable method for making decisions based on the input data.']}, {'end': 10497.19, 'start': 10181.02, 'title': 'Random forest classifier', 'summary': 'Explains the process of splitting data to maximize information gain, using entropy to measure the decrease in chaos, and the application of random forest classifier with three trees to categorize fruits based on different attributes, achieving 100% accuracy in prediction.', 'duration': 316.17, 'highlights': ['The entropy after splitting has decreased considerably, leading to less chaos and an entropy value of zero for certain branches, eliminating the need for further splitting. The entropy after splitting has decreased considerably, leading to less chaos and an entropy value of zero for certain branches, eliminating the need for further splitting.', 'Using a random forest classifier with three decision trees, the process achieves 100% accuracy in predicting the type of fruit based on different features such as diameter, color, and shape. Using a random forest classifier with three decision trees, the process achieves 100% accuracy in predicting the type of fruit based on different features such as diameter, color, and shape.', 'The process involves splitting the data based on conditions that result in the highest gain, such as splitting based on diameter and color, to categorize fruits into distinct groups with zero entropy. The process involves splitting the data based on conditions that result in the highest gain, such as splitting based on diameter and color, to categorize fruits into distinct groups with zero entropy.']}], 'duration': 1631.831, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY588865359.jpg', 'highlights': ['Random Forest classifier achieves 100% accuracy in categorizing fruits based on features.', 'Decision tree model predicts loan repayments with 93.6% accuracy.', 'Kinect tracks body movements for game and marks user based on accuracy.', 'Random Forest method constructs multiple decision trees for reliable decision-making.', "Pandas 'shape' function reveals dataset size of 1000 lines and 5 columns.", "Pandas 'head' function displays first five rows of dataset for examination."]}, {'end': 12651.096, 'segs': [{'end': 10546.465, 'src': 'embed', 'start': 10517.043, 'weight': 0, 'content': [{'end': 10521.887, 'text': 'This is the exciting part as we roll up our sleeves and actually look at some Python coding.', 'start': 10517.043, 'duration': 4.844}, {'end': 10526.09, 'text': 'Before we start the Python coding, we need to go ahead and create a problem statement.', 'start': 10522.067, 'duration': 4.023}, {'end': 10533.716, 'text': "Wonder what species of iris do these flowers belong to? Let's try to predict the species of the flowers using machine learning in Python.", 'start': 10526.49, 'duration': 7.226}, {'end': 10535.377, 'text': "Let's see how it can be done.", 'start': 10533.936, 'duration': 1.441}, {'end': 10539.68, 'text': 'So here we begin to go ahead and implement our Python code.', 'start': 10535.597, 'duration': 4.083}, {'end': 10546.465, 'text': "And you'll find that the first half of our implementation is all about organizing and exploring the data coming in.", 'start': 10539.96, 'duration': 6.505}], 'summary': 'Implementing python code to predict iris flower species using machine learning.', 'duration': 29.422, 'max_score': 10517.043, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5810517043.jpg'}, {'end': 10655.503, 'src': 'embed', 'start': 10577.236, 'weight': 1, 'content': [{'end': 10586.327, 'text': "and i've already opened up a new page for Python 3 code and I'm just going to paste this right in there and let's take a look and see what we're bringing into our Python.", 'start': 10577.236, 'duration': 9.091}, {'end': 10591.734, 'text': "The first thing we're going to do is from the sklearn.datasets import load iris.", 'start': 10586.468, 'duration': 5.266}, {'end': 10598.18, 'text': "Now this isn't the actual data this is just the module that allows us to bring in the data, the load iris.", 'start': 10592.075, 'duration': 6.105}, {'end': 10604.582, 'text': "And the iris is so popular it's been around since 1936, when Ronald Fisher published a paper on it,", 'start': 10598.56, 'duration': 6.022}, {'end': 10610.424, 'text': "and they're measuring the different parts of the flower and, based on those measurements, predicting what kind of flower it is.", 'start': 10604.582, 'duration': 5.842}, {'end': 10617.346, 'text': "And then if we're going to do a random forest classifier, we need to go ahead and import a random forest classifier from the sklearn module.", 'start': 10610.824, 'duration': 6.522}, {'end': 10621.692, 'text': 'So sklearn.ensemble, import random forest classifier.', 'start': 10617.711, 'duration': 3.981}, {'end': 10623.753, 'text': 'And then we want to bring in two more modules.', 'start': 10621.952, 'duration': 1.801}, {'end': 10632.796, 'text': 'And these are probably the most commonly used modules in Python and data science with any of the other modules that we bring in.', 'start': 10624.733, 'duration': 8.063}, {'end': 10634.116, 'text': 'And one is going to be pandas.', 'start': 10632.876, 'duration': 1.24}, {'end': 10636.017, 'text': "We're going to import pandas as pd.", 'start': 10634.136, 'duration': 1.881}, {'end': 10638.518, 'text': 'PD is a common term used for pandas.', 'start': 10636.437, 'duration': 2.081}, {'end': 10648.201, 'text': 'And pandas basically creates a data format for us where when you create a pandas data frame, it looks like an Excel spreadsheet.', 'start': 10638.818, 'duration': 9.383}, {'end': 10651.562, 'text': "And you'll see that in a minute when we start digging deeper into the code.", 'start': 10648.521, 'duration': 3.041}, {'end': 10655.503, 'text': 'Panda is just wonderful because it plays nice with all the other modules in there.', 'start': 10651.702, 'duration': 3.801}], 'summary': 'Using python 3, importing sklearn.datasets for iris data, and pandas as pd for data manipulation.', 'duration': 78.267, 'max_score': 10577.236, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5810577236.jpg'}, {'end': 11137.743, 'src': 'embed', 'start': 11108.797, 'weight': 5, 'content': [{'end': 11109.977, 'text': "Let's see what that looks like.", 'start': 11108.797, 'duration': 1.18}, {'end': 11115.759, 'text': "And you'll see that it puts 118 in the training module and it puts 32 in the testing module,", 'start': 11110.137, 'duration': 5.622}, {'end': 11119.001, 'text': 'which lets us know that there was 150 lines of data in here.', 'start': 11115.759, 'duration': 3.242}, {'end': 11122.362, 'text': "So if you went and looked at the original data, you could see that there's 150 lines.", 'start': 11119.061, 'duration': 3.301}, {'end': 11128.384, 'text': "And that's roughly 75% in one and 25% for us to test our model on afterward.", 'start': 11122.762, 'duration': 5.622}, {'end': 11131.045, 'text': "So let's jump back to our code and see where this goes.", 'start': 11128.664, 'duration': 2.381}, {'end': 11137.743, 'text': "In the next two steps, We want to do one more thing with our data and that's make it readable to humans.", 'start': 11131.365, 'duration': 6.378}], 'summary': 'Data divided into 75% training and 25% testing modules, totaling 150 lines.', 'duration': 28.946, 'max_score': 11108.797, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5811108797.jpg'}, {'end': 11706.267, 'src': 'embed', 'start': 11657.173, 'weight': 7, 'content': [{'end': 11664.527, 'text': "2 And we're going to put those in and we're going to predict what our new forest classifier is going to come up with.", 'start': 11657.173, 'duration': 7.354}, {'end': 11665.808, 'text': 'And this is what it predicts.', 'start': 11664.727, 'duration': 1.081}, {'end': 11669.77, 'text': 'It predicts 0001211222.', 'start': 11665.868, 'duration': 3.902}, {'end': 11675.492, 'text': 'And again, this is the flower type, setosa, virginica, and versicolor.', 'start': 11669.77, 'duration': 5.722}, {'end': 11680.473, 'text': "So now that we've taken our test features, let's explore that.", 'start': 11675.792, 'duration': 4.681}, {'end': 11683.474, 'text': "Let's see exactly what that data means to us.", 'start': 11680.553, 'duration': 2.921}, {'end': 11689.756, 'text': 'So the first thing we can do with our predicts is we can actually generate a different prediction model.', 'start': 11683.814, 'duration': 5.942}, {'end': 11692.436, 'text': "When I say different, we're going to view it differently.", 'start': 11690.236, 'duration': 2.2}, {'end': 11694.397, 'text': "It's not that the data itself is different.", 'start': 11692.757, 'duration': 1.64}, {'end': 11697.638, 'text': "So let's take this next piece of code and put it into our script.", 'start': 11694.977, 'duration': 2.661}, {'end': 11706.267, 'text': "So we're pasting it in here, and you'll see that we're doing predict, and we've added underscore proba for probability.", 'start': 11699.224, 'duration': 7.043}], 'summary': 'Using a new forest classifier to predict flower types, yielding results of 0001211222.', 'duration': 49.094, 'max_score': 11657.173, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5811657173.jpg'}, {'end': 11820.788, 'src': 'embed', 'start': 11796.283, 'weight': 10, 'content': [{'end': 11802.424, 'text': "So now we've looked at both how to do a basic predict of the features and we've looked at the predict probability.", 'start': 11796.283, 'duration': 6.141}, {'end': 11804.345, 'text': "Let's see what's next on here.", 'start': 11803.065, 'duration': 1.28}, {'end': 11808.566, 'text': 'So now we want to go ahead and start mapping names for the plants.', 'start': 11804.665, 'duration': 3.901}, {'end': 11812.407, 'text': 'We want to attach names so that it makes a little more sense for us.', 'start': 11809.026, 'duration': 3.381}, {'end': 11814.707, 'text': "And that's what we're going to do in these next two steps.", 'start': 11812.427, 'duration': 2.28}, {'end': 11820.788, 'text': "We're going to start by setting up our predictions and mapping them to the name.", 'start': 11815.067, 'duration': 5.721}], 'summary': 'Mapping names for plants based on predictions.', 'duration': 24.505, 'max_score': 11796.283, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5811796283.jpg'}, {'end': 11970.571, 'src': 'embed', 'start': 11941.336, 'weight': 9, 'content': [{'end': 11945.079, 'text': 'We need to know how good our forest is, how good it is at predicting the features.', 'start': 11941.336, 'duration': 3.743}, {'end': 11947.981, 'text': "So that's where we come up to the next step, which is lots of fun.", 'start': 11945.279, 'duration': 2.702}, {'end': 11956.067, 'text': "We're going to use a single line of code to combine our predictions and our actuals so we have a nice chart to look at.", 'start': 11948.581, 'duration': 7.486}, {'end': 11959.129, 'text': "And let's go ahead and put that in our script in our Jupyter notebook here.", 'start': 11956.207, 'duration': 2.922}, {'end': 11961.189, 'text': "Let's see, let's go ahead and paste that in.", 'start': 11959.629, 'duration': 1.56}, {'end': 11966.11, 'text': "And then I'm going to, because I'm on the Jupyter Notebook, I can do a control minus.", 'start': 11961.449, 'duration': 4.661}, {'end': 11967.431, 'text': 'You can see the whole line there.', 'start': 11966.19, 'duration': 1.241}, {'end': 11970.571, 'text': 'There we go, resize it.', 'start': 11967.451, 'duration': 3.12}], 'summary': "Analyzing forest's prediction accuracy using code in jupyter notebook.", 'duration': 29.235, 'max_score': 11941.336, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5811941336.jpg'}, {'end': 12051.977, 'src': 'embed', 'start': 12020.444, 'weight': 11, 'content': [{'end': 12024.666, 'text': 'And then we notice here where it says virginica but it was supposed to be versicolor.', 'start': 12020.444, 'duration': 4.222}, {'end': 12025.826, 'text': 'This is inaccurate.', 'start': 12024.826, 'duration': 1}, {'end': 12031.788, 'text': 'So now we have two, two inaccurate predictions and 30 accurate predictions.', 'start': 12026.246, 'duration': 5.542}, {'end': 12034.271, 'text': "So we'll say that the model accuracy is 93.", 'start': 12032.228, 'duration': 2.043}, {'end': 12035.914, 'text': "That's just 30 divided by 32.", 'start': 12034.271, 'duration': 1.643}, {'end': 12038.178, 'text': 'And if we multiply it by 100, we can say that it is 93% accurate.', 'start': 12035.914, 'duration': 2.264}, {'end': 12039.64, 'text': 'So we have a 93% accuracy with our model.', 'start': 12038.418, 'duration': 1.222}, {'end': 12051.977, 'text': 'I did want to add one more quick thing in here on our scripting before we wrap it up.', 'start': 12047.452, 'duration': 4.525}], 'summary': 'Model accuracy is 93%, with 30 accurate predictions out of 32.', 'duration': 31.533, 'max_score': 12020.444, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5812020444.jpg'}, {'end': 12349.93, 'src': 'embed', 'start': 12313.146, 'weight': 12, 'content': [{'end': 12319.169, 'text': 'K and KNN is a perimeter that refers to the number of nearest neighbors to include in the majority of the voting process.', 'start': 12313.146, 'duration': 6.023}, {'end': 12324.492, 'text': 'And so if we add a new glass of wine there, red or white, we want to know what the neighbors are.', 'start': 12319.409, 'duration': 5.083}, {'end': 12327.253, 'text': "In this case, we're going to put K equals 5.", 'start': 12324.612, 'duration': 2.641}, {'end': 12328.634, 'text': "We'll talk about K in just a minute.", 'start': 12327.253, 'duration': 1.381}, {'end': 12333.076, 'text': 'A data point is classified by the majority of votes from its five nearest neighbors.', 'start': 12328.854, 'duration': 4.222}, {'end': 12337.758, 'text': 'Here, the unknown point would be classified as red since four out of five neighbors are red.', 'start': 12333.536, 'duration': 4.222}, {'end': 12345.005, 'text': "So, how do we choose K? How do we know K equals 5? I mean, that was the value we put in there, so we're going to talk about it.", 'start': 12338.338, 'duration': 6.667}, {'end': 12349.93, 'text': 'How do we choose the factor K? KNN algorithm is based on feature similarity.', 'start': 12345.285, 'duration': 4.645}], 'summary': 'Knn algorithm uses k=5 for classifying data based on feature similarity.', 'duration': 36.784, 'max_score': 12313.146, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5812313146.jpg'}, {'end': 12476.745, 'src': 'embed', 'start': 12447.231, 'weight': 14, 'content': [{'end': 12453.934, 'text': "so usually take the square root of n and if it's even, you add one to it or subtract one from it, and that's where you get the K value from.", 'start': 12447.231, 'duration': 6.703}, {'end': 12456.274, 'text': "that is the most common use and it's pretty solid.", 'start': 12453.934, 'duration': 2.34}, {'end': 12457.555, 'text': 'it works very well.', 'start': 12456.274, 'duration': 1.281}, {'end': 12459.117, 'text': 'when do we use KNN?', 'start': 12457.555, 'duration': 1.562}, {'end': 12461.981, 'text': 'We can use KNN when data is labeled.', 'start': 12459.317, 'duration': 2.664}, {'end': 12463.263, 'text': 'So you need a label on it.', 'start': 12462.342, 'duration': 0.921}, {'end': 12466.427, 'text': 'We know we have a group of pictures with dogs, dogs, cats, cats.', 'start': 12463.303, 'duration': 3.124}, {'end': 12468.711, 'text': 'Data is noise-free.', 'start': 12466.688, 'duration': 2.023}, {'end': 12476.745, 'text': "And so you can see here, when we have a class and we have like underweight, 140, 23, Hello Kitty, normal, that's pretty confusing.", 'start': 12469.221, 'duration': 7.524}], 'summary': 'Knn is used when data is labeled, noise-free, and for finding k value.', 'duration': 29.514, 'max_score': 12447.231, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5812447231.jpg'}], 'start': 10497.51, 'title': 'Iris flower analysis with python', 'summary': 'Introduces iris flower analysis using python, focusing on predicting species using machine learning, covering the implementation process, significance of the iris dataset, utilizing sklearn ensemble, data science modules, model testing, prediction, mapping plant names, knn algorithm with 93% accuracy, and working principles.', 'chapters': [{'end': 10617.346, 'start': 10497.51, 'title': 'Iris flower analysis with python', 'summary': 'Introduces the iris flower analysis using python, focusing on predicting the species of the flowers using machine learning, and covers the implementation process and the significance of the iris dataset.', 'duration': 119.836, 'highlights': ['Introduction to the iris flower analysis using Python and the goal of predicting the species of flowers using machine learning. ', 'Explanation of the significance of the iris dataset, which has been popular since 1936 for predicting flower species based on measurements. The iris dataset has been in use since 1936.', "Importing the 'load iris' module from sklearn.datasets in Python for bringing in the data. ", 'Mention of the random forest classifier import from the sklearn module for implementing the analysis. ']}, {'end': 11544.693, 'start': 10617.711, 'title': 'Using sklearn ensemble and data science modules', 'summary': 'Covers importing the random forest classifier from sklearn.ensemble and utilizing pandas and numpy modules for data manipulation, exploring the data, and preparing it for training and testing, including splitting the data into training and testing sets and converting data for model prediction.', 'duration': 926.982, 'highlights': ['We import the random forest classifier from sklearn.ensemble and utilize pandas and numpy modules for data manipulation, exploring the data, and preparing it for training and testing. ', 'The transcript focuses on exploring the data and preparing it for training and testing, including splitting the data into training and testing sets and converting data for model prediction. ', 'The process involves splitting the data into training and testing sets, with approximately 75% for training and 25% for testing, and converting the species data into a format understandable by the computer. Splitting data into training and testing sets (75% for training and 25% for testing)', 'The chapter covers preparing the data for training and testing, including making the data readable to humans and converting the species data into a format understandable by the computer. ']}, {'end': 11796.043, 'start': 11544.693, 'title': 'Model testing and prediction', 'summary': 'Covers the process of training a model with 75% of the data, testing it with 25% of the data, and using a forest classifier to predict the types of flowers based on the provided features, along with exploring the probabilities of the predictions.', 'duration': 251.35, 'highlights': ['The process involves training the model with 75% of the data and testing it with the remaining 25%, followed by using a forest classifier to predict the types of flowers based on the provided features, resulting in outputs representing the three types of flowers: setosa, virginica, and versicolor.', "Exploring the probabilities of the predictions involves running the model with the 'predict_proba' function, which generates a large field of probabilities for the leaf nodes, allowing for a deeper understanding of the prediction process and the likelihood of each flower type.", "The 'predict_proba' function generates three numbers, representing the probabilities for the three leaf nodes, allowing for the interpretation of the predictions and the selection of the most likely flower type based on the calculated probabilities."]}, {'end': 11982.994, 'start': 11796.283, 'title': 'Mapping plant names and predictions', 'summary': "Covers mapping plant names to predictions, comparing the model's predictions with the actual data, and creating a chart to evaluate the model's performance, using a single line of code to combine predictions and actuals.", 'duration': 186.711, 'highlights': ["Creating a chart to evaluate the model's performance The chapter explains using a single line of code to combine predictions and actuals, creating a chart to evaluate the model's performance.", "Comparing the model's predictions with the actual data The section discusses comparing the model's predictions with the actual data to understand the model's accuracy.", 'Mapping plant names to predictions The chapter covers mapping plant names to predictions, which adds context and understanding to the predictions made by the model.']}, {'end': 12651.096, 'start': 11983.094, 'title': 'Knn algorithm and model accuracy', 'summary': 'Covers the k nearest neighbors (knn) algorithm, explaining its application in predicting species with 93% accuracy and how to choose the factor k, along with its use cases and working principles.', 'duration': 668.002, 'highlights': ["The model accuracy is 93%, based on 30 accurate predictions out of 32. The model's accuracy is quantified at 93%, calculated from 30 accurate predictions out of a total of 32.", 'KNN algorithm is a fundamental place to start in machine learning, with a focus on feature similarity for classification. The KNN algorithm is highlighted as a fundamental starting point in machine learning, emphasizing feature similarity for classification purposes.', 'The KNN algorithm works by classifying a data point based on how its neighbors are classified. The KNN algorithm operates by classifying a data point based on the classification of its neighboring data points.', 'Choosing the right value of K is important for better accuracy in the KNN algorithm. Selecting the appropriate value for K is emphasized as crucial for enhancing accuracy within the KNN algorithm.', 'KNN is suitable for use in labeled data sets, noise-free data, and small data sets. KNN is deemed suitable for application in labeled, noise-free, and small data sets.']}], 'duration': 2153.586, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5810497510.jpg', 'highlights': ['Introduction to iris flower analysis using Python and machine learning for species prediction', 'Significance of iris dataset since 1936 for predicting flower species based on measurements', "Importing 'load iris' module from sklearn.datasets in Python for data import", 'Utilizing random forest classifier from sklearn module for analysis implementation', 'Utilizing pandas and numpy modules for data manipulation and preparation for training and testing', 'Splitting data into training and testing sets (75% for training and 25% for testing)', 'Training the model with 75% of the data and testing with the remaining 25%', 'Using forest classifier to predict flower types based on features, resulting in setosa, virginica, and versicolor', "Exploring probabilities of predictions using 'predict_proba' function for deeper understanding", "Creating a chart to evaluate the model's performance and comparing predictions with actual data", 'Mapping plant names to predictions for contextual understanding', 'Model accuracy at 93% with 30 accurate predictions out of 32', 'KNN algorithm as a fundamental starting point in machine learning for feature similarity', "KNN algorithm classifies data based on neighbors' classification and importance of choosing the right value for K", 'Suitability of KNN for labeled, noise-free, and small data sets']}, {'end': 14202.183, 'segs': [{'end': 12727.715, 'src': 'embed', 'start': 12697.872, 'weight': 2, 'content': [{'end': 12700.794, 'text': 'And this is in a simple spreadsheet format.', 'start': 12697.872, 'duration': 2.922}, {'end': 12703.096, 'text': 'The data itself is comma separated.', 'start': 12701.235, 'duration': 1.861}, {'end': 12704.557, 'text': 'Very common set of data.', 'start': 12703.336, 'duration': 1.221}, {'end': 12706.259, 'text': "And it's also a very common way to get the data.", 'start': 12704.677, 'duration': 1.582}, {'end': 12709.701, 'text': 'And you can see here we have columns A through I.', 'start': 12706.299, 'duration': 3.402}, {'end': 12710.963, 'text': "That's what 1, 2, 3, 4, 5, 6, 7, 8 are.", 'start': 12709.701, 'duration': 1.262}, {'end': 12720.35, 'text': 'eight columns with a particular attribute, and then the ninth column, which is the outcome, is whether they have diabetes.', 'start': 12713.324, 'duration': 7.026}, {'end': 12724.033, 'text': 'As a data scientist, the first thing you should be looking at is insulin.', 'start': 12720.63, 'duration': 3.403}, {'end': 12727.715, 'text': "Well, you know if someone has insulin, they have diabetes because that's why they're taking it.", 'start': 12724.113, 'duration': 3.602}], 'summary': 'Data in spreadsheet format with 8 attributes and 1 outcome column for diabetes prediction.', 'duration': 29.843, 'max_score': 12697.872, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5812697872.jpg'}, {'end': 12875.958, 'src': 'embed', 'start': 12845.672, 'weight': 1, 'content': [{'end': 12846.573, 'text': "It's a markdown language.", 'start': 12845.672, 'duration': 0.901}, {'end': 12850.052, 'text': 'So if I run this first one, it comes up in nice big letters, which is kind of nice.', 'start': 12846.889, 'duration': 3.163}, {'end': 12851.654, 'text': "Remind us what we're working on.", 'start': 12850.532, 'duration': 1.122}, {'end': 12855.377, 'text': 'And by now, you should be familiar with doing all of our imports.', 'start': 12851.934, 'duration': 3.443}, {'end': 12860.262, 'text': "We're going to import the pandas as pd, import numpy as np.", 'start': 12855.617, 'duration': 4.645}, {'end': 12864.285, 'text': 'Pandas is the pandas data frame, and numpy is the number array.', 'start': 12860.582, 'duration': 3.703}, {'end': 12866.448, 'text': 'Very powerful tools to use in here.', 'start': 12864.546, 'duration': 1.902}, {'end': 12867.709, 'text': 'So we have our imports.', 'start': 12866.468, 'duration': 1.241}, {'end': 12872.434, 'text': "So we've brought in our pandas, our numpy, our two general Python tools.", 'start': 12868.229, 'duration': 4.205}, {'end': 12875.958, 'text': 'And then you can see over here we have our train test split.', 'start': 12872.834, 'duration': 3.124}], 'summary': 'An overview of using pandas and numpy for data manipulation and split', 'duration': 30.286, 'max_score': 12845.672, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5812845672.jpg'}, {'end': 12937.848, 'src': 'embed', 'start': 12907.963, 'weight': 3, 'content': [{'end': 12909.304, 'text': 'And then the actual tool.', 'start': 12907.963, 'duration': 1.341}, {'end': 12912.206, 'text': "This is the k-neighbors classifier we're going to use.", 'start': 12909.504, 'duration': 2.702}, {'end': 12918.495, 'text': 'And finally, the last three are three tools to test, all about testing our model.', 'start': 12913.371, 'duration': 5.124}, {'end': 12920.596, 'text': 'How good is it? Let me just put down test on there.', 'start': 12918.535, 'duration': 2.061}, {'end': 12924.519, 'text': 'And we have our confusion matrix, our F1 score, and our accuracy.', 'start': 12920.716, 'duration': 3.803}, {'end': 12933.025, 'text': "So we have our two general Python modules we're importing, and then we have our six modules specific from the sklearn setup.", 'start': 12924.719, 'duration': 8.306}, {'end': 12937.848, 'text': 'And then we do need to go ahead and run this so that these are actually imported.', 'start': 12933.545, 'duration': 4.303}], 'summary': 'Using k-neighbors classifier and three testing tools to evaluate model performance with confusion matrix, f1 score, and accuracy.', 'duration': 29.885, 'max_score': 12907.963, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5812907963.jpg'}, {'end': 13345.588, 'src': 'embed', 'start': 13315.934, 'weight': 5, 'content': [{'end': 13318.536, 'text': "So far, we haven't had any printout other than to look at the data.", 'start': 13315.934, 'duration': 2.602}, {'end': 13321.338, 'text': 'But that is a lot of this, is prepping this data.', 'start': 13319.036, 'duration': 2.302}, {'end': 13324.24, 'text': 'Once you prep it, the actual lines of code are quick and easy.', 'start': 13321.558, 'duration': 2.682}, {'end': 13327.595, 'text': "And we're almost there with the actual running of our KNN.", 'start': 13324.753, 'duration': 2.842}, {'end': 13329.937, 'text': 'We need to go ahead and do a scale the data.', 'start': 13327.675, 'duration': 2.262}, {'end': 13336.561, 'text': "If you remember correctly, we're fitting the data in a standard scaler, which means instead of the data being from, you know,", 'start': 13330.237, 'duration': 6.324}, {'end': 13345.588, 'text': "5 to 303 in one column and the next column is 1 to 6, we're going to set that all so that all the data is between minus 1 and 1..", 'start': 13336.561, 'duration': 9.027}], 'summary': 'Data prepping is crucial. code lines are quick. knn almost ready. scaling data with standard scaler for consistency.', 'duration': 29.654, 'max_score': 13315.934, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5813315934.jpg'}, {'end': 13700.25, 'src': 'embed', 'start': 13672.212, 'weight': 0, 'content': [{'end': 13676.073, 'text': "It lets us know that there's more false positives than we would like on here.", 'start': 13672.212, 'duration': 3.861}, {'end': 13686.296, 'text': "But 82%, not too bad for a quick flash look at people's different statistics and running an SKLearn and running the KNN, the K nearest neighbor on it.", 'start': 13676.253, 'duration': 10.043}, {'end': 13693.928, 'text': 'So we have created a model using KNN, which can predict whether a person will have diabetes or not, or, at the very least,', 'start': 13686.795, 'duration': 7.133}, {'end': 13697.815, 'text': 'whether they should go get a checkup and have their glucose checked regularly or not.', 'start': 13693.928, 'duration': 3.887}, {'end': 13700.25, 'text': 'The print accuracy score, we got the .', 'start': 13698.288, 'duration': 1.962}], 'summary': 'Model achieved 82% accuracy in predicting diabetes risk.', 'duration': 28.038, 'max_score': 13672.212, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5813672212.jpg'}, {'end': 13799.628, 'src': 'embed', 'start': 13772.177, 'weight': 7, 'content': [{'end': 13776.178, 'text': 'Then, once we train our model, that model then can be given the new data,', 'start': 13772.177, 'duration': 4.001}, {'end': 13781.219, 'text': "and the new data is this image in this case you can see a question mark on it, and it comes through and goes it's a strawberry.", 'start': 13776.178, 'duration': 5.041}, {'end': 13785.04, 'text': "In this case we're using the support vector machine model.", 'start': 13781.399, 'duration': 3.641}, {'end': 13792.082, 'text': 'SVM is a supervised learning method that looks at data and sorts it into one of two categories,', 'start': 13785.3, 'duration': 6.782}, {'end': 13795.323, 'text': "and in this case we're sorting the strawberry into the strawberry side.", 'start': 13792.082, 'duration': 3.241}, {'end': 13799.628, 'text': 'At this point you should be asking the question how does the prediction work?', 'start': 13795.563, 'duration': 4.065}], 'summary': 'Trained svm model categorizes new image data, predicting strawberry with accuracy.', 'duration': 27.451, 'max_score': 13772.177, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5813772177.jpg'}, {'end': 14118.87, 'src': 'embed', 'start': 14093.268, 'weight': 8, 'content': [{'end': 14099.717, 'text': "Before we start looking at a programming example and dive into the script, let's look at the advantage of the support vector machine.", 'start': 14093.268, 'duration': 6.449}, {'end': 14106.221, 'text': "We'll start with high dimensional input space, or sometimes referred to as the curse of dimensionality.", 'start': 14100.097, 'duration': 6.124}, {'end': 14109.884, 'text': 'We looked at earlier one dimension, two dimension, three dimension.', 'start': 14106.482, 'duration': 3.402}, {'end': 14115.588, 'text': 'When you get to a thousand dimensions, a lot of problems start occurring with most algorithms that have to be adjusted for.', 'start': 14110.044, 'duration': 5.544}, {'end': 14118.87, 'text': 'The SVM automatically does that in high dimensional space.', 'start': 14115.808, 'duration': 3.062}], 'summary': 'Support vector machines automatically adjust for problems in high-dimensional spaces.', 'duration': 25.602, 'max_score': 14093.268, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5814093268.jpg'}], 'start': 12652.757, 'title': 'Data modeling and preprocessing', 'summary': 'Explains the knn algorithm for predicting diabetes using a dataset of 768 people, covers data preprocessing using pandas in python, and achieves an 82% accuracy and 0.69 f1 score for predicting diabetes with a knn model. it also discusses the support vector machine model for classification and its application in predicting fruits and genders.', 'chapters': [{'end': 12907.963, 'start': 12652.757, 'title': 'Knn algorithm & predicting diabetes', 'summary': 'Explains the knn algorithm, with a focus on predicting diabetes using a dataset of 768 people, and also covers the use of python tools like pandas and numpy for data manipulation and preprocessing.', 'duration': 255.206, 'highlights': ['The chapter explains the KNN algorithm and its application in predicting diabetes using a dataset of 768 people. It outlines the process of selecting k entries in a database closest to a new sample and finding the most common classification, demonstrating a straightforward approach to classification.', 'The use of Python tools like pandas and numpy for data manipulation and preprocessing is discussed. It mentions the import of pandas as pd and numpy as np, along with the use of train test split and standard scalar pre-processor for data normalization.', 'The data set used for predicting diabetes consists of 768 people and is in a simple comma-separated spreadsheet format. The dataset comprises eight columns representing attributes, and the ninth column indicating the outcome of whether the individuals have diabetes.']}, {'end': 13181.253, 'start': 12907.963, 'title': 'Data preprocessing and model testing', 'summary': "Covers the preprocessing of a dataset using pandas in python, including replacing zero values with nan and then with the mean, and testing the model's accuracy using a confusion matrix, f1 score, and accuracy.", 'duration': 273.29, 'highlights': ['Preprocessing the dataset by replacing zero values with NaN and then with the mean using Pandas commands. Replacing zero values with NaN and then with the mean to handle missing data in the dataset.', "Testing the model's accuracy using a confusion matrix, F1 score, and accuracy. Utilizing the confusion matrix, F1 score, and accuracy to evaluate the performance of the model.", 'Importing specific Python modules and sklearn setup for model building and testing. Importing Python modules and sklearn setup for model building and testing.']}, {'end': 13734.778, 'start': 13181.65, 'title': 'Data preprocessing and knn modeling', 'summary': 'Covers the data preprocessing steps including data exploration, splitting, and scaling, followed by building and evaluating a k-nearest neighbors (knn) model, achieving an accuracy score of 82% and f1 score of 0.69 for predicting diabetes.', 'duration': 553.128, 'highlights': ['The chapter covers the data preprocessing steps including data exploration, splitting, and scaling, followed by building and evaluating a K-nearest neighbors (KNN) model, achieving an accuracy score of 82% and F1 score of 0.69 for predicting diabetes. data preprocessing, splitting, scaling, K-nearest neighbors (KNN) model, accuracy score of 82%, F1 score of 0.69', 'The test size for data splitting is set to 20%, allowing 20% of the data to be put aside for testing the model later. test size: 20%', 'The data is standardized using the standard scaler, ensuring all the data is between -1 and 1, which is essential for KNN modeling. standard scaler, data standardization', 'The KNN model is created with n_neighbors=11 to ensure an odd number of neighbors for voting, P=2 for classifying diabetic condition, and using the Euclidean metric. KNN model parameters: n_neighbors=11, P=2, Euclidean metric', 'The confusion matrix, F1 score, and accuracy score are used to evaluate the KNN model, yielding an F1 score of 0.69 and an accuracy score of 82% for predicting diabetes. confusion matrix, F1 score: 0.69, accuracy score: 82%']}, {'end': 14202.183, 'start': 13735.058, 'title': 'Support vector machine model', 'summary': 'Discusses the use of support vector machine model for classification, including its application in predicting fruits and genders, explaining the concept of an optimal hyperplane, and addressing its advantages in high dimensional space and regularization parameter.', 'duration': 467.125, 'highlights': ['The chapter discusses the use of support vector machine model for classification, including its application in predicting fruits and genders. It explains how the support vector machine model can be utilized to classify fruits such as strawberries and apples, as well as to classify genders based on height and weight data.', 'Explaining the concept of an optimal hyperplane and its significance in classification. The chapter elaborates on the concept of an optimal hyperplane, demonstrating its role in separating different classes of data, and how it contributes to accurate classification.', 'Addressing the advantages of the support vector machine model in high dimensional space and regularization parameter. It highlights the advantages of the support vector machine model in handling high dimensional space and sparse document vectors, as well as its natural avoidance of overfitting and bias problems through the regularization parameter.']}], 'duration': 1549.426, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5812652757.jpg', 'highlights': ['The KNN algorithm is explained for predicting diabetes using a dataset of 768 people, achieving an accuracy score of 82% and an F1 score of 0.69.', 'Data preprocessing using Python tools like pandas and numpy is discussed, including handling missing data and data normalization.', 'The dataset for predicting diabetes consists of 768 people and includes eight columns representing attributes, with the ninth column indicating the outcome of whether the individuals have diabetes.', "The model's accuracy is tested using a confusion matrix, F1 score, and accuracy, and specific Python modules and sklearn setup are imported for model building and testing.", 'The data preprocessing steps, including exploration, splitting, and scaling, are covered, followed by building and evaluating a K-nearest neighbors (KNN) model with an accuracy score of 82% and an F1 score of 0.69 for predicting diabetes.', 'The test size for data splitting is set to 20%, and the data is standardized using the standard scaler for KNN modeling.', 'The KNN model is created with specific parameters, and the confusion matrix, F1 score, and accuracy score are used to evaluate the model, yielding an F1 score of 0.69 and an accuracy score of 82% for predicting diabetes.', 'The chapter discusses the use of support vector machine model for classification, including its application in predicting fruits and genders, and explains the concept of an optimal hyperplane and its significance in classification.', 'It addresses the advantages of the support vector machine model in high dimensional space and regularization parameter.']}, {'end': 15548.038, 'segs': [{'end': 14266.287, 'src': 'embed', 'start': 14238.61, 'weight': 0, 'content': [{'end': 14243.793, 'text': "First, we're going to cover in the code the setup, how to actually create our SVM.", 'start': 14238.61, 'duration': 5.183}, {'end': 14246.895, 'text': "And you're going to find that there's only two lines of code that actually create it,", 'start': 14243.973, 'duration': 2.922}, {'end': 14251.097, 'text': "and the rest of it is done so quick and fast that it's all here in the first page.", 'start': 14246.895, 'duration': 4.202}, {'end': 14255.44, 'text': "And we'll show you what that looks like as far as our data, because we're going to create some data.", 'start': 14251.357, 'duration': 4.083}, {'end': 14257.181, 'text': 'I talked about creating data just a minute ago.', 'start': 14255.56, 'duration': 1.621}, {'end': 14260.943, 'text': "And so we'll get into the creating data here and you'll see this nice correction of our two blobs.", 'start': 14257.321, 'duration': 3.622}, {'end': 14262.344, 'text': "And we'll go through that in just a second.", 'start': 14260.983, 'duration': 1.361}, {'end': 14266.287, 'text': "And then the second part is we're going to take this and we're going to bump it up a notch.", 'start': 14262.584, 'duration': 3.703}], 'summary': 'Setting up svm with only two lines of code, creating and visualizing data, then enhancing it further.', 'duration': 27.677, 'max_score': 14238.61, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5814238610.jpg'}, {'end': 14371.202, 'src': 'embed', 'start': 14345.612, 'weight': 2, 'content': [{'end': 14350.874, 'text': "And I'll talk about that in a minute so you can understand why we want to use a numpy array versus a standard Python array.", 'start': 14345.612, 'duration': 5.262}, {'end': 14354.895, 'text': "And normally it's pretty standard setup to use np for numpy.", 'start': 14351.074, 'duration': 3.821}, {'end': 14357.796, 'text': "The matplotlibrary is how we're going to view our data.", 'start': 14355.095, 'duration': 2.701}, {'end': 14365.438, 'text': 'So this has, you do need the np for the sklearn module, but the matplotlibrary is purely for our use for visualization.', 'start': 14358.076, 'duration': 7.362}, {'end': 14371.202, 'text': "And so you really don't need that for the SVM, but we're going to put it there so you have a nice visual aid and we can show you what it looks like.", 'start': 14365.858, 'duration': 5.344}], 'summary': 'Using numpy array vs python array for visualization in sklearn and svm.', 'duration': 25.59, 'max_score': 14345.612, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5814345612.jpg'}, {'end': 14486.517, 'src': 'embed', 'start': 14463.463, 'weight': 4, 'content': [{'end': 14470.647, 'text': 'SVC kernel equals linear and I set c equal to one, although in this example, since we are not regularizing the data,', 'start': 14463.463, 'duration': 7.184}, {'end': 14473.509, 'text': 'since we want to be very clear and easy to see, I went ahead.', 'start': 14470.647, 'duration': 2.862}, {'end': 14476.291, 'text': "you can set it to a thousand a lot of times when you're not doing that.", 'start': 14473.509, 'duration': 2.782}, {'end': 14483.495, 'text': "but for this thing linear, because it's a very simple linear example we only have the two dimensions and it'll be a nice linear hyperplane.", 'start': 14476.291, 'duration': 7.204}, {'end': 14486.517, 'text': "It'll be a nice linear line instead of a full plane.", 'start': 14483.755, 'duration': 2.762}], 'summary': 'Using linear svc kernel with c=1 for a simple 2d example.', 'duration': 23.054, 'max_score': 14463.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5814463463.jpg'}, {'end': 14742.847, 'src': 'embed', 'start': 14710.852, 'weight': 5, 'content': [{'end': 14713.553, 'text': "And then we're going to go in there and add in those lines.", 'start': 14710.852, 'duration': 2.701}, {'end': 14716.714, 'text': "We're going to see what those lines look like and how to set those up.", 'start': 14714.013, 'duration': 2.701}, {'end': 14719.867, 'text': "And finally, we're going to plot all that on here and show it.", 'start': 14717.324, 'duration': 2.543}, {'end': 14725.713, 'text': "And you'll get a nice graph with what we saw earlier when we were going through the theory behind this,", 'start': 14720.127, 'duration': 5.586}, {'end': 14729.337, 'text': 'where it shows the support vectors and the hyperplane.', 'start': 14725.713, 'duration': 3.624}, {'end': 14734.723, 'text': 'And those are done where you can see the support vectors as the dashed lines and the solid line, which is the hyperplane.', 'start': 14729.717, 'duration': 5.006}, {'end': 14737.164, 'text': "Let's get that into our Jupyter notebook.", 'start': 14735.003, 'duration': 2.161}, {'end': 14742.847, 'text': 'Before I scroll down to a new line, I want you to notice line 13.', 'start': 14737.804, 'duration': 5.043}], 'summary': 'Demonstrating how to plot support vectors and hyperplane on a graph in jupyter notebook.', 'duration': 31.995, 'max_score': 14710.852, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5814710852.jpg'}, {'end': 15151.619, 'src': 'embed', 'start': 15124.624, 'weight': 3, 'content': [{'end': 15130.807, 'text': 'And then you have your hyperplane down the middle, which is as far from the two different points as possible, creating the maximum distance.', 'start': 15124.624, 'duration': 6.183}, {'end': 15136.55, 'text': 'So you can see that we have our nice output for the size of the body and the width of the snout,', 'start': 15131.167, 'duration': 5.383}, {'end': 15139.871, 'text': "and we've easily separated the two groups of crocodile and alligator.", 'start': 15136.55, 'duration': 3.321}, {'end': 15141.752, 'text': "Congratulations, you've done it.", 'start': 15140.051, 'duration': 1.701}, {'end': 15142.452, 'text': "We've made it.", 'start': 15141.932, 'duration': 0.52}, {'end': 15146.335, 'text': 'Of course, these are pretend data for our crocodiles and alligators.', 'start': 15142.472, 'duration': 3.863}, {'end': 15151.619, 'text': 'But this hands-on example will help you to encounter any support vector machine projects in the future.', 'start': 15146.576, 'duration': 5.043}], 'summary': 'Support vector machine separates crocodile and alligator data for future projects.', 'duration': 26.995, 'max_score': 15124.624, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5815124624.jpg'}, {'end': 15186.166, 'src': 'embed', 'start': 15161.806, 'weight': 1, 'content': [{'end': 15169.811, 'text': 'Or how online news channels perform news text classification? Or how companies perform sentimental analysis of their audience on social media?', 'start': 15161.806, 'duration': 8.005}, {'end': 15175.554, 'text': 'All of this and more is done through a machine learning algorithm called Naive Bayes Classifier.', 'start': 15169.991, 'duration': 5.563}, {'end': 15177.797, 'text': 'Welcome to Naive Bayes Tutorial.', 'start': 15175.734, 'duration': 2.063}, {'end': 15179.358, 'text': 'My name is Richard Kirshner.', 'start': 15177.957, 'duration': 1.401}, {'end': 15181.16, 'text': "I'm with the Simply Learn team.", 'start': 15179.599, 'duration': 1.561}, {'end': 15184.084, 'text': "That's www.simplylearn.com.", 'start': 15181.581, 'duration': 2.503}, {'end': 15185.245, 'text': 'Get certified.', 'start': 15184.404, 'duration': 0.841}, {'end': 15186.166, 'text': 'Get ahead.', 'start': 15185.566, 'duration': 0.6}], 'summary': 'Naive bayes classifier used in online news and social media analysis.', 'duration': 24.36, 'max_score': 15161.806, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5815161806.jpg'}, {'end': 15286.334, 'src': 'embed', 'start': 15258.828, 'weight': 6, 'content': [{'end': 15263.65, 'text': 'But when you have something more complex, you can see where these formulas really come in and work.', 'start': 15258.828, 'duration': 4.822}, {'end': 15270.531, 'text': 'So the Bayes Theorem gives us the conditional probability of an event A given another event B has occurred.', 'start': 15263.97, 'duration': 6.561}, {'end': 15275.832, 'text': 'In this case, the first coin toss will be B and the second coin toss A.', 'start': 15270.912, 'duration': 4.92}, {'end': 15281.473, 'text': "This could be confusing because we've actually reversed the order of them and go from B to A instead of A to B.", 'start': 15275.832, 'duration': 5.641}, {'end': 15283.614, 'text': "You'll see this a lot when you work in probabilities.", 'start': 15281.473, 'duration': 2.141}, {'end': 15286.334, 'text': "The reason is we're looking for event A.", 'start': 15284.054, 'duration': 2.28}], 'summary': 'Bayes theorem calculates conditional probability in complex scenarios, as in coin tosses.', 'duration': 27.506, 'max_score': 15258.828, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5815258828.jpg'}, {'end': 15492.883, 'src': 'embed', 'start': 15465.649, 'weight': 7, 'content': [{'end': 15469.711, 'text': "Let's go ahead and glance into where is Naive Bay's use.", 'start': 15465.649, 'duration': 4.062}, {'end': 15471.652, 'text': "Let's look at some of the use scenarios for it.", 'start': 15469.791, 'duration': 1.861}, {'end': 15474.533, 'text': 'As a classifier, we use it in face recognition.', 'start': 15471.932, 'duration': 2.601}, {'end': 15477.114, 'text': 'Is this Cindy, or is it not Cindy or whoever?', 'start': 15474.773, 'duration': 2.341}, {'end': 15483.557, 'text': 'Or it might be used to identify parts of the face that they then feed into another part of the face recognition program.', 'start': 15477.635, 'duration': 5.922}, {'end': 15486.079, 'text': 'This is the eye, this is the nose, this is the mouth.', 'start': 15483.638, 'duration': 2.441}, {'end': 15487.119, 'text': 'Weather prediction.', 'start': 15486.419, 'duration': 0.7}, {'end': 15489.961, 'text': 'Is it going to be rainy or sunny? Medical recognition.', 'start': 15487.379, 'duration': 2.582}, {'end': 15490.861, 'text': 'News prediction.', 'start': 15490.141, 'duration': 0.72}, {'end': 15492.883, 'text': "It's also used in medical diagnosis.", 'start': 15491.001, 'duration': 1.882}], 'summary': 'Naive bayes used for face recognition, weather prediction, medical diagnosis, and news prediction.', 'duration': 27.234, 'max_score': 15465.649, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5815465649.jpg'}], 'start': 14202.443, 'title': 'Using support vector machine and naive bayes in python', 'summary': 'Introduces the concept of using support vector machine (svm) to segregate data into two groups in python, covering the setup and creation of data, with a focus on simplicity and speed. it also covers the use of bayes theorem with an example of tossing two coins to calculate conditional probabilities and delves into the understanding of naive bayes in machine learning.', 'chapters': [{'end': 14324.814, 'start': 14202.443, 'title': 'Introduction to support vector machine in python', 'summary': 'Introduces the concept of using support vector machine (svm) to segregate data into two groups in python, covering the setup and creation of data, with a focus on the simplicity and speed of the process, and the use of anaconda jupyter notebook for implementation.', 'duration': 122.371, 'highlights': ['The chapter introduces the concept of using support vector machine (SVM) to segregate data into two groups in Python. The chapter discusses the use of support vector machine (SVM) for data segregation, emphasizing its application in Python.', 'The setup for creating the SVM involves only two lines of code, making the process quick and fast. The setup for creating the support vector machine (SVM) in Python is highlighted for its simplicity and efficiency.', 'The chapter emphasizes the use of Anaconda Jupyter notebook for implementing the SVM, highlighting its ease of use and compatibility with Python 3. The use of Anaconda Jupyter notebook for implementing the support vector machine (SVM) is emphasized for its ease of use and compatibility with Python 3.']}, {'end': 14673.304, 'start': 14325.074, 'title': 'Python svm visualization and prediction', 'summary': 'Covers the use of numpy and matplotlibrary for data visualization, creating synthetic data with make_blobs, and implementing a support vector machine (svm) with sklearn, resulting in a simple linear hyperplane model and prediction of new data.', 'duration': 348.23, 'highlights': ['Creating synthetic data with make_blobs in SKLearn to generate 40 samples with two centers and random state 20 for SVM training. By using make_blobs, 40 samples of data with two centers and a random state of 20 are generated for SVM training.', 'Implementing SVM with sklearn, setting kernel to linear and C to 1, resulting in a simple linear hyperplane model. The SVM is implemented with sklearn, using a linear kernel and C set to 1, resulting in a simple linear hyperplane model.', "Visualizing the synthetic data and SVM model using matplotlibrary's scatter plot, with specific notation for numpy array data. The synthetic data and SVM model are visualized using matplotlibrary's scatter plot, utilizing specific notation for numpy array data."]}, {'end': 15206.325, 'start': 14673.444, 'title': 'Support vector machine insights', 'summary': 'Explores the detailed process of creating a graph using support vector machine, including steps such as data creation, setting up support vectors and hyperplane, and plotting, resulting in the clear separation of alligator and crocodile groups with a nice output.', 'duration': 532.881, 'highlights': ['The support vectors and hyperplane are plotted to create a graph showing the clear separation of alligator and crocodile groups. The process involves setting up support vectors and hyperplane, resulting in a clear separation of alligator and crocodile groups with a nice output.', 'The detailed process of creating a graph using support vector machine, including data creation, setting up support vectors and hyperplane, and plotting. The chapter delves into the detailed process of creating a graph using support vector machine, including steps such as data creation, setting up support vectors and hyperplane, and plotting.', 'Introduction to Naive Bayes Classifier and its applications in spam filtering, news text classification, and sentimental analysis on social media. The chapter introduces Naive Bayes Classifier and its applications in spam filtering, news text classification, and sentimental analysis on social media.']}, {'end': 15548.038, 'start': 15206.546, 'title': 'Bayes theorem and naive bayes', 'summary': 'Introduces bayes theorem, explaining its application with an example of tossing two coins to calculate conditional probabilities, and then delves into the use scenarios and understanding of naive bayes in machine learning.', 'duration': 341.492, 'highlights': ["Bayes Theorem and conditional probabilities are demonstrated with an example of tossing two coins, where the probability of getting two heads is one fourth and the probability of at least one tail is three quarters, providing a simple understanding of the theorem's application. probability of getting two heads: 1/4, probability of at least one tail: 3/4", 'The application of Bayes Theorem in more complex scenarios is emphasized, with the concept of conditional probability explained using the example of the second coin being a head given the first coin is a tail, showcasing the practical utility of the theorem in complex probability calculations. emphasis on application in complex scenarios, practical utility in complex probability calculations', 'The use scenarios for Naive Bayes in machine learning are outlined, including its application in face recognition, weather prediction, medical recognition, news prediction, and news classification, providing insights into its diverse real-world applications. use scenarios: face recognition, weather prediction, medical recognition, news prediction, news classification', 'The basic understanding of the Naive Bayes classifier is reinforced, emphasizing its foundation on the Bayes Theorem and the calculation of conditional probabilities, offering a quick review and reinforcement of the fundamental concepts. reinforcement of basic understanding, foundation on Bayes Theorem, calculation of conditional probabilities']}], 'duration': 1345.595, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5814202443.jpg', 'highlights': ['The setup for creating the SVM involves only two lines of code, making the process quick and fast.', 'The chapter introduces Naive Bayes Classifier and its applications in spam filtering, news text classification, and sentimental analysis on social media.', "The synthetic data and SVM model are visualized using matplotlibrary's scatter plot, utilizing specific notation for numpy array data.", 'The support vectors and hyperplane are plotted to create a graph showing the clear separation of alligator and crocodile groups.', 'The SVM is implemented with sklearn, using a linear kernel and C set to 1, resulting in a simple linear hyperplane model.', 'The chapter delves into the detailed process of creating a graph using support vector machine, including steps such as data creation, setting up support vectors and hyperplane, and plotting.', 'The application of Bayes Theorem in more complex scenarios is emphasized, with the concept of conditional probability explained using the example of the second coin being a head given the first coin is a tail, showcasing the practical utility of the theorem in complex probability calculations.', 'The use scenarios for Naive Bayes in machine learning are outlined, including its application in face recognition, weather prediction, medical recognition, news prediction, and news classification, providing insights into its diverse real-world applications.']}, {'end': 16462.952, 'segs': [{'end': 15610.785, 'src': 'embed', 'start': 15580.082, 'weight': 1, 'content': [{'end': 15581.743, 'text': "Are they going to buy or don't buy?", 'start': 15580.082, 'duration': 1.661}, {'end': 15583.504, 'text': "Very important if you're running a business,", 'start': 15582.063, 'duration': 1.441}, {'end': 15588.749, 'text': 'you want to know how to maximize your profits or at least maximize the purchase of the people coming into your store.', 'start': 15583.504, 'duration': 5.245}, {'end': 15592.692, 'text': "And we're going to look at a specific combination of different variables.", 'start': 15588.929, 'duration': 3.763}, {'end': 15596.435, 'text': "In this case, we're going to look at the day, the discount, and the free delivery.", 'start': 15592.792, 'duration': 3.643}, {'end': 15597.876, 'text': 'And you can see here under the day.', 'start': 15596.595, 'duration': 1.281}, {'end': 15603.32, 'text': "we want to know whether it's on the weekday, you know somebody's working, they come in after work or maybe they don't work.", 'start': 15597.876, 'duration': 5.444}, {'end': 15607.884, 'text': 'Weekend, you can see the bright colors coming down there, celebrating not being in work, or holiday.', 'start': 15603.661, 'duration': 4.223}, {'end': 15610.785, 'text': 'And did we offer a discount that day, yes or no??', 'start': 15608.164, 'duration': 2.621}], 'summary': 'Analyzing variables like day, discount, and delivery to maximize store purchases.', 'duration': 30.703, 'max_score': 15580.082, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5815580082.jpg'}, {'end': 15715.526, 'src': 'embed', 'start': 15687.78, 'weight': 2, 'content': [{'end': 15692.646, 'text': "discount and free delivery We're going to go ahead and populate that to frequency tables for each attribute.", 'start': 15687.78, 'duration': 4.866}, {'end': 15698.111, 'text': 'So we want to know if they had a discount, how many people buy and did not buy.', 'start': 15693.006, 'duration': 5.105}, {'end': 15699.893, 'text': 'Did they have a discount? Yes or no.', 'start': 15698.592, 'duration': 1.301}, {'end': 15701.955, 'text': 'Do we have a free delivery? Yes or no.', 'start': 15700.113, 'duration': 1.842}, {'end': 15707.021, 'text': "On those days, how many people made a purchase? How many people didn't? And the same with the three days of the week.", 'start': 15702.196, 'duration': 4.825}, {'end': 15710.164, 'text': 'Was it a weekday, a weekend, a holiday? And did they buy? Yes or no.', 'start': 15707.081, 'duration': 3.083}, {'end': 15715.526, 'text': 'As we dig in deeper to this table for our Bayes theorem, let the event buy be A.', 'start': 15710.464, 'duration': 5.062}], 'summary': 'Analyzing purchase behavior based on discount, free delivery, day of the week, and purchase frequency.', 'duration': 27.746, 'max_score': 15687.78, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5815687780.jpg'}, {'end': 15855.835, 'src': 'embed', 'start': 15825.204, 'weight': 3, 'content': [{'end': 15829.766, 'text': 'So when we look at that, probability of the weekday without a purchase is going to be .', 'start': 15825.204, 'duration': 4.562}, {'end': 15831.487, 'text': '33 or 33 percent.', 'start': 15829.766, 'duration': 1.721}, {'end': 15836.831, 'text': "Let's take a look at this, at different probabilities, and, based on this likelihood table,", 'start': 15832.028, 'duration': 4.803}, {'end': 15839.934, 'text': "let's go ahead and calculate conditional probabilities as below.", 'start': 15836.831, 'duration': 3.103}, {'end': 15845.432, 'text': 'The first three we just did, the probability of making a purchase on the weekday is 11 out of 30, or roughly 36 or 37%, .', 'start': 15840.215, 'duration': 5.217}, {'end': 15855.835, 'text': "367. The probability of not making a purchase at all, doesn't matter what day of the week, is roughly 0.2 or 20 percent,", 'start': 15845.432, 'duration': 10.403}], 'summary': 'Probability of weekday purchase is 37%, no purchase 20%.', 'duration': 30.631, 'max_score': 15825.204, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5815825204.jpg'}, {'end': 15975.783, 'src': 'embed', 'start': 15952.354, 'weight': 0, 'content': [{'end': 15961.178, 'text': 'so now we have our probabilities for a discount and whether the discount leads to a purchase or not, and the probability for free delivery.', 'start': 15952.354, 'duration': 8.824}, {'end': 15965.459, 'text': 'Does that lead to a purchase or not? And this is where it starts getting really exciting.', 'start': 15961.338, 'duration': 4.121}, {'end': 15972.922, 'text': 'Let us use these three likelihood tables to calculate whether a customer will purchase a product on a specific combination of day,', 'start': 15965.62, 'duration': 7.302}, {'end': 15975.783, 'text': 'discount and free delivery or not purchase.', 'start': 15972.922, 'duration': 2.861}], 'summary': 'Calculating customer purchase probability using discount and free delivery likelihood tables.', 'duration': 23.429, 'max_score': 15952.354, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5815952354.jpg'}], 'start': 15548.038, 'title': 'Predicting customer purchases', 'summary': 'Discusses applying probability and data analysis to a shopping demo problem to predict purchases based on specific variables. it emphasizes visualizing data, maximizing traits to increase sales, and using likelihood tables and conditional probability to predict customer purchases.', 'chapters': [{'end': 15613.186, 'start': 15548.038, 'title': 'Probability and data analysis', 'summary': 'Discusses applying probability and data analysis to a shopping demo problem statement, aiming to predict whether a person will purchase a product based on specific combinations of variables, such as day, discount, and free delivery.', 'duration': 65.148, 'highlights': ['The importance of predicting whether a person will purchase a product for maximizing profits or purchase, considering variables like day, discount, and free delivery.', 'Applying probability and data analysis to a shopping demo problem statement to predict purchasing behavior based on specific combinations of variables like day, discount, and free delivery.', 'Discussing the use of table form and Python to understand and solve the shopping demo problem statement through data analysis.']}, {'end': 15915.32, 'start': 15613.366, 'title': 'Maximizing sales through data analysis', 'summary': 'Discusses the process of analyzing a small sample dataset to calculate conditional probabilities for purchasing based on different variables, emphasizing the importance of visualizing data and maximizing traits to increase sales.', 'duration': 301.954, 'highlights': ["The importance of analyzing data to maximize traits and find the best system for increasing sales is emphasized, with a mention of a small sample dataset of 30 rows and the demonstration of the dataset's first 15 rows.", 'The process of populating frequency tables for each attribute, such as discounts, free delivery, and days of the week, to calculate conditional probabilities for purchasing, is explained, highlighting the simplicity of the dataset and its relevance in understanding buyer behavior.', 'Calculation of conditional probabilities for purchasing based on different variables, including the probability of making a purchase on the weekday, the probability of not making a purchase at all, and the probability of a weekday no purchase, is detailed, showcasing the step-by-step approach to deriving these probabilities.', 'The calculation of conditional probabilities using the likelihood table and the importance of visualizing the data to determine the probability of buying on a weekday versus not buying on a weekday is emphasized, concluding with the significance of these probabilities in influencing sales strategies.', 'The detailed calculations and formulas for conditional probabilities, including the probability of no purchase on a weekday, are illustrated, highlighting the process of calculating these probabilities both from the likelihood table and using the formula to derive the probability of not purchasing on a weekday.']}, {'end': 16462.952, 'start': 15915.32, 'title': 'Predicting customer purchases', 'summary': 'Discusses using likelihood tables and conditional probability to predict customer purchases based on factors like day, discount, and free delivery, and concludes with the advantages of using naive bayes classifier for predictions.', 'duration': 547.632, 'highlights': ['Calculating conditional probabilities for customer purchases based on day, discount, and free delivery The chapter outlines the process of using likelihood tables and conditional probability to calculate the probability of customer purchases based on specific combinations of day, discount, and free delivery, resulting in a 84.71% likelihood of purchase for a specific combination.', 'Advantages of Naive Bayes classifier The advantages of using Naive Bayes classifier are discussed, including its simplicity, ability to handle small training data, scalability, real-time prediction capability, and insensitivity to irrelevant features.']}], 'duration': 914.914, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5815548038.jpg', 'highlights': ['Applying probability and data analysis to predict purchasing behavior based on specific combinations of variables like day, discount, and free delivery.', 'The importance of predicting whether a person will purchase a product for maximizing profits or purchase, considering variables like day, discount, and free delivery.', 'The process of populating frequency tables for each attribute, such as discounts, free delivery, and days of the week, to calculate conditional probabilities for purchasing, is explained.', 'The calculation of conditional probabilities using the likelihood table and the importance of visualizing the data to determine the probability of buying on a weekday versus not buying on a weekday is emphasized.', 'The chapter outlines the process of using likelihood tables and conditional probability to calculate the probability of customer purchases based on specific combinations of day, discount, and free delivery, resulting in a 84.71% likelihood of purchase for a specific combination.']}, {'end': 17771.195, 'segs': [{'end': 17131.407, 'src': 'embed', 'start': 17100.941, 'weight': 1, 'content': [{'end': 17106.305, 'text': "And what's happening here is the trained data is going into the TFID vectorizer.", 'start': 17100.941, 'duration': 5.364}, {'end': 17111.229, 'text': 'So when you have one of these articles, it goes in there, it weights all the words in there.', 'start': 17106.705, 'duration': 4.524}, {'end': 17113.891, 'text': "So there's thousands of words with different weights on them.", 'start': 17111.329, 'duration': 2.562}, {'end': 17119.555, 'text': 'I remember once running a model on this and I literally had 2.4 million tokens go into this.', 'start': 17113.951, 'duration': 5.604}, {'end': 17124.84, 'text': "So when you're dealing with large document bases, you can have a huge number of different words.", 'start': 17120.316, 'duration': 4.524}, {'end': 17131.407, 'text': 'It then takes those words, gives them a weight and then, based on that weight, based on the words and the weights,', 'start': 17125.281, 'duration': 6.126}], 'summary': 'Tfid vectorizer processes 2.4m tokens from large document bases.', 'duration': 30.466, 'max_score': 17100.941, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5817100941.jpg'}, {'end': 17220.706, 'src': 'embed', 'start': 17195.385, 'weight': 3, 'content': [{'end': 17202.051, 'text': 'This is just training our model, creating the labels so we can see how good it is, and then we move on to the next step to find out what happened.', 'start': 17195.385, 'duration': 6.666}, {'end': 17207.075, 'text': "To do this, we're going to go ahead and create a confusion matrix and a heat map.", 'start': 17202.591, 'duration': 4.484}, {'end': 17216.042, 'text': 'The confusion matrix, which is confusing just by its very name, is basically going to ask how confused is our answer?', 'start': 17208.596, 'duration': 7.446}, {'end': 17220.706, 'text': 'Did it get it correct or did it miss some things in there or have some missed labels?', 'start': 17216.283, 'duration': 4.423}], 'summary': 'Training model, creating labels, evaluating with confusion matrix and heat map.', 'duration': 25.321, 'max_score': 17195.385, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5817195385.jpg'}, {'end': 17644.248, 'src': 'embed', 'start': 17614.92, 'weight': 0, 'content': [{'end': 17622.565, 'text': 'We were able to correctly classify texts into different groups based on which category they belong to using the Naive Bayes classifier.', 'start': 17614.92, 'duration': 7.645}, {'end': 17629.077, 'text': 'Now, we did throw in the pipeline, the TF-IDF vectorizer, we threw in the graphs.', 'start': 17622.813, 'duration': 6.264}, {'end': 17636.542, 'text': "Those are all things that you don't necessarily have to know to understand the Naive Bayes setup or classifier, but they're important to know.", 'start': 17629.137, 'duration': 7.405}, {'end': 17644.248, 'text': 'One of the main uses for the Naive Bayes is with the TF-IDF tokenizer or vectorizer, where it tokenizes a word and adds labels.', 'start': 17636.703, 'duration': 7.545}], 'summary': 'Naive bayes classifier accurately classifies texts into groups using tf-idf vectorizer.', 'duration': 29.328, 'max_score': 17614.92, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5817614920.jpg'}, {'end': 17687.942, 'src': 'embed', 'start': 17657.056, 'weight': 4, 'content': [{'end': 17661.259, 'text': 'And we can see our categorizer, our Naive Bayes classifier.', 'start': 17657.056, 'duration': 4.203}, {'end': 17669.044, 'text': 'We were able to predict the category religion, space motorcycles, autos, politics and properly classify all these different things.', 'start': 17661.459, 'duration': 7.585}, {'end': 17671.786, 'text': 'we pushed into our prediction and our trained model.', 'start': 17669.044, 'duration': 2.742}, {'end': 17673.347, 'text': 'Thanks again, Richard.', 'start': 17672.327, 'duration': 1.02}, {'end': 17674.288, 'text': 'That was great.', 'start': 17673.687, 'duration': 0.601}, {'end': 17679.699, 'text': 'That brings us to the end of this comprehensive look at machine learning and its algorithms.', 'start': 17675.069, 'duration': 4.63}, {'end': 17687.942, 'text': 'Now, we have Rahul who will take you through the various applications of machine learning and how you can be a machine learning engineer.', 'start': 17680.498, 'duration': 7.444}], 'summary': 'Naive bayes classifier accurately predicts and categorizes religion, space, motorcycles, autos, and politics. comprehensive overview of machine learning and its algorithms, followed by discussion on applications and becoming a machine learning engineer.', 'duration': 30.886, 'max_score': 17657.056, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5817657056.jpg'}], 'start': 16463.152, 'title': 'Text classification and model evaluation', 'summary': 'Covers the application of naive bayes classifier in text classification, using tf-idf vectorizer and multinomial naive bayes model to process 2.4 million tokens, creating confusion matrix and heatmap for model evaluation, and achieving good overall accuracy in predicting categories.', 'chapters': [{'end': 16555.557, 'start': 16463.152, 'title': 'Text classification with naive bayes', 'summary': 'Discusses the application of naive bayes classifier in performing text classification for news headlines, demonstrating the process of training the data, creating a graph, and exploring the usage, while also encouraging audience involvement in obtaining and implementing the data for a shopping cart.', 'duration': 92.405, 'highlights': ['Demonstrating the process of training the data, creating a graph, and exploring the usage The chapter covers the process of training the data, creating a graph, and exploring the usage of the Naive Bayes classifier for text classification.', 'Encouraging audience involvement in obtaining and implementing the data for a shopping cart The audience is encouraged to request and use data for a shopping cart in Python code to walk through the information discussed.', 'Introduction to using the Naive Bayes classifier for text classification of news headlines The chapter introduces the use of the Naive Bayes classifier for text classification of news headlines, demonstrating its application in classifying news into different topics for a news website.']}, {'end': 17007.92, 'start': 16555.737, 'title': 'Python data analysis and visualization', 'summary': 'Covers setting up python environment for data analysis, importing necessary packages for data visualization, and using tf-idf vectorizer to weigh the words in a document for analysis and prediction.', 'duration': 452.183, 'highlights': ['The tf-idf vectorizer is imported from sklearn.featuresextraction.txt, which weighs the words based on their usage in a document and across documents, aiding in data analysis and prediction.', 'The process of setting up the Python environment for data analysis and visualization is explained, including importing necessary packages like matplotlib, numpy, and seaborn for graphing and data visualization.', 'The chapter delves into using Jupyter notebook for data analysis and visualization, including importing modules like matplotlibrary, numpy, and seaborn for graphing and setting up data for analysis and prediction.', 'The transcript also covers the importance of visualizing data in data analysis, emphasizing the significance of graphs in presenting data effectively to the audience.']}, {'end': 17381.238, 'start': 17008.38, 'title': 'Text classification and model evaluation', 'summary': 'Explains the process of using tf-idf vectorizer and multinomial naive bayes model to classify text data, with a mention of processing 2.4 million tokens and creating a confusion matrix and heatmap for model evaluation.', 'duration': 372.858, 'highlights': ['The TF-IDF vectorizer is used to weigh words in different articles, with a mention of processing 2.4 million tokens.', 'The process involves pumping the TF-IDF vectorized data into the multinomial Naive Bayes model for classification.', "A confusion matrix and heatmap are generated to evaluate the model's performance with new test data."]}, {'end': 17771.195, 'start': 17381.358, 'title': 'Naive bayes classifier in machine learning', 'summary': 'Demonstrates the use of a naive bayes classifier to predict categories, achieving a good overall accuracy, while detailing the process of creating a function to run predictions, and showcases the successful classification of various topics, including space, automobiles, and politics.', 'duration': 389.837, 'highlights': ['The Naive Bayes classifier successfully predicts categories with a good overall accuracy, although it mislabels some very similar topics, such as social religion miscellaneous versus talk, politics miscellaneous, with a couple of red spots indicating missed predictions.', 'The process of creating a function to run predictions is explained, which involves sending a string as input, utilizing the training model and pipeline model to avoid resending variables each time, and converting the prediction from a number to an actual category based on the train target names.', "The successful classification of various topics is showcased, including predicting 'social, religion, Christian' for 'Jesus Christ', 'science space' for 'International Space Station', 'recreational autos' for 'BMW is better than an Audi', and 'talk politics miscellaneous' for 'President of India', demonstrating the effectiveness of the Naive Bayes classifier in categorizing texts into different groups based on their categories."]}], 'duration': 1308.043, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5816463151.jpg', 'highlights': ['The chapter covers the process of training the data, creating a graph, and exploring the usage of the Naive Bayes classifier for text classification.', 'The tf-idf vectorizer is imported from sklearn.featuresextraction.txt, which weighs the words based on their usage in a document and across documents, aiding in data analysis and prediction.', 'The TF-IDF vectorizer is used to weigh words in different articles, with a mention of processing 2.4 million tokens.', "A confusion matrix and heatmap are generated to evaluate the model's performance with new test data.", 'The Naive Bayes classifier successfully predicts categories with a good overall accuracy, although it mislabels some very similar topics, such as social religion miscellaneous versus talk, politics miscellaneous, with a couple of red spots indicating missed predictions.']}, {'end': 18748.681, 'segs': [{'end': 17816.172, 'src': 'embed', 'start': 17789.847, 'weight': 0, 'content': [{'end': 17795.213, 'text': 'So how exactly is Google able to tell you that the traffic is clear, slow-moving or heavily congested?', 'start': 17789.847, 'duration': 5.366}, {'end': 17798.997, 'text': 'So this is with the help of machine learning and with the help of two important measures.', 'start': 17795.413, 'duration': 3.584}, {'end': 17803.883, 'text': "First is the average time that's taken on specific days at specific times on that route.", 'start': 17799.318, 'duration': 4.565}, {'end': 17808.949, 'text': 'The second one is the real-time location data of vehicles from Google Maps and with the help of sensors.', 'start': 17804.083, 'duration': 4.866}, {'end': 17813.671, 'text': 'Some of the other popular map services are Bing Maps, Maps.me and here we go.', 'start': 17809.409, 'duration': 4.262}, {'end': 17816.172, 'text': 'Next up, we have social media personalization.', 'start': 17813.971, 'duration': 2.201}], 'summary': 'Google uses machine learning and real-time vehicle data to provide traffic updates. other map services include bing maps, maps.me, and here we go.', 'duration': 26.325, 'max_score': 17789.847, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5817789847.jpg'}, {'end': 17918.48, 'src': 'embed', 'start': 17895.126, 'weight': 8, 'content': [{'end': 17902.931, 'text': 'So what happens with feed forward neural networks are that the outputs are converted into hash values and these values become the inputs for the next round.', 'start': 17895.126, 'duration': 7.805}, {'end': 17906.373, 'text': "So for every real transaction that takes place, there's a specific pattern.", 'start': 17903.091, 'duration': 3.282}, {'end': 17911.315, 'text': 'A fraudulent transaction would stand out because of the significant changes that it would cause with the hash values.', 'start': 17906.533, 'duration': 4.782}, {'end': 17912.496, 'text': 'Stock market trading.', 'start': 17911.556, 'duration': 0.94}, {'end': 17916.238, 'text': 'Machine learning is used extensively when it comes to stock market trading.', 'start': 17912.776, 'duration': 3.462}, {'end': 17918.48, 'text': 'Now you have stock market indices like Nikkei.', 'start': 17916.458, 'duration': 2.022}], 'summary': 'Feed forward neural networks use hash values to detect fraudulent transactions and are also used in stock market trading, such as with the nikkei index.', 'duration': 23.354, 'max_score': 17895.126, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5817895126.jpg'}, {'end': 17977.861, 'src': 'embed', 'start': 17930.788, 'weight': 1, 'content': [{'end': 17933.13, 'text': 'Now, medical technology has been innovated.', 'start': 17930.788, 'duration': 2.342}, {'end': 17936.353, 'text': 'With the help of machine learning, diagnosing diseases has been easier.', 'start': 17933.27, 'duration': 3.083}, {'end': 17940.956, 'text': 'From which we can create 3D models that can predict where exactly there are lesions in the brain.', 'start': 17936.513, 'duration': 4.443}, {'end': 17944.259, 'text': 'It works just as well for brain tumors and isochemic stroke lesions.', 'start': 17941.116, 'duration': 3.143}, {'end': 17947.141, 'text': 'They can also be used in fetal imaging and cardiac analysis.', 'start': 17944.419, 'duration': 2.722}, {'end': 17954.305, 'text': 'Now, some of the medical fields that machine learning will help assist in is disease identification, personalized treatment, drug discovery,', 'start': 17947.441, 'duration': 6.864}, {'end': 17956.006, 'text': 'clinical research and radiology.', 'start': 17954.305, 'duration': 1.701}, {'end': 17958.188, 'text': 'And finally, we have automatic translation.', 'start': 17956.266, 'duration': 1.922}, {'end': 17961.91, 'text': "Now, say you're in a foreign country and you see billboards and signs that you don't understand.", 'start': 17958.308, 'duration': 3.602}, {'end': 17963.851, 'text': "That's where automatic translation comes of help.", 'start': 17961.99, 'duration': 1.861}, {'end': 17966.493, 'text': 'Now, how does automatic translation actually work?', 'start': 17964.051, 'duration': 2.442}, {'end': 17971.457, 'text': "The technology behind it is the same as the sequence to sequence learning, which is the same thing that's used with chatbots.", 'start': 17966.613, 'duration': 4.844}, {'end': 17977.861, 'text': 'Here the image recognition happens using convolutional neural networks and the text is identified using optical character recognition.', 'start': 17971.617, 'duration': 6.244}], 'summary': 'Medical technology with machine learning aids disease diagnosis, 3d modeling, and translation.', 'duration': 47.073, 'max_score': 17930.788, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5817930788.jpg'}, {'end': 18041.08, 'src': 'embed', 'start': 18016.917, 'weight': 9, 'content': [{'end': 18022.903, 'text': "Now before you can start off on your journey to becoming a machine learning engineer, there's a certain number of steps that you need to follow.", 'start': 18016.917, 'duration': 5.986}, {'end': 18026.967, 'text': 'On this learning path, your first step is to improve your math skills.', 'start': 18023.303, 'duration': 3.664}, {'end': 18033.032, 'text': 'Mathematics plays a very important role in helping you understand how machine learning and its algorithms work.', 'start': 18027.407, 'duration': 5.625}, {'end': 18041.08, 'text': 'Among the many concepts that you need to understand, three of the most important ones are probability and statistics, linear algebra and calculus.', 'start': 18033.332, 'duration': 7.748}], 'summary': 'To become a machine learning engineer, start by improving math skills in probability, statistics, linear algebra, and calculus.', 'duration': 24.163, 'max_score': 18016.917, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5818016917.jpg'}, {'end': 18155.704, 'src': 'embed', 'start': 18129.027, 'weight': 5, 'content': [{'end': 18133.088, 'text': 'And you can see that there are two languages that dominate since 2015.', 'start': 18129.027, 'duration': 4.061}, {'end': 18134.249, 'text': 'Python and R.', 'start': 18133.088, 'duration': 1.161}, {'end': 18138.311, 'text': 'Now these are one of the most wanted languages when it comes to machine learning engineers.', 'start': 18134.249, 'duration': 4.062}, {'end': 18141.392, 'text': 'These are closely followed by JavaScript and C.', 'start': 18138.511, 'duration': 2.881}, {'end': 18147.695, 'text': 'Hence we would like to recommend that you learn Python and R as they are the best option when it comes to coding in machine learning algorithms.', 'start': 18141.392, 'duration': 6.303}, {'end': 18151.199, 'text': 'So here are a few things that you need to know about Python and R.', 'start': 18148.015, 'duration': 3.184}, {'end': 18155.704, 'text': 'Python is an object-oriented language which means their main emphasis is on the object.', 'start': 18151.199, 'duration': 4.505}], 'summary': 'Python and r dominate in machine learning since 2015, followed by javascript and c. python is object-oriented.', 'duration': 26.677, 'max_score': 18129.027, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5818129027.jpg'}, {'end': 18309.594, 'src': 'embed', 'start': 18283.635, 'weight': 6, 'content': [{'end': 18288.958, 'text': 'Association is used to determine patterns of association among variables in large data sets.', 'start': 18283.635, 'duration': 5.323}, {'end': 18293.165, 'text': 'So now that you know about these algorithms, let me tell you where you can learn about them.', 'start': 18289.238, 'duration': 3.927}, {'end': 18295.51, 'text': "Let's take a look at our Simply Learn channel.", 'start': 18293.566, 'duration': 1.944}, {'end': 18297.193, 'text': "And let's go to playlists.", 'start': 18295.991, 'duration': 1.202}, {'end': 18301.982, 'text': 'And on this, we have a dedicated set of playlists that talk about machine learning.', 'start': 18297.694, 'duration': 4.288}, {'end': 18309.594, 'text': 'Here you have videos on machine learning,', 'start': 18307.392, 'duration': 2.202}], 'summary': 'Association used to find patterns in data sets. simply learn offers machine learning tutorials.', 'duration': 25.959, 'max_score': 18283.635, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5818283635.jpg'}, {'end': 18414.99, 'src': 'embed', 'start': 18388.791, 'weight': 7, 'content': [{'end': 18392.954, 'text': "There's TensorFlow, Theano, Torch, scikit-learn and so on.", 'start': 18388.791, 'duration': 4.163}, {'end': 18394.995, 'text': "Now let's look at some of them in detail.", 'start': 18393.354, 'duration': 1.641}, {'end': 18396.797, 'text': "First let's look at TensorFlow.", 'start': 18395.235, 'duration': 1.562}, {'end': 18400.299, 'text': 'TensorFlow is the most widely used machine learning framework.', 'start': 18396.977, 'duration': 3.322}, {'end': 18402.821, 'text': "It's used for machine learning as well as deep learning.", 'start': 18400.499, 'duration': 2.322}, {'end': 18409.366, 'text': "Now it's an open-source software library which performs numerical computations which is done with the help of data flow graphs.", 'start': 18403.081, 'duration': 6.285}, {'end': 18413.449, 'text': 'Google Translate is one of the most popular use cases of TensorFlow.', 'start': 18409.686, 'duration': 3.763}, {'end': 18414.99, 'text': "Now let's look at Theano.", 'start': 18413.729, 'duration': 1.261}], 'summary': 'Tensorflow is the most widely used ml framework, with google translate as a popular use case.', 'duration': 26.199, 'max_score': 18388.791, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5818388791.jpg'}, {'end': 18510.87, 'src': 'embed', 'start': 18471.976, 'weight': 4, 'content': [{'end': 18476.84, 'text': 'Before 2015, machine learning was much less popular than big data and cloud computing.', 'start': 18471.976, 'duration': 4.864}, {'end': 18478.522, 'text': 'But all of that suddenly changed.', 'start': 18477.16, 'duration': 1.362}, {'end': 18483.565, 'text': 'And right now, a machine learning engineer earns around $114, 000 per annum.', 'start': 18478.842, 'duration': 4.723}, {'end': 18490.871, 'text': 'And this is a clear indication that organizations are ready to invest heavily in people who are skilled in the concepts of machine learning.', 'start': 18483.766, 'duration': 7.105}, {'end': 18495.935, 'text': "You can also make the learning process easier by using Simply Learn's machine learning certification.", 'start': 18491.291, 'duration': 4.644}, {'end': 18510.87, 'text': 'Simply Learns Machine Learning certification course, provides 36 hours of instructor-led training, provides 25 plus hands-on exercises,', 'start': 18503.28, 'duration': 7.59}], 'summary': 'Machine learning engineer earns around $114,000 per annum, indicating heavy investment in skilled individuals.', 'duration': 38.894, 'max_score': 18471.976, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5818471976.jpg'}], 'start': 17771.355, 'title': 'Machine learning applications', 'summary': 'Explores google maps traffic analysis using machine learning for traffic congestion identification, and discusses various applications of machine learning including personalized advertising, spam filtering, online fraud detection, stock market trading, medical technology, and automatic translation. it also emphasizes the importance of acquiring python and r skills, data engineering, understanding machine learning algorithms, and the increase in job opportunities and earnings in the field.', 'chapters': [{'end': 17827.317, 'start': 17771.355, 'title': 'Google maps traffic analysis', 'summary': 'Explains how google maps uses machine learning and real-time location data to analyze traffic congestion, with red indicating heavily congested, yellow for slow moving, and blue for clear roads, while also discussing the use of social media personalization in advertising.', 'duration': 55.962, 'highlights': ['Google uses machine learning and real-time location data to analyze traffic congestion, with red indicating heavily congested, yellow for slow moving, and blue for clear roads, based on average time taken and real-time vehicle location data from Google Maps.', "The chapter discusses the impact of social media personalization in advertising, where a user's search on one platform leads to targeted ads on another platform."]}, {'end': 18106.469, 'start': 17827.617, 'title': 'Applications of machine learning', 'summary': 'Discusses the applications of machine learning, including personalized advertising, spam filtering, online fraud detection, stock market trading, medical technology, automatic translation, and the steps to become a machine learning engineer, emphasizing the importance of mathematics skills such as probability and statistics, linear algebra, and calculus.', 'duration': 278.852, 'highlights': ["Machine learning's role in personalized advertising, spam filtering, online fraud detection, stock market trading, medical technology, automatic translation, and the steps to become a machine learning engineer. Machine learning applications across various domains", 'The use of machine learning in personalized advertising on platforms like YouTube and Instagram, and email spam filtering by Gmail through analyzing labeled spam or not spam emails, and employing spam filters like content, header, and general blacklist filters. Personalized advertising and email spam filtering', "Online fraud detection utilizing feed-forward neural networks to distinguish genuine transactions from fraudulent ones, with hash values used for pattern identification, and machine learning's extensive use in stock market trading, particularly with long short-term memory neural networks to predict stock market trends. Online fraud detection and stock market trading", 'The application of machine learning in medical technology for diagnosing diseases, creating 3D models to predict brain lesions, assisting in disease identification, personalized treatment, drug discovery, clinical research, radiology, fetal imaging, and cardiac analysis. Medical technology and disease diagnosis', 'The technology behind automatic translation, involving sequence to sequence learning, image recognition using convolutional neural networks, text identification via optical character recognition, and the translation process utilizing the sequence to sequence algorithm. Automatic translation technology', 'The steps to become a machine learning engineer, involving the improvement of math skills, understanding probability and statistics, linear algebra, and calculus, and their specific applications in machine learning. Steps to become a machine learning engineer and importance of mathematics skills']}, {'end': 18748.681, 'start': 18106.689, 'title': 'Becoming a machine learning engineer', 'summary': 'Emphasizes the importance of learning python and r as the dominant languages in machine learning, acquiring data engineering skills, understanding machine learning algorithms, exploring machine learning frameworks, and highlights the significant increase in job opportunities and potential earnings in the field of machine learning.', 'duration': 641.992, 'highlights': ["Python and R are recommended as the best languages for coding in machine learning algorithms, with Python being generic and suitable for integration with other software, while R works closely with statistical analysis and is slightly faster than Python due to inbuilt packages. Python and R recommended for machine learning, Python's generic nature, R's emphasis on statistical analysis, R being slightly faster than Python", 'Data engineering skills are essential for analyzing and processing data, involving data pre-processing, ETL (extract, transform, load) processes, and knowledge about database management software such as MySQL, Oracle, and NoSQL. Importance of data engineering skills, data pre-processing steps, ETL processes, knowledge about database management software', "Understanding various machine learning algorithms, including supervised and unsupervised learning, classification and regression algorithms, clustering, and association algorithms, and where to learn about them, such as Simply Learn's dedicated playlists. Overview of machine learning algorithms, supervised and unsupervised learning, classification and regression algorithms, clustering and association algorithms, learning resources at Simply Learn", "Overview of popular machine learning frameworks like TensorFlow, Theano, Spark ML library, and scikit-learn, including their features and applications, with TensorFlow being the most widely used framework and its use case in Google Translate. Overview of popular machine learning frameworks, features and applications, TensorFlow as the most widely used framework, TensorFlow's use case in Google Translate", 'Significant increase in job opportunities and potential earnings in the field of machine learning, with machine learning engineers earning around $114,000 per annum and the availability of machine learning certification courses. Increase in job opportunities, potential earnings for machine learning engineers, availability of machine learning certification courses']}], 'duration': 977.326, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5817771355.jpg', 'highlights': ['Google uses machine learning and real-time location data to analyze traffic congestion, with red indicating heavily congested, yellow for slow moving, and blue for clear roads, based on average time taken and real-time vehicle location data from Google Maps.', "Machine learning's role in personalized advertising, spam filtering, online fraud detection, stock market trading, medical technology, automatic translation, and the steps to become a machine learning engineer. Machine learning applications across various domains", 'The application of machine learning in medical technology for diagnosing diseases, creating 3D models to predict brain lesions, assisting in disease identification, personalized treatment, drug discovery, clinical research, radiology, fetal imaging, and cardiac analysis. Medical technology and disease diagnosis', 'The technology behind automatic translation, involving sequence to sequence learning, image recognition using convolutional neural networks, text identification via optical character recognition, and the translation process utilizing the sequence to sequence algorithm. Automatic translation technology', 'Significant increase in job opportunities and potential earnings in the field of machine learning, with machine learning engineers earning around $114,000 per annum and the availability of machine learning certification courses', "Python and R are recommended as the best languages for coding in machine learning algorithms, with Python being generic and suitable for integration with other software, while R works closely with statistical analysis and is slightly faster than Python due to inbuilt packages. Python and R recommended for machine learning, Python's generic nature, R's emphasis on statistical analysis, R being slightly faster than Python", "Understanding various machine learning algorithms, including supervised and unsupervised learning, classification and regression algorithms, clustering, and association algorithms, and where to learn about them, such as Simply Learn's dedicated playlists. Overview of machine learning algorithms, supervised and unsupervised learning, classification and regression algorithms, clustering and association algorithms, learning resources at Simply Learn", "Overview of popular machine learning frameworks like TensorFlow, Theano, Spark ML library, and scikit-learn, including their features and applications, with TensorFlow being the most widely used framework and its use case in Google Translate. Overview of popular machine learning frameworks, features and applications, TensorFlow as the most widely used framework, TensorFlow's use case in Google Translate", "Online fraud detection utilizing feed-forward neural networks to distinguish genuine transactions from fraudulent ones, with hash values used for pattern identification, and machine learning's extensive use in stock market trading, particularly with long short-term memory neural networks to predict stock market trends. Online fraud detection and stock market trading", 'The steps to become a machine learning engineer, involving the improvement of math skills, understanding probability and statistics, linear algebra, and calculus, and their specific applications in machine learning. Steps to become a machine learning engineer and importance of mathematics skills']}, {'end': 21084.729, 'segs': [{'end': 18861.375, 'src': 'embed', 'start': 18808.173, 'weight': 0, 'content': [{'end': 18817.116, 'text': 'And this is used primarily or very, very impactful for teaching the system to learn games and so on.', 'start': 18808.173, 'duration': 8.943}, {'end': 18820.377, 'text': 'Examples of this are basically used in AlphaGo.', 'start': 18817.456, 'duration': 2.921}, {'end': 18824.798, 'text': 'You can throw that as an example where AlphaGo used reinforcement.', 'start': 18820.577, 'duration': 4.221}, {'end': 18832.421, 'text': 'and learning to actually learn to play the game of Go and finally it defeated the Go world champion, right?', 'start': 18824.898, 'duration': 7.523}, {'end': 18835.222, 'text': 'This much of information that would be good enough, okay?', 'start': 18832.461, 'duration': 2.761}, {'end': 18839.644, 'text': 'Then there could be a question on overfitting.', 'start': 18835.563, 'duration': 4.081}, {'end': 18843.826, 'text': 'So the question could be what is overfitting and how can you avoid it?', 'start': 18839.664, 'duration': 4.162}, {'end': 18846.687, 'text': 'So what is overfitting?', 'start': 18844.346, 'duration': 2.341}, {'end': 18852.71, 'text': "Let's first try to understand the concept, because sometimes overfitting may be a little difficult to understand.", 'start': 18846.887, 'duration': 5.823}, {'end': 18858.473, 'text': 'Overfitting is a situation where the model has kind of memorized the data.', 'start': 18852.81, 'duration': 5.663}, {'end': 18861.375, 'text': 'So this is an equivalent of memorizing the data.', 'start': 18858.573, 'duration': 2.802}], 'summary': 'Reinforcement learning used in alphago to defeat world champion in go, overfitting defined as model memorizing data.', 'duration': 53.202, 'max_score': 18808.173, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5818808173.jpg'}, {'end': 19238.22, 'src': 'embed', 'start': 19215.606, 'weight': 3, 'content': [{'end': 19226.311, 'text': 'we set aside a portion of that data and we call that test set, and the remaining we call as training set, and we use only this for training our model.', 'start': 19215.606, 'duration': 10.705}, {'end': 19232.555, 'text': 'Now, the training process, remember, is not just about passing one round of this data set.', 'start': 19226.411, 'duration': 6.144}, {'end': 19235.338, 'text': "so let's say now your training set has 800 records.", 'start': 19232.555, 'duration': 2.783}, {'end': 19238.22, 'text': 'it is not just one time you pass this 800 records.', 'start': 19235.338, 'duration': 2.882}], 'summary': 'Data is divided into training set and test set. training process involves multiple rounds on 800 records.', 'duration': 22.614, 'max_score': 19215.606, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5819215606.jpg'}, {'end': 19317.314, 'src': 'embed', 'start': 19292.558, 'weight': 2, 'content': [{'end': 19298.642, 'text': 'And if it is able to accurately predict the values, that means your training has worked.', 'start': 19292.558, 'duration': 6.084}, {'end': 19300.543, 'text': 'The model got trained properly.', 'start': 19299.122, 'duration': 1.421}, {'end': 19304.906, 'text': "But let's say while you're testing this with this test data, you're getting a lot of errors.", 'start': 19300.743, 'duration': 4.163}, {'end': 19310.47, 'text': 'That means you need to probably either change your model or retrain with more data and things like that.', 'start': 19305.066, 'duration': 5.404}, {'end': 19317.314, 'text': 'Now, coming back to the question of how do you split this? What should be the ratio? There is no fixed number.', 'start': 19310.83, 'duration': 6.484}], 'summary': 'Accurate prediction indicates successful training, while high errors suggest need for model change or more data.', 'duration': 24.756, 'max_score': 19292.558, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5819292558.jpg'}, {'end': 19419.798, 'src': 'embed', 'start': 19391.598, 'weight': 4, 'content': [{'end': 19397.463, 'text': 'you can illustrate with examples saying that I was on one project where I received this kind of data.', 'start': 19391.598, 'duration': 5.865}, {'end': 19404.688, 'text': 'These were the columns where data was not filled or these were the this many rows where the data was missing.', 'start': 19397.483, 'duration': 7.205}, {'end': 19408.65, 'text': 'That would be in fact a perfect way to respond to this question.', 'start': 19405.068, 'duration': 3.582}, {'end': 19412.893, 'text': "But if you don't have that obviously you have to provide some good answer.", 'start': 19408.73, 'duration': 4.163}, {'end': 19419.798, 'text': 'I think it really depends on what exactly the situation is and there are multiple ways of handling the missing data or corrupt data.', 'start': 19412.973, 'duration': 6.825}], 'summary': 'Using examples and quantifiable data, address missing or corrupt data in a project, offering multiple ways to handle the situation.', 'duration': 28.2, 'max_score': 19391.598, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5819391598.jpg'}, {'end': 19602.358, 'src': 'embed', 'start': 19573.006, 'weight': 5, 'content': [{'end': 19575.547, 'text': 'Otherwise, this is how you need to handle this question.', 'start': 19573.006, 'duration': 2.541}, {'end': 19577.388, 'text': 'Okay, Okay, so.', 'start': 19575.827, 'duration': 1.561}, {'end': 19583.63, 'text': 'then the next question can be how can you choose a classifier based on a training set data size?', 'start': 19577.388, 'duration': 6.242}, {'end': 19590.753, 'text': 'So again, this is one of those questions where you probably do not have like a one size fits all answer.', 'start': 19584.11, 'duration': 6.643}, {'end': 19598.376, 'text': "First of all, you may not, let's say, decide your classifier based on the training set size.", 'start': 19590.813, 'duration': 7.563}, {'end': 19602.358, 'text': 'Maybe not the best way to decide the type of the classifier.', 'start': 19598.696, 'duration': 3.662}], 'summary': 'Choosing a classifier based on training set size may not be the best approach.', 'duration': 29.352, 'max_score': 19573.006, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5819573006.jpg'}, {'end': 19718.744, 'src': 'embed', 'start': 19688.519, 'weight': 6, 'content': [{'end': 19693.042, 'text': 'this is used especially in classification learning process,', 'start': 19688.519, 'duration': 4.523}, {'end': 19702.71, 'text': 'and when you get the results and our model predicts the results you compare it with the actual value and try to find out what is the accuracy okay.', 'start': 19693.042, 'duration': 9.668}, {'end': 19709.737, 'text': "So in this case, let's say this is an example of a confusion matrix and it is a binary matrix.", 'start': 19703.072, 'duration': 6.665}, {'end': 19718.744, 'text': 'So you have the actual values, which is the labeled data, right? And which is, so you have how many yes and how many no.', 'start': 19709.897, 'duration': 8.847}], 'summary': 'Classification learning process involves comparing model predictions with actual values to determine accuracy, illustrated by a binary confusion matrix.', 'duration': 30.225, 'max_score': 19688.519, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5819688519.jpg'}, {'end': 19809.408, 'src': 'embed', 'start': 19784.791, 'weight': 7, 'content': [{'end': 19791.316, 'text': 'you need to first explain that the total sum in this matrix the numbers is equal to the size of the test data set,', 'start': 19784.791, 'duration': 6.525}, {'end': 19795.338, 'text': 'and the diagonal values indicate the accuracy.', 'start': 19791.316, 'duration': 4.022}, {'end': 19801.802, 'text': 'So by just by looking at it, you can probably have an idea about is this an accurate model??', 'start': 19795.398, 'duration': 6.404}, {'end': 19803.163, 'text': 'Is the model being accurate??', 'start': 19801.902, 'duration': 1.261}, {'end': 19809.408, 'text': "If they're all spread out equally in all these four boxes, that means probably the accuracy is not very good.", 'start': 19803.669, 'duration': 5.739}], 'summary': 'Matrix total equals test set size, diagonal values indicate accuracy. spread out values suggest low accuracy.', 'duration': 24.617, 'max_score': 19784.791, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5819784791.jpg'}, {'end': 19982.162, 'src': 'embed', 'start': 19951.852, 'weight': 8, 'content': [{'end': 19957.333, 'text': 'So that means the system has predicted it as a positive, but the real value.', 'start': 19951.852, 'duration': 5.481}, {'end': 19959.374, 'text': 'So this is what the false comes from.', 'start': 19957.413, 'duration': 1.961}, {'end': 19961.834, 'text': 'But the real value is not positive.', 'start': 19959.414, 'duration': 2.42}, {'end': 19964.055, 'text': 'That is the way you should understand this term.', 'start': 19961.894, 'duration': 2.161}, {'end': 19967.016, 'text': 'false positive or even false negative.', 'start': 19964.455, 'duration': 2.561}, {'end': 19968.497, 'text': 'So false positive.', 'start': 19967.096, 'duration': 1.401}, {'end': 19972.078, 'text': 'So positive is what your system has predicted.', 'start': 19968.637, 'duration': 3.441}, {'end': 19974.439, 'text': 'So where is that system predicted? This is the one.', 'start': 19972.118, 'duration': 2.321}, {'end': 19975.62, 'text': 'Positive is what? Yes.', 'start': 19974.459, 'duration': 1.161}, {'end': 19982.162, 'text': 'So you basically consider this row, okay? Now if you consider this row, so this is all positive values.', 'start': 19975.78, 'duration': 6.382}], 'summary': 'The system predicted false positives due to misinterpretation of positive values.', 'duration': 30.31, 'max_score': 19951.852, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5819951852.jpg'}, {'end': 20145.332, 'src': 'embed', 'start': 20116.031, 'weight': 9, 'content': [{'end': 20118.473, 'text': 'So it is around the methodology that is applied.', 'start': 20116.031, 'duration': 2.442}, {'end': 20126.778, 'text': 'so, basically the way you can probably answer in your own words, but the way the model development of the machine learning model happens is like this.', 'start': 20118.633, 'duration': 8.145}, {'end': 20134.363, 'text': 'so first of all, you try to understand the problem and try to figure out whether it is a classification problem or a regression problem.', 'start': 20126.778, 'duration': 7.585}, {'end': 20141.448, 'text': 'based on that, you select a few algorithms and then you start the process of training these models.', 'start': 20134.363, 'duration': 7.085}, {'end': 20145.332, 'text': 'okay, So you can either do that or you can.', 'start': 20141.448, 'duration': 3.884}], 'summary': 'Methodology for machine learning model development and training explained.', 'duration': 29.301, 'max_score': 20116.031, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5820116031.jpg'}, {'end': 20363.552, 'src': 'embed', 'start': 20337.087, 'weight': 10, 'content': [{'end': 20342.093, 'text': 'and these are called deep neural networks and therefore the term deep learning.', 'start': 20337.087, 'duration': 5.006}, {'end': 20350.981, 'text': 'The other difference between machine learning and deep learning, which the interviewer may be wanting to hear, is that in case of machine learning,', 'start': 20342.633, 'duration': 8.348}, {'end': 20353.843, 'text': 'the feature engineering is done manually.', 'start': 20350.981, 'duration': 2.862}, {'end': 20355.725, 'text': 'What do we mean by feature engineering?', 'start': 20354.063, 'duration': 1.662}, {'end': 20361.25, 'text': 'Basically, when we are trying to train our model, we have our training data right?', 'start': 20355.785, 'duration': 5.465}, {'end': 20363.552, 'text': 'So we have our training label data.', 'start': 20361.27, 'duration': 2.282}], 'summary': 'Deep learning involves deep neural networks. in machine learning, feature engineering is done manually.', 'duration': 26.465, 'max_score': 20337.087, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5820337087.jpg'}, {'end': 20585.325, 'src': 'embed', 'start': 20555.798, 'weight': 11, 'content': [{'end': 20558.72, 'text': 'It could be a very specific question around supervised machine learning.', 'start': 20555.798, 'duration': 2.922}, {'end': 20565.367, 'text': 'So this is like give examples of supervised machine learning, use of supervised machine learning in modern business.', 'start': 20559, 'duration': 6.367}, {'end': 20567.609, 'text': 'So that could be the next question.', 'start': 20565.407, 'duration': 2.202}, {'end': 20574.176, 'text': 'So there are quite a few examples or quite a few use cases, if you will, for supervised machine learning.', 'start': 20567.649, 'duration': 6.527}, {'end': 20577.639, 'text': 'The very common one is email spam detection.', 'start': 20574.316, 'duration': 3.323}, {'end': 20585.325, 'text': 'So you want to train your application or your system to detect between spam and non-spam.', 'start': 20577.799, 'duration': 7.526}], 'summary': 'Supervised machine learning has various use cases, with email spam detection being a common one.', 'duration': 29.527, 'max_score': 20555.798, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5820555798.jpg'}, {'end': 20845.62, 'src': 'embed', 'start': 20817.768, 'weight': 12, 'content': [{'end': 20821.949, 'text': 'So this is where semi-supervised learning comes into play.', 'start': 20817.768, 'duration': 4.181}, {'end': 20826.891, 'text': 'So what happens is there is a large amount of data, maybe a part of it is labeled.', 'start': 20822.09, 'duration': 4.801}, {'end': 20836.775, 'text': 'Then we try some techniques to label the remaining part of the data so that we get completely labeled data and then we train model.', 'start': 20827.071, 'duration': 9.704}, {'end': 20845.62, 'text': 'So I know this is a little long winding explanation, but unfortunately there is no quick and easy definition for semi supervised machine learning.', 'start': 20836.815, 'duration': 8.805}], 'summary': 'Semi-supervised learning leverages partially labeled data to train models, enabling better utilization of large datasets.', 'duration': 27.852, 'max_score': 20817.768, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5820817768.jpg'}, {'end': 20888.494, 'src': 'embed', 'start': 20861.95, 'weight': 13, 'content': [{'end': 20866.625, 'text': 'so it can be worded in So how do we answer this question?', 'start': 20861.95, 'duration': 4.675}, {'end': 20868.706, 'text': 'So, unsupervised learning?', 'start': 20867.045, 'duration': 1.661}, {'end': 20873.068, 'text': 'you can say that there are two types clustering and association.', 'start': 20868.706, 'duration': 4.362}, {'end': 20878.43, 'text': 'And clustering is a technique where similar objects are put together.', 'start': 20873.508, 'duration': 4.922}, {'end': 20881.251, 'text': 'There are different ways of finding similar objects.', 'start': 20878.77, 'duration': 2.481}, {'end': 20883.712, 'text': 'So their characteristics can be measured.', 'start': 20881.331, 'duration': 2.381}, {'end': 20888.494, 'text': 'And if they have most of the characteristics, if they are similar, then they can be put together.', 'start': 20883.832, 'duration': 4.662}], 'summary': 'Unsupervised learning includes clustering and association, grouping similar objects based on shared characteristics.', 'duration': 26.544, 'max_score': 20861.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5820861950.jpg'}], 'start': 18748.681, 'title': 'Machine learning concepts', 'summary': 'Covers reinforcement learning, overfitting, machine learning training, handling missing data, choosing classifiers, machine learning methodology, and comparisons between machine learning and deep learning. it provides insights into various techniques, applications, and methodologies, emphasizing practical examples and considerations for model development and deployment.', 'chapters': [{'end': 18861.375, 'start': 18748.681, 'title': 'Reinforcement learning and overfitting', 'summary': 'Covers reinforcement learning, highlighting its two main components - the agent and the environment, and its application in teaching systems to learn games, with examples such as alphago. it also delves into overfitting, defining it as a situation where the model memorizes the data.', 'duration': 112.694, 'highlights': ["Reinforcement learning involves an agent working in an environment to achieve a target, being rewarded for actions moving towards the target and punished for actions moving away from it, with impactful applications in teaching systems to learn games like AlphaGo's victory over the Go world champion.", 'Overfitting is when a model memorizes the data, posing challenges in understanding and avoiding it.']}, {'end': 19351.065, 'start': 18861.415, 'title': 'Machine learning training and overfitting', 'summary': 'Explains the concept of machine learning training using an analogy of teaching a child to recognize fruits, illustrating overfitting, techniques to avoid it, and the process of splitting data into training and test sets, emphasizing the importance of realistic testing and individual preferences in ratio selection.', 'duration': 489.65, 'highlights': ['Machine Learning Training Process Analogy The chapter uses the analogy of teaching a child to recognize fruits to explain the machine learning training process, involving showing a child a basket of fruits, teaching recognition, and illustrating the memorization and overfitting phenomena.', 'Overfitting Concept and Impact The concept of overfitting is explained as a situation where the training process reaches very high accuracy and low loss, but during testing, a high error rate occurs, and the techniques to avoid overfitting, such as regularization, are mentioned.', 'Importance of Realistic Testing The importance of realistic testing is emphasized, where the model is tested with new data to ensure proper training and the need for potential model adjustments if high errors are encountered during testing.', 'Data Splitting and Ratio Selection The process of splitting data into training and test sets is detailed, highlighting the significance of setting aside a portion as a test set, the repeated training iterations, and individual preferences in selecting the ratio, with examples of 50-50, 60-40, 70-30, and other variations.']}, {'end': 19572.866, 'start': 19351.065, 'title': 'Handling missing data in data management', 'summary': 'Discusses various ways to handle missing or corrupt data, including the option of removing records, filling with mean, minimum, or maximum values, and emphasizing the importance of illustrating answers with relevant examples and experiences.', 'duration': 221.801, 'highlights': ['Illustrating with examples of handling missing data can be a perfect way to respond to questions around data management. Providing examples of handling missing data, such as mentioning the columns with missing data and the number of rows affected, can be a strong response in a data management interview.', 'Emphasizing the importance of illustrating answers with relevant examples and experiences. Illustrating answers with relevant examples and experiences is emphasized as the best way to respond to questions about handling missing or corrupt data.', 'Discussing various ways to handle missing or corrupt data, including the option of removing records, filling with mean, minimum, or maximum values. The chapter discusses various methods for handling missing or corrupt data, such as removing records with missing data, filling with mean, minimum, or maximum values.']}, {'end': 20091.212, 'start': 19573.006, 'title': 'Choosing classifiers and understanding confusion matrix', 'summary': 'Covers the process of choosing classifiers based on training set size and emphasizes the need to try out multiple classifiers to determine the most suitable one, while also explaining the concept of confusion matrix, its components, and the calculation of accuracy from the matrix.', 'duration': 518.206, 'highlights': ['Choosing Classifiers Based on Training Set Size The best approach is to try out multiple classifiers regardless of data size, and decide based on the specific situation and accuracy, emphasizing the need to test and compare classifiers for determining the most suitable one.', 'Explaining Confusion Matrix The confusion matrix is used in classification learning to compare model predictions with actual values and determine accuracy, with the diagonal values indicating accuracy and the total sum of values equating to the test data set size.', 'Calculating Accuracy from Confusion Matrix The accuracy is calculated by summing the diagonal values and dividing by the total, providing a simple mathematical calculation to determine accuracy, which is essential in evaluating model performance.', 'Understanding False Positive and False Negative The differentiation between false positive and false negative is explained using the confusion matrix, where false positive refers to the system predicting a positive value when the actual value is negative, while false negative refers to the system predicting a negative value when the actual value is positive.']}, {'end': 21084.729, 'start': 20091.332, 'title': 'Machine learning methodology and comparison', 'summary': 'Covers the methodology of developing a machine learning model, including steps such as problem understanding, algorithm selection, model training, testing, and production deployment. it also compares machine learning and deep learning, emphasizing differences in feature engineering and neural network usage. additionally, it discusses applications of supervised machine learning in modern business and explains semi-supervised and unsupervised machine learning techniques.', 'duration': 993.397, 'highlights': ['The process of developing a machine learning model involves problem understanding, algorithm selection, model training, testing, and production deployment, with the possibility of iterative cycles for refinement and maintenance. The chapter explains the steps involved in developing a machine learning model, including problem understanding, algorithm selection, model training, testing, and production deployment, with potential iterative cycles for refinement and maintenance.', 'Machine learning and deep learning are compared, focusing on differences in feature engineering and neural network usage, where machine learning involves manual feature engineering and deep learning automates this process using neural networks. A comparison between machine learning and deep learning is provided, emphasizing differences in feature engineering and neural network usage, with machine learning requiring manual feature engineering and deep learning automating this process using neural networks.', 'Applications of supervised machine learning in modern business are discussed, such as email spam detection and healthcare diagnostics, highlighting the use of labeled data for training models. The chapter explores applications of supervised machine learning in modern business, including email spam detection and healthcare diagnostics, showcasing the utilization of labeled data for model training.', 'Semi-supervised machine learning, positioned between supervised and unsupervised learning, is explained as a method to tackle the challenge of obtaining labeled data for training, involving techniques to label a portion of the data to facilitate model training. The concept of semi-supervised machine learning is detailed as a method addressing the challenge of obtaining labeled data for training, involving techniques to label a portion of the data to facilitate model training.', 'Unsupervised machine learning techniques, including clustering and association, are outlined, with clustering involving grouping similar objects and association identifying relationships between items. The chapter outlines unsupervised machine learning techniques, including clustering and association, where clustering groups similar objects and association identifies relationships between items.']}], 'duration': 2336.048, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5818748681.jpg', 'highlights': ["Reinforcement learning involves an agent working in an environment to achieve a target, with impactful applications in teaching systems to learn games like AlphaGo's victory over the Go world champion.", 'Overfitting is when a model memorizes the data, posing challenges in understanding and avoiding it.', 'The importance of realistic testing is emphasized, where the model is tested with new data to ensure proper training and the need for potential model adjustments if high errors are encountered during testing.', 'The process of splitting data into training and test sets is detailed, highlighting the significance of setting aside a portion as a test set, the repeated training iterations, and individual preferences in selecting the ratio.', 'Illustrating with examples of handling missing data can be a perfect way to respond to questions around data management, providing examples of handling missing data, such as mentioning the columns with missing data and the number of rows affected.', 'Choosing Classifiers Based on Training Set Size The best approach is to try out multiple classifiers regardless of data size, and decide based on the specific situation and accuracy, emphasizing the need to test and compare classifiers for determining the most suitable one.', 'The confusion matrix is used in classification learning to compare model predictions with actual values and determine accuracy, with the diagonal values indicating accuracy and the total sum of values equating to the test data set size.', 'The accuracy is calculated by summing the diagonal values and dividing by the total, providing a simple mathematical calculation to determine accuracy, which is essential in evaluating model performance.', 'The differentiation between false positive and false negative is explained using the confusion matrix, where false positive refers to the system predicting a positive value when the actual value is negative, while false negative refers to the system predicting a negative value when the actual value is positive.', 'The process of developing a machine learning model involves problem understanding, algorithm selection, model training, testing, and production deployment, with the possibility of iterative cycles for refinement and maintenance.', 'A comparison between machine learning and deep learning is provided, emphasizing differences in feature engineering and neural network usage, with machine learning requiring manual feature engineering and deep learning automating this process using neural networks.', 'The chapter explores applications of supervised machine learning in modern business, including email spam detection and healthcare diagnostics, showcasing the utilization of labeled data for model training.', 'The concept of semi-supervised machine learning is detailed as a method addressing the challenge of obtaining labeled data for training, involving techniques to label a portion of the data to facilitate model training.', 'The chapter outlines unsupervised machine learning techniques, including clustering and association, where clustering groups similar objects and association identifies relationships between items.']}, {'end': 22887.741, 'segs': [{'end': 21108.909, 'src': 'embed', 'start': 21084.889, 'weight': 1, 'content': [{'end': 21093.472, 'text': 'Compared to that, what is deductive learning? So here you draw conclusion or the person draws conclusion out of experience.', 'start': 21084.889, 'duration': 8.583}, {'end': 21094.972, 'text': 'So we will stick to the analogy.', 'start': 21093.492, 'duration': 1.48}, {'end': 21099.974, 'text': "So compared to the showing a video, let's assume a person is allowed to play with fire.", 'start': 21095.012, 'duration': 4.962}, {'end': 21106.507, 'text': "And then he figures out that if he puts his finger, it's burning or if throw something into the fire, it burns.", 'start': 21100.684, 'duration': 5.823}, {'end': 21108.909, 'text': 'So he is learning through experience.', 'start': 21106.627, 'duration': 2.282}], 'summary': 'Deductive learning involves drawing conclusions from experience, akin to learning through fire experimentation.', 'duration': 24.02, 'max_score': 21084.889, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5821084889.jpg'}, {'end': 21164.069, 'src': 'embed', 'start': 21134.887, 'weight': 0, 'content': [{'end': 21138.568, 'text': 'So let us take a little while to understand what these two are.', 'start': 21134.887, 'duration': 3.681}, {'end': 21140.648, 'text': 'One is KNN, another is K-means.', 'start': 21138.708, 'duration': 1.94}, {'end': 21147.43, 'text': 'KNN stands for K-nearest neighbors and K-means of course is the clustering mechanism.', 'start': 21141.108, 'duration': 6.322}, {'end': 21152.671, 'text': 'Now, these two are completely different except for the letter K being common between them.', 'start': 21147.51, 'duration': 5.161}, {'end': 21154.492, 'text': 'KNN is completely different.', 'start': 21152.831, 'duration': 1.661}, {'end': 21164.069, 'text': 'K-means clustering is completely KNN is a classification process and therefore it comes under supervised learning,', 'start': 21154.492, 'duration': 9.577}], 'summary': 'Knn is for classification, k-means for clustering. knn is supervised learning.', 'duration': 29.182, 'max_score': 21134.887, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5821134887.jpg'}, {'end': 21373.155, 'src': 'embed', 'start': 21343.991, 'weight': 3, 'content': [{'end': 21350.955, 'text': 'So naive is basically an English word, and that has been added here because of the nature of this particular classifier.', 'start': 21343.991, 'duration': 6.964}, {'end': 21364.382, 'text': 'Naive based classifier is a probability based classifier and it makes some assumptions that presence of one feature of a class is not related to the presence of any other feature of maybe other classes.', 'start': 21351.115, 'duration': 13.267}, {'end': 21373.155, 'text': 'So, which is not a very strong or not a very, what do you say, accurate assumption because these features can be related and so on.', 'start': 21364.952, 'duration': 8.203}], 'summary': 'Naive classifier is based on probability and makes assumptions about feature independence, which may not always be accurate.', 'duration': 29.164, 'max_score': 21343.991, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5821343991.jpg'}, {'end': 21434.403, 'src': 'embed', 'start': 21408.308, 'weight': 2, 'content': [{'end': 21412.851, 'text': 'So, first of all, reinforcement learning has an environment and an agent,', 'start': 21408.308, 'duration': 4.543}, {'end': 21418.294, 'text': 'and the agent is basically performing some actions in order to achieve a certain goal.', 'start': 21412.851, 'duration': 5.443}, {'end': 21421.696, 'text': 'And these goals can be anything either.', 'start': 21418.654, 'duration': 3.042}, {'end': 21429.121, 'text': 'if it is related to game, then the goal could be that you have to score very high score, a high value, high number.', 'start': 21421.696, 'duration': 7.425}, {'end': 21434.403, 'text': 'Or it could be that your number of lives should be as high as possible.', 'start': 21429.461, 'duration': 4.942}], 'summary': 'Reinforcement learning involves an agent achieving goals, such as achieving high scores or maximizing lives in a game environment.', 'duration': 26.095, 'max_score': 21408.308, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5821408308.jpg'}, {'end': 21728.904, 'src': 'embed', 'start': 21701.112, 'weight': 4, 'content': [{'end': 21704.815, 'text': 'So in machine learning, a lot of it happens through trial and error.', 'start': 21701.112, 'duration': 3.703}, {'end': 21712.261, 'text': 'There is no real possibility that anybody can, just by looking at the problem or understanding the problem, tell you that, okay,', 'start': 21704.875, 'duration': 7.386}, {'end': 21716.505, 'text': 'in this particular situation, this is exactly the algorithm that you should use.', 'start': 21712.261, 'duration': 4.244}, {'end': 21720.728, 'text': 'Then the questions may be around application of machine learning.', 'start': 21717.005, 'duration': 3.723}, {'end': 21726.982, 'text': 'And this question is specifically around how Amazon is able to recommend other things to buy.', 'start': 21721.335, 'duration': 5.647}, {'end': 21728.904, 'text': 'So this is around recommendation engine.', 'start': 21727.002, 'duration': 1.902}], 'summary': 'Machine learning involves trial and error, and amazon uses a recommendation engine to suggest purchases.', 'duration': 27.792, 'max_score': 21701.112, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5821701112.jpg'}, {'end': 21765.367, 'src': 'embed', 'start': 21741.977, 'weight': 5, 'content': [{'end': 21750.34, 'text': 'Amazon website or e-commerce site like Amazon collects a lot of data around the customer behavior who is purchasing what,', 'start': 21741.977, 'duration': 8.363}, {'end': 21754.741, 'text': 'and if somebody is buying a particular thing, they are also buying something else.', 'start': 21750.34, 'duration': 4.401}, {'end': 21756.922, 'text': 'so this kind of association right.', 'start': 21754.741, 'duration': 2.181}, {'end': 21759.323, 'text': 'so this is the unsupervised learning we talked about.', 'start': 21756.922, 'duration': 2.401}, {'end': 21765.367, 'text': 'they use this to associate and link or relate items, and that is one part of it.', 'start': 21759.323, 'duration': 6.044}], 'summary': 'Amazon uses unsupervised learning to analyze customer behavior and make product associations.', 'duration': 23.39, 'max_score': 21741.977, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5821741977.jpg'}, {'end': 21918.313, 'src': 'embed', 'start': 21892.544, 'weight': 6, 'content': [{'end': 21898.966, 'text': 'It could be binary classification problem like for example whether a customer will buy or he will not buy.', 'start': 21892.544, 'duration': 6.422}, {'end': 21901.787, 'text': 'That is a classification, binary classification.', 'start': 21899.126, 'duration': 2.661}, {'end': 21904.768, 'text': 'It can be in the weather forecast area.', 'start': 21901.947, 'duration': 2.821}, {'end': 21910.63, 'text': 'Now weather forecast is again combination of regression and classification, because, on the one hand,', 'start': 21904.908, 'duration': 5.722}, {'end': 21913.331, 'text': "you want to predict whether it's going to rain or not.", 'start': 21910.63, 'duration': 2.701}, {'end': 21914.552, 'text': "That's a classification problem.", 'start': 21913.411, 'duration': 1.141}, {'end': 21916.012, 'text': "That's a binary classification.", 'start': 21914.672, 'duration': 1.34}, {'end': 21918.313, 'text': "Whether it's going to rain or not rain.", 'start': 21916.392, 'duration': 1.921}], 'summary': 'Binary classification can be used to predict customer purchases or weather forecasts, such as predicting rain or no rain.', 'duration': 25.769, 'max_score': 21892.544, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5821892544.jpg'}, {'end': 22140.52, 'src': 'embed', 'start': 22101.836, 'weight': 7, 'content': [{'end': 22103.517, 'text': 'So one thing is random.', 'start': 22101.836, 'duration': 1.681}, {'end': 22110.282, 'text': 'forest is kind of, in one way it is an extension of decision trees because it is basically nothing.', 'start': 22103.517, 'duration': 6.765}, {'end': 22116.066, 'text': 'but you have multiple decision trees and trees will basically you will use for doing.', 'start': 22110.282, 'duration': 5.784}, {'end': 22118.347, 'text': 'if it is classification, mostly it is classification.', 'start': 22116.066, 'duration': 2.281}, {'end': 22123.21, 'text': 'you will use the trees for classification and then you use voting for finding the final class.', 'start': 22118.347, 'duration': 4.863}, {'end': 22124.491, 'text': 'So that is the underlines.', 'start': 22123.25, 'duration': 1.241}, {'end': 22125.912, 'text': 'But how will you explain this??', 'start': 22124.571, 'duration': 1.341}, {'end': 22127.613, 'text': 'How will you respond to this?', 'start': 22125.972, 'duration': 1.641}, {'end': 22140.52, 'text': 'and the more important thing that you need to, probably the interviewer is, is waiting to hear is ensemble learner right?', 'start': 22132.356, 'duration': 8.164}], 'summary': 'Random forest is an ensemble learner using multiple decision trees for classification and voting for final class.', 'duration': 38.684, 'max_score': 22101.836, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5822101836.jpg'}, {'end': 22407.348, 'src': 'embed', 'start': 22361.763, 'weight': 8, 'content': [{'end': 22366.885, 'text': 'So the way to go about is you choose a few algorithms based on what the problem is.', 'start': 22361.763, 'duration': 5.122}, {'end': 22371.026, 'text': 'You try out your data, you train some models of these algorithms,', 'start': 22367.105, 'duration': 3.921}, {'end': 22379.468, 'text': 'check which one gives you the lowest error or the highest accuracy and based on that, you choose that particular algorithm.', 'start': 22371.026, 'duration': 8.442}, {'end': 22384.54, 'text': 'All right, then there can be questions around bias and variance.', 'start': 22380.379, 'duration': 4.161}, {'end': 22391.883, 'text': 'So the question can be, what is bias and variance in machine learning? So you just need to give out a definition for each of these.', 'start': 22384.841, 'duration': 7.042}, {'end': 22399.206, 'text': 'For example, bias in machine learning, it occurs when the predicted values are far away from the actual value.', 'start': 22392.003, 'duration': 7.203}, {'end': 22407.348, 'text': 'So that is a bias, okay? And whereas all the values are probably, they are far off, but they are very near to each other though.', 'start': 22399.286, 'duration': 8.062}], 'summary': 'Choose algorithms based on error/accuracy, define bias and variance in ml.', 'duration': 45.585, 'max_score': 22361.763, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5822361763.jpg'}, {'end': 22500.109, 'src': 'embed', 'start': 22469.003, 'weight': 10, 'content': [{'end': 22471.146, 'text': 'and there is no way you can minimize both of them.', 'start': 22469.003, 'duration': 2.143}, {'end': 22480.193, 'text': 'so you need to have a trade-off saying that, okay, this is the level at which i will have my bias and this is the level at which i will have variance.', 'start': 22471.525, 'duration': 8.668}, {'end': 22482.675, 'text': 'so the trade-off is that pretty much, uh,', 'start': 22480.193, 'duration': 2.482}, {'end': 22491.924, 'text': 'that you you decide what is the level you will tolerate for your bias and what is the level you will tolerate for variance and a combination of these two,', 'start': 22482.675, 'duration': 9.249}, {'end': 22495.067, 'text': 'in such a way that your final results are not way off.', 'start': 22491.924, 'duration': 3.143}, {'end': 22500.109, 'text': 'And having a trade-off will ensure that the results are consistent, right?', 'start': 22495.327, 'duration': 4.782}], 'summary': 'Find a trade-off between bias and variance to ensure consistent results.', 'duration': 31.106, 'max_score': 22469.003, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5822469003.jpg'}, {'end': 22561.71, 'src': 'embed', 'start': 22526.001, 'weight': 11, 'content': [{'end': 22527.142, 'text': 'And it is very simple.', 'start': 22526.001, 'duration': 1.141}, {'end': 22529.723, 'text': 'The definition is like a formula.', 'start': 22527.382, 'duration': 2.341}, {'end': 22535.907, 'text': 'Your precision is true positive by true positive plus false positive.', 'start': 22529.983, 'duration': 5.924}, {'end': 22542.154, 'text': 'And your recall is true positive by true positive plus false negative.', 'start': 22536.488, 'duration': 5.666}, {'end': 22545.438, 'text': "So that's you can just show it in a mathematical way.", 'start': 22542.435, 'duration': 3.003}, {'end': 22546.42, 'text': "That's pretty much it.", 'start': 22545.518, 'duration': 0.902}, {'end': 22547.798, 'text': 'can be shown.', 'start': 22547.318, 'duration': 0.48}, {'end': 22549.4, 'text': "That's the easiest way to define.", 'start': 22547.838, 'duration': 1.562}, {'end': 22553.163, 'text': 'So the next question can be about decision tree.', 'start': 22549.6, 'duration': 3.563}, {'end': 22561.71, 'text': 'What is decision tree pruning and why is it? So basically, decision trees are really simple to implement and understand.', 'start': 22553.383, 'duration': 8.327}], 'summary': 'Precision is true positive by true positive plus false positive, recall is true positive by true positive plus false negative. decision trees are simple to implement and understand.', 'duration': 35.709, 'max_score': 22526.001, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5822526001.jpg'}, {'end': 22597.861, 'src': 'embed', 'start': 22573.26, 'weight': 12, 'content': [{'end': 22582.008, 'text': "And this can also lead to overfitting, which is basically that during training you will get 100% accuracy, but when you're doing testing,", 'start': 22573.26, 'duration': 8.748}, {'end': 22583.209, 'text': "you'll get a lot of errors.", 'start': 22582.008, 'duration': 1.201}, {'end': 22587.253, 'text': 'So that is the reason pruning needs to be done.', 'start': 22583.63, 'duration': 3.623}, {'end': 22595.4, 'text': 'So the purpose or the reason for doing decision tree pruning is to reduce overfitting or to down on overfitting.', 'start': 22587.393, 'duration': 8.007}, {'end': 22597.861, 'text': 'and what is decision tree pruning?', 'start': 22595.4, 'duration': 2.461}], 'summary': 'Decision tree pruning reduces overfitting during training and testing.', 'duration': 24.601, 'max_score': 22573.26, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5822573260.jpg'}, {'end': 22706.205, 'src': 'embed', 'start': 22679.663, 'weight': 13, 'content': [{'end': 22688.391, 'text': 'so logistic regression is used for binary classification and the output of a logistic regression is either a 0 or a 1, and it varies.', 'start': 22679.663, 'duration': 8.728}, {'end': 22695.477, 'text': "so it's basically it calculates a probability between 0 and 1 and we can set a threshold that can vary.", 'start': 22688.391, 'duration': 7.086}, {'end': 22706.205, 'text': 'typically it is 0.5, so any value above 0.5 is considered as 1, And if the probability is below 0.5, it is considered as 0..', 'start': 22695.477, 'duration': 10.728}], 'summary': 'Logistic regression outputs 0 or 1 based on probability, with 0.5 threshold.', 'duration': 26.542, 'max_score': 22679.663, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5822679663.jpg'}, {'end': 22804.779, 'src': 'embed', 'start': 22777.249, 'weight': 14, 'content': [{'end': 22780.27, 'text': 'you want to find out which class this belongs to, right?', 'start': 22777.249, 'duration': 3.021}, {'end': 22783.592, 'text': 'So you go about as the name suggests.', 'start': 22780.45, 'duration': 3.142}, {'end': 22790.174, 'text': 'you go about finding the nearest neighbors right?, The points which are closest to this and how many of them you will find.', 'start': 22783.592, 'duration': 6.582}, {'end': 22791.495, 'text': 'that is what is defined by K.', 'start': 22790.174, 'duration': 1.321}, {'end': 22795.716, 'text': "Now let's say our initial value of k was 5.", 'start': 22791.875, 'duration': 3.841}, {'end': 22800.157, 'text': 'So you will find the k, the 5 nearest data points.', 'start': 22795.716, 'duration': 4.441}, {'end': 22804.779, 'text': 'So in this case as it is illustrated, these are the 5 nearest data points.', 'start': 22800.338, 'duration': 4.441}], 'summary': 'Finding the 5 nearest data points for classification.', 'duration': 27.53, 'max_score': 22777.249, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5822777249.jpg'}], 'start': 21084.889, 'title': 'Machine learning concepts', 'summary': 'Covers inductive vs deductive learning, naive base classifier, choosing ml algorithms, classification and regression, and logistic regression and k nearest neighbor. it provides insights on algorithm selection, bias and variance, precision and recall, and decision tree pruning.', 'chapters': [{'end': 21322.585, 'start': 21084.889, 'title': 'Inductive vs deductive learning', 'summary': 'Explains the difference between inductive and deductive learning, the distinction between knn and k-means clustering, and the impact of the value of k on classification, with knn being a supervised learning process and k-means clustering being unsupervised.', 'duration': 237.696, 'highlights': ['Difference between KNN and K-means clustering KNN is a classification process under supervised learning, with the value of k determining the number of nearest objects used for classification, while K-means clustering is an unsupervised process that creates clusters based on the specified value of k.', 'Impact of the value of k on classification The classification outcome varies based on the value of k in KNN, as a smaller value may result in one class allocation, while a larger value could lead to a different class allocation, highlighting the importance of selecting an appropriate value for k.', 'Inductive vs Deductive Learning The difference between inductive and deductive learning is explained, with inductive learning involving drawing conclusions from experience and deductive learning being akin to learning through experience, providing a clear distinction between the two learning approaches.']}, {'end': 21619.422, 'start': 21323.365, 'title': 'Naive base classifier and reinforcement learning', 'summary': 'Discusses the naive base classifier, its assumptions and effectiveness, and explains the concept of reinforcement learning in the context of playing games, particularly chess, using visual learning and repetition.', 'duration': 296.057, 'highlights': ['Naive Base Classifier is a probability-based classifier that makes assumptions about the independence of features, despite the potential relationship between them, but still performs well. The Naive Base Classifier makes assumptions about the independence of features, which may not always hold true, but it is noted for its effectiveness despite these assumptions.', 'Reinforcement learning involves an agent performing actions in an environment to achieve a goal, with examples ranging from scoring high in games to teaching self-driving cars to navigate roads. Reinforcement learning involves an agent performing actions in an environment to achieve a goal, with examples including scoring high in games and teaching self-driving cars to navigate roads.', "In reinforcement learning, the system is rewarded for actions that move towards the goal and penalized for actions that go against it, creating a 'carrot and stick' system. In reinforcement learning, the system is rewarded for actions that move towards the goal and penalized for actions that go against it, creating a 'carrot and stick' system.", 'Reinforcement learning for games like chess involves the system visually learning from watching games and then repeatedly playing and learning from its own experiences. Reinforcement learning for games like chess involves the system visually learning from watching games and then repeatedly playing and learning from its own experiences.']}, {'end': 21812.148, 'start': 21620.303, 'title': 'Choosing machine learning algorithms', 'summary': 'Discusses the challenge of choosing the right machine learning algorithm for a problem, highlighting that there is no exact way to decide and that trial and error is often necessary. it also explores the workings of a recommendation engine, emphasizing the use of customer behavior data and profiling to generate recommendations.', 'duration': 191.845, 'highlights': ['There is no exact way to decide which machine learning algorithm to use, and trial and error is often necessary The speaker emphasizes that it is not possible to outright say which algorithm to use for a problem and that trying out a variety of algorithms to find the best-performing one is crucial.', "Recommendation engines like Amazon's work based on collecting customer behavior data and profiling The process involves collecting data on customer behavior, such as purchases and associations between items, and profiling users based on factors like age, gender, and location to generate recommendations."]}, {'end': 22294.519, 'start': 21812.148, 'title': 'Understanding classification and regression', 'summary': 'Covers the basics of classification and regression, providing examples and applications such as spam filters, random forests, and their implementation with multiple algorithms and ensemble learning.', 'duration': 482.371, 'highlights': ['Classification vs. Regression Classification is used for identifying discrete classes, while regression is used for finding continuous values, with examples such as image categorization and stock price prediction.', 'Designing a Spam Filter The process involves identifying the problem as a classification one, selecting algorithms like logistic regression or decision trees, training and testing models with historical data, and implementing the chosen model.', 'Understanding Random Forest Random forest is an ensemble learner, consisting of multiple decision trees used for classification with a voting mechanism, and for regression by averaging the outputs of individual trees to reduce error.']}, {'end': 22620.475, 'start': 22295.225, 'title': 'Machine learning algorithms & concepts', 'summary': 'Discusses the selection of machine learning algorithms, bias and variance in machine learning, trade-off between bias and variance, precision and recall, and decision tree pruning. it also provides insights on how to make algorithm selections and define key concepts in machine learning.', 'duration': 325.25, 'highlights': ['The chapter explains the process of selecting machine learning algorithms based on problem type and model performance, emphasizing the absence of a one-size-fits-all approach. The response highlights the need to narrow down the list of algorithms based on whether the problem is a classification or regression type, and then evaluate their performance to determine the most suitable algorithm, recognizing the absence of a universal selection criterion.', 'The concept of bias and variance in machine learning is defined, with bias indicating the deviation of predicted values from actual values, and variance representing the scattered nature of predicted values. The explanation differentiates bias as the deviation of predicted values from the actual value, while variance is described as the scattered nature of predicted values, providing clarity on these fundamental concepts in machine learning.', 'The trade-off between bias and variance is elucidated, emphasizing the inability to minimize both simultaneously and the need to strike a balance to ensure consistent and accurate results. The trade-off between bias and variance is explained, highlighting the challenge of minimizing both simultaneously and the necessity to maintain a balance between them to ensure consistent and accurate outcomes.', 'The definition and mathematical representation of precision and recall in machine learning are outlined, suggesting the use of a diagram and a confusion matrix for clarity. The precise definition and mathematical representation of precision and recall are provided, recommending the use of diagrams and a confusion matrix to illustrate these key metrics in machine learning.', 'The concept of decision tree pruning is explained as a method to reduce overfitting and complexity in decision trees by decreasing the number of internal nodes. The explanation covers the purpose of decision tree pruning to address overfitting and complexity by reducing the number of internal nodes in decision trees, offering insights into this essential concept in machine learning.']}, {'end': 22887.741, 'start': 22620.475, 'title': 'Logistic regression and k nearest neighbor', 'summary': 'Explains logistic regression, a binary classification technique, which calculates probability and sets a threshold for classification, along with an explanation of k nearest neighbor algorithm, which classifies objects based on the majority of the k nearest data points.', 'duration': 267.266, 'highlights': ['Logistic regression is used for binary classification, calculating a probability between 0 and 1 and setting a threshold, typically 0.5, for classification.', 'k Nearest Neighbor algorithm involves finding the k nearest data points, determining the majority class among them, and assigning the new data point to that class, with k being an integer variable, usually an odd number.']}], 'duration': 1802.852, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9f-GarcDY58/pics/9f-GarcDY5821084889.jpg', 'highlights': ['Difference between KNN and K-means clustering', 'Inductive vs Deductive Learning', 'Reinforcement learning involves an agent performing actions in an environment to achieve a goal', 'Naive Base Classifier makes assumptions about the independence of features, which may not always hold true', 'There is no exact way to decide which machine learning algorithm to use, and trial and error is often necessary', "Recommendation engines like Amazon's work based on collecting customer behavior data and profiling", 'Classification vs. Regression', 'Understanding Random Forest', 'The chapter explains the process of selecting machine learning algorithms based on problem type and model performance', 'The concept of bias and variance in machine learning is defined', 'The trade-off between bias and variance is elucidated', 'The definition and mathematical representation of precision and recall in machine learning are outlined', 'The concept of decision tree pruning is explained as a method to reduce overfitting and complexity in decision trees', 'Logistic regression is used for binary classification', 'k Nearest Neighbor algorithm involves finding the k nearest data points, determining the majority class among them, and assigning the new data point to that class']}], 'highlights': ['The course collection includes 30 important questions that might be faced in a machine learning interview.', 'The basics of machine learning are explained, including the idea of training machines to learn from past data and perform tasks much faster than humans.', 'The abundance of data and enhanced computational capabilities are the key factors driving the advancement of machine learning, with numerous applications in healthcare, finance, e-commerce, and transportation industries.', 'Reinforcement learning, though still in its infant stages, is expected to play a crucial role in the future of machine learning, enabling agents to learn how to behave in an environment by performing actions and observing the results.', 'Supervised learning involves labeled data and direct feedback, enabling accurate predictions.', 'The ability to connect various machine learning tools to make a bigger picture is emphasized.', 'The chapter illustrates the application of linear regression in profit estimation for a company.', 'The relationship between R&D expenditure and profit is discussed, emphasizing the correlation between the two.', 'Linear regression is used for economic growth prediction, product price forecasting, housing sales estimation, and cricket score prediction.', 'The statistical model of linear regression examines the relationship between independent and dependent variables.', 'The chapter emphasizes the importance of numpy and pandas as essential tools for the sklearn toolbox.', 'Pandas allows for easy reading and manipulation of CSV data, emphasizing its ability to recognize column names and easily interpret CSV data.', 'The chapter discusses visualizing data with Seaborn and preparing it for linear regression.', 'A detailed walk-through of creating and using a linear regression model is provided, contributing to a comprehensive understanding of the process.', "The evaluation of the model's performance using an R squared value of 0.9352 demonstrates a high level of accuracy and validity, indicating successful training for profit estimation and model effectiveness.", 'Logistic regression curve determines car breakdown probability with 0.5 threshold.', 'Logistic regression used for discrete outputs, linear regression for continuous.', "Logistic regression equation 'px = 1 / (1 + e^(-β0 + β1x))' for probability calculation.", 'Logistic regression used for classification problems, linear regression for regression.', 'K-means clustering groups similar data into clusters, with methods to determine the optimum value of k.', 'The chapter discusses the application of k-means clustering to cluster cricket players into batsmen and bowlers based on runs scored and wickets taken.', 'The process of k-means clustering involves the allocation of centroids, distance measurement of data points from centroids, and the repositioning of centroids.', 'The Elbow method is used to determine the optimum number of clusters by measuring the within sum of squares (WSS) for different values of k.', 'Using k-means clustering, Walmart can determine the best store locations based on customer addresses in their database, as demonstrated in a Python notebook.', "Color compression using k-means clustering reduces an image's 16 million colors to 16, preserving most of the image quality.", 'The chapter covers the process of training the data, creating a graph, and exploring the usage of the Naive Bayes classifier for text classification.', 'The tf-idf vectorizer is imported from sklearn.featuresextraction.txt, which weighs the words based on their usage in a document and across documents, aiding in data analysis and prediction.', 'Google uses machine learning and real-time location data to analyze traffic congestion, with red indicating heavily congested, yellow for slow moving, and blue for clear roads, based on average time taken and real-time vehicle location data from Google Maps.', "Machine learning's role in personalized advertising, spam filtering, online fraud detection, stock market trading, medical technology, automatic translation, and the steps to become a machine learning engineer. Machine learning applications across various domains", 'Significance of iris dataset since 1936 for predicting flower species based on measurements', 'The KNN algorithm is explained for predicting diabetes using a dataset of 768 people, achieving an accuracy score of 82% and an F1 score of 0.69.', 'The chapter discusses the use of support vector machine model for classification, including its application in predicting fruits and genders, and explains the concept of an optimal hyperplane and its significance in classification.', "Reinforcement learning involves an agent working in an environment to achieve a target, with impactful applications in teaching systems to learn games like AlphaGo's victory over the Go world champion.", 'The process of splitting data into training and test sets is detailed, highlighting the significance of setting aside a portion as a test set, the repeated training iterations, and individual preferences in selecting the ratio.', 'The confusion matrix is used in classification learning to compare model predictions with actual values and determine accuracy, with the diagonal values indicating accuracy and the total sum of values equating to the test data set size.', 'The process of developing a machine learning model involves problem understanding, algorithm selection, model training, testing, and production deployment, with the possibility of iterative cycles for refinement and maintenance.', 'The concept of semi-supervised machine learning is detailed as a method addressing the challenge of obtaining labeled data for training, involving techniques to label a portion of the data to facilitate model training.', 'The chapter outlines unsupervised machine learning techniques, including clustering and association, where clustering groups similar objects and association identifies relationships between items.']}