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Machine Learning Tutorial | Machine Learning Course | Machine Learning Projects 2022 |Simplilearn

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In this video on Machine Learning with Python full course, you will understand the basics of machine learning, essential applications of machine learning, machine learning concepts and understand why mathematics, statistics, and linear algebra are crucial. We'll also learn about regularization, dimensionality reduction, PCA. We will perform a prediction analysis on the recently held US Elections. Finally, you will study the Machine Learning roadmap for 2021?
00:00:00 Machine Learning Basics
00:08:45 Top 10 applications of machine learning
00:13:40 Machine Learning Tutorial Part-1
0:14:26 Why Machine Learning
0:18:33 What is Machine Learning
0:25:15 Types of Machine Learning
0:25:27 Supervised Learning
0:27:47 Reinforcement Learning
0:29:07 Supervised vs Unsupervised Learning
0:39:23 Decision Trees
01:15:10 Machine Learning Tutorial Part-2
01:19:47 K-Means Algorithm
02:10:47 Mathematics for Machine Learning
2:11:15 What is Data?
02:12:07 Quantitative/Categorical Data
02:14:54 Qualitative/Categorical Data
02:15:12 Linear Algebra
02:38:01 Calculus
02:52:21 Statistics
03:05:16 Demo on Statistics
03:22:27 Probability
03:48:09 Demo on Naive Bayes
04:01:00 Linear Regression Analysis
04:20:37 Logistic Regression
04:38:35 Confusion Matrix
04:58:31 Decision Tree in Machine Learning
05:20:30 Random Forest
05:50:29 K Nearest Neighbors
06:16:56 Support Vector Machine
06:35:57 Regularization in ML
07:05:03 PCA
07:35:16 US Election Prediction
08:03:49 Machine Learning roadmap 2021
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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.
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{'title': 'Machine Learning Tutorial | Machine Learning Course | Machine Learning Projects 2022 |Simplilearn', 'heatmap': [{'end': 3848.601, 'start': 1771.073, 'weight': 0.909}, {'end': 4739.126, 'start': 4438.675, 'weight': 0.819}, {'end': 5328.694, 'start': 5029.333, 'weight': 0.732}], 'summary': 'This machine learning tutorial covers essential basics, algorithms like k-nearest neighbors, supervised and unsupervised learning, real-world applications such as traffic predictions, and impact on industries. it also includes practical applications of machine learning, support vector machines for recipe classification, svm and k-means in python, car data analysis, decision making with data, logistic regression, linear algebra, mathematics in data science, sampling and statistics fundamentals, probability and data analysis, python set, iteration, probability, and naive bayes model, linear regression, logistic regression, decision trees, machine learning techniques, introduction to machine learning, pca for dimensionality reduction, and us election twitter sentiment analysis.', 'chapters': [{'end': 613.65, 'segs': [{'end': 57.999, 'src': 'embed', 'start': 28.598, 'weight': 0, 'content': [{'end': 31.321, 'text': 'You will then know the essential applications of machine learning.', 'start': 28.598, 'duration': 2.723}, {'end': 37.388, 'text': 'You will understand machine learning concepts and learn why mathematics, statistics and linear algebra are crucial.', 'start': 31.702, 'duration': 5.686}, {'end': 44.937, 'text': 'Then we will focus on some vital machine learning algorithms, such as linear regression, logistic regression, decision trees,', 'start': 37.869, 'duration': 7.068}, {'end': 46.759, 'text': 'random forest and k-nearest neighbors.', 'start': 44.937, 'duration': 1.822}, {'end': 53.818, 'text': 'You will also learn about regularization, dimensionality reduction and principal component analysis.', 'start': 48.157, 'duration': 5.661}, {'end': 57.999, 'text': 'We will perform a prediction analysis on the recently held US elections as well.', 'start': 54.238, 'duration': 3.761}], 'summary': 'Learn essential machine learning applications and algorithms, covering linear regression, logistic regression, decision trees, random forest, and k-nearest neighbors, with a focus on mathematics, statistics, and linear algebra.', 'duration': 29.401, 'max_score': 28.598, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU28598.jpg'}, {'end': 358.305, 'src': 'embed', 'start': 330.776, 'weight': 2, 'content': [{'end': 334.557, 'text': 'Hence, the learning with unlabeled data is unsupervised learning.', 'start': 330.776, 'duration': 3.781}, {'end': 340.3, 'text': 'So, we saw supervised learning where the data was labeled and the unsupervised learning where the data was unlabeled.', 'start': 334.738, 'duration': 5.562}, {'end': 344.521, 'text': 'and then there is reinforcement learning, which is reward based learning.', 'start': 340.88, 'duration': 3.641}, {'end': 346.962, 'text': 'or we can say that it works on the principle of feedback.', 'start': 344.521, 'duration': 2.441}, {'end': 351.963, 'text': "here let's say you provide the system with an image of a dog and ask it to identify it.", 'start': 346.962, 'duration': 5.001}, {'end': 358.305, 'text': "the system identifies it as a cat, so you give a negative feedback to the machine, saying that it's a dog's image.", 'start': 351.963, 'duration': 6.342}], 'summary': 'Unsupervised learning uses unlabeled data. reinforcement learning is based on feedback.', 'duration': 27.529, 'max_score': 330.776, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU330776.jpg'}, {'end': 553.823, 'src': 'embed', 'start': 525.782, 'weight': 1, 'content': [{'end': 528.604, 'text': 'Machine learning has improved our lives in a number of wonderful ways.', 'start': 525.782, 'duration': 2.822}, {'end': 530.406, 'text': "Today, let's talk about some of these.", 'start': 528.804, 'duration': 1.602}, {'end': 534.329, 'text': "I'm Rahul from Simply Learn and these are the top 10 applications of machine learning.", 'start': 530.586, 'duration': 3.743}, {'end': 536.971, 'text': "First, let's talk about virtual personal assistants.", 'start': 534.589, 'duration': 2.382}, {'end': 541.074, 'text': 'Google Assistant, Alexa, Cortana and Siri.', 'start': 537.411, 'duration': 3.663}, {'end': 543.996, 'text': "Now, we've all used one of these at least at some point in our lives.", 'start': 541.334, 'duration': 2.662}, {'end': 546.558, 'text': 'Now, these help improve our lives in a great number of ways.', 'start': 544.217, 'duration': 2.341}, {'end': 548.6, 'text': 'For example, you could tell them to call someone.', 'start': 546.819, 'duration': 1.781}, {'end': 550.301, 'text': 'You could tell them to play some music.', 'start': 548.92, 'duration': 1.381}, {'end': 552.202, 'text': 'You could tell them to even schedule an appointment.', 'start': 550.421, 'duration': 1.781}, {'end': 553.823, 'text': 'So how do these things actually work??', 'start': 552.522, 'duration': 1.301}], 'summary': 'Machine learning has enhanced lives through virtual personal assistants like google assistant, alexa, cortana, and siri, enabling tasks such as making calls, playing music, and scheduling appointments.', 'duration': 28.041, 'max_score': 525.782, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU525782.jpg'}], 'start': 7.677, 'title': 'Machine learning basics, algorithms, applications, and learning types', 'summary': 'Covers essential machine learning basics, algorithms like k-nearest neighbors, supervised and unsupervised learning, along with real-world applications like virtual personal assistants, traffic predictions, and surge pricing models.', 'chapters': [{'end': 330.556, 'start': 7.677, 'title': 'Machine learning basics and algorithms', 'summary': 'Covers the basics of machine learning, including essential applications, vital algorithms such as k-nearest neighbors, and the concepts of supervised and unsupervised learning.', 'duration': 322.879, 'highlights': ['The chapter covers the basics of machine learning, including essential applications, vital algorithms such as k-nearest neighbors, and the concepts of supervised and unsupervised learning. The chapter includes coverage of essential applications of machine learning, vital algorithms such as k-nearest neighbors, and the concepts of supervised and unsupervised learning.', "Paul likes the song with fast tempo and soaring intensity, while he dislikes the song with relaxed tempo and light intensity. Paul's song preferences are based on tempo and intensity, where fast tempo and soaring intensity are liked, while relaxed tempo and light intensity are disliked.", 'In the same example, for song B, if we draw a circle around the song B, we see that there are four votes for like, whereas one vote for dislike. For song B, there are four votes for like and one vote for dislike, illustrating how the k-nearest neighbors algorithm can be used for classification.']}, {'end': 613.65, 'start': 330.776, 'title': 'Machine learning applications & learning types', 'summary': 'Explains the concepts of supervised, unsupervised, and reinforcement learning, along with real-world applications of machine learning such as virtual personal assistants, traffic predictions, and surge pricing models.', 'duration': 282.874, 'highlights': ['The chapter explains the concepts of supervised, unsupervised, and reinforcement learning It distinguishes between supervised learning with labeled data, unsupervised learning with unlabeled data, and reinforcement learning based on feedback.', 'Real-world applications of machine learning such as virtual personal assistants, traffic predictions, and surge pricing models are discussed Examples include virtual personal assistants like Google Assistant, traffic predictions on Google Maps, and surge pricing models used by taxi companies like Uber.', 'The significant amount of data and enhanced computational capabilities have made machine learning possible The availability of vast amounts of online data and the improved memory handling and computational powers of computers have enabled the growth of machine learning applications.']}], 'duration': 605.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU7677.jpg', 'highlights': ['The chapter covers the basics of machine learning, including essential applications, vital algorithms such as k-nearest neighbors, and the concepts of supervised and unsupervised learning.', 'Real-world applications of machine learning such as virtual personal assistants, traffic predictions, and surge pricing models are discussed.', 'The chapter explains the concepts of supervised, unsupervised, and reinforcement learning.']}, {'end': 1717.385, 'segs': [{'end': 697.105, 'src': 'embed', 'start': 669.571, 'weight': 1, 'content': [{'end': 674.773, 'text': "So here with the help of machine learning Google has understood that I'm interested in this particular product.", 'start': 669.571, 'duration': 5.202}, {'end': 677.195, 'text': "Hence it's targeting me with these advertisements.", 'start': 674.914, 'duration': 2.281}, {'end': 679.176, 'text': 'This is also with the help of machine learning.', 'start': 677.355, 'duration': 1.821}, {'end': 681.197, 'text': "Let's talk about email spam filtering.", 'start': 679.336, 'duration': 1.861}, {'end': 683.318, 'text': "Now this is a spam that's in my inbox.", 'start': 681.437, 'duration': 1.881}, {'end': 686.299, 'text': "Now, how does Gmail know what's spam and what's not spam?", 'start': 683.478, 'duration': 2.821}, {'end': 691.142, 'text': 'So Gmail has an entire collection of emails which have already been labeled as spam or not spam.', 'start': 686.499, 'duration': 4.643}, {'end': 697.105, 'text': 'So after analyzing this data Gmail is able to find some characteristics like the word lottery or winner.', 'start': 691.382, 'duration': 5.723}], 'summary': 'Google uses machine learning to target ads and filter spam emails.', 'duration': 27.534, 'max_score': 669.571, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU669571.jpg'}, {'end': 784.633, 'src': 'embed', 'start': 750.248, 'weight': 0, 'content': [{'end': 753.73, 'text': 'Machine learning is used extensively when it comes to stock market trading.', 'start': 750.248, 'duration': 3.482}, {'end': 755.971, 'text': 'Now you have stock market indices like Nikkei.', 'start': 753.95, 'duration': 2.021}, {'end': 758.393, 'text': 'They use long short-term memory neural networks.', 'start': 756.092, 'duration': 2.301}, {'end': 763.937, 'text': 'Now these are used to classify, process and predict data when there are time lags of unknown size and duration.', 'start': 758.553, 'duration': 5.384}, {'end': 766.299, 'text': 'Now this is used to predict stock market trends.', 'start': 764.237, 'duration': 2.062}, {'end': 768.1, 'text': 'Assistive medical technology.', 'start': 766.699, 'duration': 1.401}, {'end': 770.622, 'text': 'Now medical technology has been innovated.', 'start': 768.28, 'duration': 2.342}, {'end': 773.845, 'text': 'With the help of machine learning, diagnosing diseases has been easier.', 'start': 770.762, 'duration': 3.083}, {'end': 778.448, 'text': 'From which we can create 3D models that can predict where exactly there are lesions in the brain.', 'start': 774.005, 'duration': 4.443}, {'end': 781.751, 'text': 'It works just as well for brain tumors and isochemic stroke lesions.', 'start': 778.608, 'duration': 3.143}, {'end': 784.633, 'text': 'They can also be used in fetal imaging and cardiac analysis.', 'start': 781.911, 'duration': 2.722}], 'summary': 'Machine learning predicts stock market trends and aids in medical diagnosis and imaging.', 'duration': 34.385, 'max_score': 750.248, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU750248.jpg'}, {'end': 990.944, 'src': 'embed', 'start': 963.891, 'weight': 4, 'content': [{'end': 970.773, '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': 963.891, 'duration': 6.882}, {'end': 975.334, 'text': "So in this case, we're using Facebook, but this is, of course, across all the different social media.", 'start': 971.073, 'duration': 4.261}, {'end': 976.834, 'text': 'They have different tools for billing.', 'start': 975.514, 'duration': 1.32}, {'end': 982.238, 'text': 'And the Facebook scroll GIF will be replaced, kind of like a virus coming in there.', 'start': 977.114, 'duration': 5.124}, {'end': 986.201, 'text': "It notices that there's a certain setup with Facebook and it's able to replace it.", 'start': 982.338, 'duration': 3.863}, {'end': 990.944, 'text': 'And they have like vote baiting, react baiting, share baiting.', 'start': 986.741, 'duration': 4.203}], 'summary': 'Company trained model to detect engagement bait in hundreds of thousands of posts across various social media platforms.', 'duration': 27.053, 'max_score': 963.891, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU963891.jpg'}, {'end': 1078.334, 'src': 'embed', 'start': 1054.898, 'weight': 5, 'content': [{'end': 1062.265, 'text': "A computer program that plays a board game Go has defeated the world's number one Go player, and I hope I say his name right, Kiji.", 'start': 1054.898, 'duration': 7.367}, {'end': 1069.933, 'text': 'The ultimate Go challenge game of three of three was on May 27th, 2017, so that was just last year that this happened.', 'start': 1062.786, 'duration': 7.147}, {'end': 1075.294, 'text': 'And what makes this so important is that, you know, Go is just a game.', 'start': 1070.373, 'duration': 4.921}, {'end': 1078.334, 'text': "So it's not like you're driving a car or something in our real world.", 'start': 1075.354, 'duration': 2.98}], 'summary': "In 2017, a computer program defeated the world's number one go player in a historic match.", 'duration': 23.436, 'max_score': 1054.898, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU1054898.jpg'}, {'end': 1142.682, 'src': 'embed', 'start': 1117.69, 'weight': 6, 'content': [{'end': 1125.555, 'text': 'Machine learning is the science of making computers learn and act like humans by feeding data and information without being explicitly programmed.', 'start': 1117.69, 'duration': 7.865}, {'end': 1129.656, 'text': 'We see here we have a nice little diagram where we have our ordinary system.', 'start': 1126.195, 'duration': 3.461}, {'end': 1135.619, 'text': 'Your computer nowadays, it can even run a lot of this stuff on a cell phone because cell phones advance so much.', 'start': 1130.497, 'duration': 5.122}, {'end': 1142.682, 'text': 'Then with artificial intelligence and machine learning, it now takes the data and it learns from what happened before.', 'start': 1136.199, 'duration': 6.483}], 'summary': 'Machine learning enables computers to learn from data, advancing to cell phone capabilities.', 'duration': 24.992, 'max_score': 1117.69, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU1117690.jpg'}, {'end': 1550.258, 'src': 'embed', 'start': 1523.434, 'weight': 8, 'content': [{'end': 1532.437, 'text': 'Supervised learning is a method used to enable machines to classify, predict objects, problems or situations based on labeled data fed to the machine.', 'start': 1523.434, 'duration': 9.003}, {'end': 1538.304, 'text': 'And in here, you see we have a jumble of data with circles, triangles, and squares, and we label them.', 'start': 1532.777, 'duration': 5.527}, {'end': 1544.171, 'text': "We have what's a circle, what's a triangle, what's a square, and we have our model training, and it trains it so we know the answer.", 'start': 1538.404, 'duration': 5.767}, {'end': 1550.258, 'text': "Very important, when you're doing supervised learning, you already know the answer to a lot of your information coming in.", 'start': 1544.391, 'duration': 5.867}], 'summary': 'Supervised learning trains machines to classify objects based on labeled data.', 'duration': 26.824, 'max_score': 1523.434, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU1523434.jpg'}, {'end': 1695.22, 'src': 'embed', 'start': 1666.75, 'weight': 9, 'content': [{'end': 1674.714, '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': 1666.75, 'duration': 7.964}, {'end': 1677.475, 'text': 'We have here, in this case, a baby.', 'start': 1675.254, 'duration': 2.221}, {'end': 1684.177, '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': 1677.815, 'duration': 6.362}, {'end': 1695.22, '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': 1684.677, 'duration': 10.543}], 'summary': 'Reinforcement learning is in its early stages but is expected to grow in demand over the next few years.', 'duration': 28.47, 'max_score': 1666.75, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU1666750.jpg'}], 'start': 614.011, 'title': 'The impact and future of machine learning', 'summary': "Delves into various applications of machine learning, including traffic prediction, social media personalization, email spam filtering, online fraud detection, stock market trading, assistive medical technology, and automatic translation, highlighting their impact on industries and daily life. it also discusses facebook's use of machine learning to eliminate engagement bait, improve user experience, and reduce annoyance, as well as the significance, applications, and divisions of machine learning, emphasizing its future role in automation.", 'chapters': [{'end': 928.187, 'start': 614.011, 'title': 'Machine learning applications', 'summary': 'Explores various applications of machine learning, such as traffic prediction, social media personalization, email spam filtering, online fraud detection, stock market trading, assistive medical technology, and automatic translation, emphasizing the impact on industries and daily life.', 'duration': 314.176, 'highlights': ['Machine learning applications in traffic prediction and social media personalization are explained, emphasizing the use of real-time data and personalized advertisements. Real-time data for traffic prediction, personalized advertisements based on user interests.', 'Explanation of email spam filtering using machine learning, highlighting the use of labeled data and various spam filters to classify emails. Use of labeled data for spam classification, different spam filters employed by Gmail.', 'Description of online fraud detection using feed-forward neural networks to identify fraudulent transactions, with an emphasis on pattern recognition and hash value comparison. Use of feed-forward neural networks, pattern recognition in fraudulent transactions.', 'Application of machine learning in stock market trading, particularly the use of long short-term memory neural networks for predicting stock market trends. Use of long short-term memory neural networks for stock market trend prediction.', 'Impact of machine learning on assistive medical technology, including disease diagnosis, 3D modeling for brain lesions, and applications in fetal imaging and cardiac analysis. Machine learning applications in disease diagnosis and 3D modeling for medical imaging.', 'Explanation of automatic translation using machine learning, detailing the technologies involved such as sequence-to-sequence learning, convolutional neural networks, and optical character recognition. Use of sequence-to-sequence learning, convolutional neural networks, and optical character recognition for automatic translation.']}, {'end': 1078.334, 'start': 928.487, 'title': "Facebook's engagement baiting tactics", 'summary': "Discusses facebook's effort to eliminate engagement bait by using machine learning to detect and replace spam posts, scanning for keywords and phrases to identify spam, ultimately improving user experience and reducing annoyance, while also highlighting google's alphago defeating the world's number one go player in 2017.", 'duration': 149.847, 'highlights': ["Facebook's use of machine learning to detect and replace engagement baiting spam posts The company reviewed and categorized hundreds of thousands of posts to train a machine learning model that detects different types of engagement bait, ultimately improving user experience.", 'Scanning for keywords and phrases to identify and eliminate spam on Facebook Facebook scans for keywords and phrases to identify spam, improving user experience by reducing annoyance caused by spam posts.', "Google's AlphaGo defeating the world's number one Go player in 2017 In May 2017, Google's AlphaGo defeated the world's number one Go player, Kiji, highlighting the advancements in artificial intelligence and machine learning."]}, {'end': 1717.385, 'start': 1078.574, 'title': 'Machine learning: the future of automation', 'summary': 'Introduces the concept of machine learning, explaining its significance and applications, detailing the process of machine learning, and discussing its divisions, including supervised, unsupervised, and reinforcement learning, as well as the key areas of classification, regression, anomaly detection, and clustering.', 'duration': 638.811, 'highlights': ['Machine learning is the science of making computers learn and act like humans by feeding data and information without being explicitly programmed. Describes the essence of machine learning and its objective to make computers learn and act like humans without explicit programming.', 'The process of machine learning involves defining objectives, collecting and preparing data, selecting and training an algorithm, testing the model, running predictions, and deploying the model. Details the step-by-step process of machine learning, including defining objectives, data collection and preparation, algorithm selection and training, model testing, prediction, and deployment.', 'Supervised learning enables machines to classify, predict objects, problems, or situations based on labeled data, while in unsupervised learning, the machine learning model finds hidden patterns in unlabeled data. Explains the distinction between supervised and unsupervised learning and their respective approaches to labeled and unlabeled data for classification and pattern recognition.', 'Reinforcement learning involves an agent learning how to behave in an environment by performing actions and observing the results. Introduces reinforcement learning and its principle of learning through actions and consequences within an environment.']}], 'duration': 1103.374, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU614011.jpg', 'highlights': ['Machine learning applications in traffic prediction and social media personalization are explained, emphasizing the use of real-time data and personalized advertisements.', 'Explanation of email spam filtering using machine learning, highlighting the use of labeled data and various spam filters to classify emails.', 'Application of machine learning in stock market trading, particularly the use of long short-term memory neural networks for predicting stock market trends.', 'Impact of machine learning on assistive medical technology, including disease diagnosis, 3D modeling for brain lesions, and applications in fetal imaging and cardiac analysis.', "Facebook's use of machine learning to detect and replace engagement baiting spam posts.", "Google's AlphaGo defeating the world's number one Go player in 2017.", 'Machine learning is the science of making computers learn and act like humans by feeding data and information without being explicitly programmed.', 'The process of machine learning involves defining objectives, collecting and preparing data, selecting and training an algorithm, testing the model, running predictions, and deploying the model.', 'Supervised learning enables machines to classify, predict objects, problems, or situations based on labeled data, while in unsupervised learning, the machine learning model finds hidden patterns in unlabeled data.', 'Reinforcement learning involves an agent learning how to behave in an environment by performing actions and observing the results.']}, {'end': 2851.412, 'segs': [{'end': 1767.388, 'src': 'embed', 'start': 1717.405, 'weight': 0, 'content': [{'end': 1720.646, 'text': "They went a different direction and now the baby's happy and laughing and playing.", 'start': 1717.405, 'duration': 3.241}, {'end': 1726.628, 'text': "Reinforcement learning is very easy to understand because that's how, as humans, that's one of the ways we learn.", 'start': 1721.106, 'duration': 5.522}, {'end': 1730.65, 'text': "We learn whether it is You know, you burn yourself on the stove, don't do that anymore.", 'start': 1726.748, 'duration': 3.902}, {'end': 1731.591, 'text': "Don't touch the stove.", 'start': 1730.71, 'duration': 0.881}, {'end': 1737.976, 'text': 'In the big picture, being able to have a machine learning program or an AI be able to do this is huge,', 'start': 1732.051, 'duration': 5.925}, {'end': 1740.998, 'text': "because now we're starting to learn how to learn.", 'start': 1737.976, 'duration': 3.022}, {'end': 1744.741, 'text': "That's a big jump in the world of computer and machine learning.", 'start': 1741.258, 'duration': 3.483}, {'end': 1749.965, 'text': "And we're going to go back and just kind of go back over supervised versus unsupervised learning.", 'start': 1745.021, 'duration': 4.944}, {'end': 1754.709, 'text': "Understanding this is huge because this is going to come up in any project you're working on.", 'start': 1750.245, 'duration': 4.464}, {'end': 1758.555, 'text': 'We have in supervised learning, we have labeled data.', 'start': 1755.31, 'duration': 3.245}, {'end': 1760.217, 'text': 'We have direct feedback.', 'start': 1758.835, 'duration': 1.382}, {'end': 1763.402, 'text': "So someone's already gone in there and said, yes, that's a triangle.", 'start': 1760.498, 'duration': 2.904}, {'end': 1764.704, 'text': "No, that's not a triangle.", 'start': 1763.602, 'duration': 1.102}, {'end': 1766.186, 'text': 'And then you predict an outcome.', 'start': 1764.984, 'duration': 1.202}, {'end': 1767.388, 'text': 'So you have a nice prediction.', 'start': 1766.366, 'duration': 1.022}], 'summary': 'Reinforcement learning is crucial in ai, with supervised learning having labeled data and direct feedback.', 'duration': 49.983, 'max_score': 1717.405, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU1717405.jpg'}, {'end': 2532.21, 'src': 'embed', 'start': 2506.268, 'weight': 5, 'content': [{'end': 2511.858, 'text': "So how do we decide how to split that data up? And is that the right decision tree? But So that's the question that's going to come up.", 'start': 2506.268, 'duration': 5.59}, {'end': 2518.146, 'text': 'Is this the right decision tree? For that, we should calculate entropy and information gain.', 'start': 2512.158, 'duration': 5.988}, {'end': 2522.913, 'text': 'Two important vocabulary words there are the entropy and the information gain.', 'start': 2518.467, 'duration': 4.446}, {'end': 2527.92, 'text': 'Entropy Entropy is a measure of randomness or impurity in the data set.', 'start': 2523.313, 'duration': 4.607}, {'end': 2532.21, 'text': 'Entropy should be low, so we want the chaos to be as low as possible.', 'start': 2528.447, 'duration': 3.763}], 'summary': 'Decide data split using entropy & information gain to minimize chaos.', 'duration': 25.942, 'max_score': 2506.268, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU2506268.jpg'}], 'start': 1717.405, 'title': 'Machine learning and decision making with data', 'summary': 'Covers machine learning fundamentals such as reinforcement learning, supervised and unsupervised learning, and linear regression, with practical applications. it also explores linear regression in the context of speed, time, and error analysis, and decision making using data with decision trees for outdoor activity recommendations.', 'chapters': [{'end': 2002.785, 'start': 1717.405, 'title': 'Machine learning fundamentals', 'summary': 'Explains the fundamentals of reinforcement learning, supervised and unsupervised learning, and linear regression, emphasizing their importance in machine learning and their practical applications, including predicting outcomes and finding hidden structures in data.', 'duration': 285.38, 'highlights': ['Understanding supervised and unsupervised learning is crucial for any project, with supervised learning involving labeled data and direct feedback, and unsupervised learning focusing on finding hidden structures in unlabeled data. Supervised learning involves labeled data and direct feedback, while unsupervised learning focuses on finding hidden structures in unlabeled data, essential for any project.', 'Linear regression is a well-known and well-understood algorithm in statistics and machine learning, used to predict outcomes based on a linear relationship between input and output variables. Linear regression is a widely used algorithm in statistics and machine learning, predicting outcomes based on a linear relationship between input and output variables.', 'Reinforcement learning is a fundamental concept, similar to human learning, and being able to apply it to AI and machine learning is a significant advancement. Reinforcement learning is fundamental and applicable to AI and machine learning, representing a significant advancement.']}, {'end': 2374.808, 'start': 2003.225, 'title': 'Speed, time, and linear regression', 'summary': 'Discusses the relationship between speed and time, as well as the mathematical implementation of linear regression using a simple dataset, calculation of regression equation, error analysis, and ways to minimize error in linear regression models.', 'duration': 371.583, 'highlights': ['The chapter discusses the relationship between speed and time, as well as the mathematical implementation of linear regression using a simple dataset. The relationship between speed and time is explored, and a simple dataset with x and y values is used for the implementation of linear regression.', 'Calculation of the regression equation and the process of finding the best fit line are explained. The process of calculating the regression equation, finding the slope and coefficient, and ensuring the line goes through the mean values is detailed.', 'Error analysis in linear regression and the goal of minimizing the error value are discussed, along with methods such as sum of squared errors and sum of absolute errors. The importance of error analysis in linear regression is emphasized, and various methods to minimize the distance between data points and the regression line are mentioned.', 'Introduction to decision trees as a different approach to problem-solving compared to linear regression is provided. The chapter introduces decision trees as a tree-shaped algorithm for determining a course of action, representing possible decisions, occurrences, or reactions.']}, {'end': 2851.412, 'start': 2374.808, 'title': 'Decision making with data', 'summary': 'Explains how decision trees are used to determine the best day for playing golf based on weather conditions, calculating entropy and information gain, and building a decision tree to make recommendations for outdoor activities.', 'duration': 476.604, 'highlights': ['Decision trees are used to determine the best day for playing golf based on weather conditions The chapter discusses using decision trees to analyze weather data and make recommendations for playing golf based on factors like outlook, temperature, humidity, and wind.', 'Calculating entropy and information gain to make data-driven decisions The chapter explains the concepts of entropy and information gain, emphasizing the importance of minimizing entropy and maximizing information gain to make accurate data-driven decisions.', 'Building a decision tree to make recommendations for outdoor activities The chapter illustrates the process of building a decision tree to provide recommendations for outdoor activities based on weather conditions, demonstrating how to navigate the tree to make informed decisions.']}], 'duration': 1134.007, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU1717405.jpg', 'highlights': ['Reinforcement learning is fundamental and applicable to AI and machine learning, representing a significant advancement.', 'Linear regression is a widely used algorithm in statistics and machine learning, predicting outcomes based on a linear relationship between input and output variables.', 'Understanding supervised and unsupervised learning is crucial for any project, with supervised learning involving labeled data and direct feedback, and unsupervised learning focusing on finding hidden structures in unlabeled data, essential for any project.', 'The chapter introduces decision trees as a tree-shaped algorithm for determining a course of action, representing possible decisions, occurrences, or reactions.', 'The chapter discusses using decision trees to analyze weather data and make recommendations for playing golf based on factors like outlook, temperature, humidity, and wind.', 'The importance of error analysis in linear regression is emphasized, and various methods to minimize the distance between data points and the regression line are mentioned.', 'The chapter explains the concepts of entropy and information gain, emphasizing the importance of minimizing entropy and maximizing information gain to make accurate data-driven decisions.', 'The relationship between speed and time is explored, and a simple dataset with x and y values is used for the implementation of linear regression.']}, {'end': 4446.62, 'segs': [{'end': 2962.143, 'src': 'embed', 'start': 2932.895, 'weight': 7, 'content': [{'end': 2939.12, 'text': "So we actually have a value, and it should be equally distant between the two points that we're comparing it to.", 'start': 2932.895, 'duration': 6.225}, {'end': 2943.904, 'text': 'When we draw the hyperplanes we observe that line 1 has a maximum distance.', 'start': 2939.48, 'duration': 4.424}, {'end': 2947.587, 'text': 'so we observe that line 1 has a maximum distance margin.', 'start': 2943.904, 'duration': 3.683}, {'end': 2949.729, 'text': "so we'll classify the new data point correctly.", 'start': 2947.587, 'duration': 2.142}, {'end': 2953.513, 'text': 'And our result on this one is going to be that the new data point is mel.', 'start': 2950.19, 'duration': 3.323}, {'end': 2962.143, 'text': "One of the reasons we call it a hyperplane versus a line is that a lot of times we're not looking at just weight and height.", 'start': 2954.139, 'duration': 8.004}], 'summary': "Using hyperplanes to classify data points with maximum distance margin, resulting in correct classification of new data point as 'mel'.", 'duration': 29.248, 'max_score': 2932.895, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU2932895.jpg'}, {'end': 3002.345, 'src': 'embed', 'start': 2974.308, 'weight': 5, 'content': [{'end': 2978.67, 'text': 'And each plane continues to cut it down until we get the best fit or match.', 'start': 2974.308, 'duration': 4.362}, {'end': 2981.151, 'text': "Let's understand this with the help of an example.", 'start': 2979.19, 'duration': 1.961}, {'end': 2982.192, 'text': 'Problem statement.', 'start': 2981.491, 'duration': 0.701}, {'end': 2984.773, 'text': "You always start with a problem statement when you're going to put some code together.", 'start': 2982.292, 'duration': 2.481}, {'end': 2985.814, 'text': "We're going to do some coding now.", 'start': 2984.793, 'duration': 1.021}, {'end': 2990.457, 'text': 'Classifying muffin and cupcake recipes using support vector machines.', 'start': 2986.134, 'duration': 4.323}, {'end': 2992.719, 'text': 'So the cupcake versus the muffin.', 'start': 2990.777, 'duration': 1.942}, {'end': 2994.66, 'text': "Let's have a look at our data set.", 'start': 2993.239, 'duration': 1.421}, {'end': 2997.022, 'text': 'And we have the different recipes here.', 'start': 2995.02, 'duration': 2.002}, {'end': 2999.943, 'text': 'We have a muffin recipe that has so much flour.', 'start': 2997.062, 'duration': 2.881}, {'end': 3002.345, 'text': "I'm not sure what measurement 55 is in, but it has 55.", 'start': 2999.963, 'duration': 2.382}], 'summary': 'Classify muffin and cupcake recipes using support vector machines', 'duration': 28.037, 'max_score': 2974.308, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU2974308.jpg'}, {'end': 3298.177, 'src': 'embed', 'start': 3254.322, 'weight': 2, 'content': [{'end': 3259.204, 'text': "We're really just focusing on the SVM, the support vector machine from sklearn.", 'start': 3254.322, 'duration': 4.882}, {'end': 3267.166, 'text': "And since we're in Jupyter Notebook, we have to add a special line in here for our MatplotLibrary.", 'start': 3259.784, 'duration': 7.382}, {'end': 3269.367, 'text': "And that's your % or &MatplotLibrary inline.", 'start': 3267.686, 'duration': 1.681}, {'end': 3280.653, 'text': "Now, if you're doing this in just a straight code project, a lot of times I use like Notepad++, and I'll run it from there.", 'start': 3273.171, 'duration': 7.482}, {'end': 3286.534, 'text': "You don't have to have that line in there, because it'll just pop up as its own window on your computer, depending on how your computer's set up.", 'start': 3281.013, 'duration': 5.521}, {'end': 3296.416, 'text': "Because we're running this in the Jupyter Notebook as a browser setup, this tells it to display all of our graphics right below on the page.", 'start': 3287.054, 'duration': 9.362}, {'end': 3298.177, 'text': "So that's what that line is for.", 'start': 3296.837, 'duration': 1.34}], 'summary': 'Focusing on svm from sklearn in jupyter notebook, displaying graphics inline.', 'duration': 43.855, 'max_score': 3254.322, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU3254322.jpg'}, {'end': 3623.588, 'src': 'embed', 'start': 3592.194, 'weight': 0, 'content': [{'end': 3593.814, 'text': "And we're going to break that up into two parts.", 'start': 3592.194, 'duration': 1.62}, {'end': 3601.717, 'text': "We need a type label, and remember, we're going to decide whether it's a muffin or a cupcake.", 'start': 3596.335, 'duration': 5.382}, {'end': 3603.877, 'text': "Well, a computer doesn't know muffin or cupcake.", 'start': 3601.857, 'duration': 2.02}, {'end': 3605.358, 'text': 'It knows zero and one.', 'start': 3603.937, 'duration': 1.421}, {'end': 3608.478, 'text': "So, what we're going to do is we're going to create a type label.", 'start': 3606.238, 'duration': 2.24}, {'end': 3613.94, 'text': "And from this, we'll create a numpy array, np, where..", 'start': 3608.498, 'duration': 5.442}, {'end': 3616.882, 'text': 'And this is where we can do some logic.', 'start': 3615, 'duration': 1.882}, {'end': 3623.588, 'text': "We take our recipes from our panda and wherever a type equals muffin, it's going to be zero.", 'start': 3617.162, 'duration': 6.426}], 'summary': 'Data processing to create numpy array for muffin and cupcake classification', 'duration': 31.394, 'max_score': 3592.194, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU3592194.jpg'}, {'end': 3755.133, 'src': 'embed', 'start': 3725.41, 'weight': 4, 'content': [{'end': 3727.871, 'text': 'We actually need the ingredients.', 'start': 3725.41, 'duration': 2.461}, {'end': 3734.035, 'text': 'And at this point, we have a couple options.', 'start': 3731.633, 'duration': 2.402}, {'end': 3736.797, 'text': 'One, we could run it over all the ingredients.', 'start': 3734.576, 'duration': 2.221}, {'end': 3740.08, 'text': "And when you're doing this, usually you do.", 'start': 3737.918, 'duration': 2.162}, {'end': 3743.803, 'text': "But for our example, we want to limit it so you can easily see what's going on.", 'start': 3740.14, 'duration': 3.663}, {'end': 3751.93, 'text': "Because if we did all the ingredients, we have, you know, that's what, seven, eight different hyperplanes that would be built into it.", 'start': 3743.883, 'duration': 8.047}, {'end': 3755.133, 'text': 'We only want to look at one so you can see what the SVM is doing.', 'start': 3752.11, 'duration': 3.023}], 'summary': 'Limiting ingredients to one for clear visualization in svm.', 'duration': 29.723, 'max_score': 3725.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU3725410.jpg'}, {'end': 3954.521, 'src': 'embed', 'start': 3917.093, 'weight': 1, 'content': [{'end': 3924.419, 'text': 'we split it into training data and test data, and they even do something where they split it into thirds, where a third is used for,', 'start': 3917.093, 'duration': 7.326}, {'end': 3926.741, 'text': "where you switch between which one's training and test.", 'start': 3924.419, 'duration': 2.322}, {'end': 3930.864, 'text': "There's all kinds of things that go into that, and it gets very complicated when you get to the higher end.", 'start': 3926.941, 'duration': 3.923}, {'end': 3936.449, 'text': "Not overly complicated, just an extra step, which we're not going to do today, because this is a very simple set of data.", 'start': 3931.405, 'duration': 5.044}, {'end': 3939.303, 'text': "let's go ahead and run this.", 'start': 3937.941, 'duration': 1.362}, {'end': 3943.067, 'text': 'and now we have our model fit and I got an error here.', 'start': 3939.303, 'duration': 3.764}, {'end': 3944.349, 'text': 'so let me fix that real quick.', 'start': 3943.067, 'duration': 1.282}, {'end': 3945.93, 'text': "it's capital SVC.", 'start': 3944.349, 'duration': 1.581}, {'end': 3953.88, 'text': 'it turns out I did it lowercase support vector classifier.', 'start': 3945.93, 'duration': 7.95}, {'end': 3954.521, 'text': 'there we go.', 'start': 3953.88, 'duration': 0.641}], 'summary': 'Data split into training and test sets, model fit with minor error corrected.', 'duration': 37.428, 'max_score': 3917.093, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU3917093.jpg'}], 'start': 2852.252, 'title': 'Support vector machines for recipe classification', 'summary': "Covers the concept of support vector machines, their application in classifying muffin and cupcake recipes, analyzing cupcake vs muffin data using python's pandas and seaborn, and implementing a support vector classifier for accurate muffin-cupcake prediction.", 'chapters': [{'end': 3332.81, 'start': 2852.252, 'title': 'Understanding support vector machines', 'summary': 'Explains the concept of support vector machines, the process of choosing a hyperplane with the greatest possible margin, and its application in classifying muffin and cupcake recipes, using python in a jupyter notebook environment.', 'duration': 480.558, 'highlights': ['Support vector machine creates a separation line which divides the classes in the best possible manner, with the goal of choosing a hyperplane with the greatest possible margin between the decision line and the nearest point within the training set. Explains the fundamental concept of support vector machines and the goal of choosing a hyperplane with the greatest possible margin.', 'Application of support vector machines in classifying muffin and cupcake recipes with 8 different features in a Python environment using Anaconda and Jupyter Notebook. Describes the practical application of support vector machines in classifying muffin and cupcake recipes using Python in a Jupyter Notebook environment.', 'Introduction to using Python in a Jupyter Notebook environment for data analysis, including importing essential packages such as NumPy, Pandas, and SK learn for support vector machines. Provides an overview of using Python in a Jupyter Notebook environment for data analysis and importing essential packages for support vector machines.']}, {'end': 3556.776, 'start': 3333.09, 'title': 'Analyzing cupcakes vs muffins data', 'summary': "Explores analyzing a csv data file containing types of cupcakes and muffins, using python's pandas and seaborn to visualize the relationship between sugar and flour, with a focus on the first five lines of data and the plot of sugar and flour.", 'duration': 223.686, 'highlights': ["The chapter explores the process of analyzing a CSV file containing data on cupcakes and muffins using Python's Pandas module to read and display the first five lines of data. Analyzing CSV file, using Pandas module, displaying first five lines of data", 'The data is visualized using Seaborn to plot the relationship between sugar and flour, demonstrating the correlation between these two features. Using Seaborn for data visualization, plotting sugar and flour relationship']}, {'end': 3930.864, 'start': 3558.937, 'title': 'Baking svm model for muffin vs cupcake', 'summary': 'Discusses using svm for classifying muffin vs cupcake recipes by creating type labels, training the model with flour and sugar ingredients, and fitting a linear kernel support vector classifier, with a focus on explaining the process and logic behind each step.', 'duration': 371.927, 'highlights': ['By creating type labels and converting them into a numpy array, the model is trained to distinguish between muffin (labeled as 0) and cupcake (labeled as 1), which is crucial for the SVM classification.', 'The process of creating recipe features involves removing the type column and converting the remaining columns into a list of strings, ensuring the accurate representation of features in the model training process.', 'The fitting of the SVM model involves using a linear kernel support vector classifier (SVC), which is a commonly used approach for binary classification such as distinguishing between muffins and cupcakes, demonstrating the practical application of SVM in the context of recipe classification.', 'The discussion emphasizes the importance of model fitting using the model.fit command, where the ingredients (limited to flour and sugar in this case) and type label are used for training, showcasing the standard approach in SKLearn for training machine learning models and its relevance to this specific SVM application.', 'The chapter also briefly mentions the complexities of splitting data into training and test sets, highlighting the advanced considerations in data science that are beyond the scope of this discussion but acknowledging their significance in more comprehensive data analysis processes.']}, {'end': 4446.62, 'start': 3931.405, 'title': 'Support vector classifier for muffin-cupcake prediction', 'summary': 'Demonstrates the implementation of a support vector classifier for distinguishing between muffin and cupcake recipes using flour and sugar as features, resulting in a clear separation of the two types on a graph, and a function to predict whether a recipe is for a muffin or a cupcake, achieving an accurate prediction with a sample input of 50 and 20.', 'duration': 515.215, 'highlights': ['The chapter demonstrates the implementation of a support vector classifier for distinguishing between muffin and cupcake recipes using flour and sugar as features. The chapter focuses on implementing a support vector classifier to classify muffin and cupcake recipes using flour and sugar as features. This classifier aims to create a clear separation between the two types of recipes.', "A function is created to predict whether a recipe is for a muffin or a cupcake, achieving an accurate prediction with a sample input of 50 and 20. A function named 'muffin or cupcake' is created to predict whether a recipe is for a muffin or a cupcake based on flour and sugar content. The function accurately predicts a sample input of 50 and 20 as a muffin recipe.", "The model's mathematical background is explored, including coefficients and the creation of a separating hyperplane, demonstrating the mathematical foundation behind the support vector classifier. The chapter delves into the mathematical background of the model, including exploring coefficients and the creation of a separating hyperplane. This provides insight into the mathematical foundation underlying the support vector classifier.", 'The chapter demonstrates the plotting of the separating hyperplane and support vectors, visually showcasing the clear separation between muffin and cupcake recipes based on flour and sugar content. The chapter visually demonstrates the plotting of the separating hyperplane and support vectors, showcasing a clear separation between muffin and cupcake recipes based on flour and sugar content. This visual representation highlights the effectiveness of the support vector classifier.']}], 'duration': 1594.368, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU2852252.jpg', 'highlights': ['Explains the fundamental concept of support vector machines and the goal of choosing a hyperplane with the greatest possible margin.', 'Provides an overview of using Python in a Jupyter Notebook environment for data analysis and importing essential packages for support vector machines.', 'Describes the practical application of support vector machines in classifying muffin and cupcake recipes using Python in a Jupyter Notebook environment.', 'Analyzing CSV file, using Pandas module, displaying first five lines of data.', 'Using Seaborn for data visualization, plotting sugar and flour relationship.', 'The fitting of the SVM model involves using a linear kernel support vector classifier (SVC), which is a commonly used approach for binary classification such as distinguishing between muffins and cupcakes, demonstrating the practical application of SVM in the context of recipe classification.', 'The chapter focuses on implementing a support vector classifier to classify muffin and cupcake recipes using flour and sugar as features. This classifier aims to create a clear separation between the two types of recipes.', "A function named 'muffin or cupcake' is created to predict whether a recipe is for a muffin or a cupcake based on flour and sugar content. The function accurately predicts a sample input of 50 and 20 as a muffin recipe.", 'The chapter delves into the mathematical background of the model, including exploring coefficients and the creation of a separating hyperplane. This provides insight into the mathematical foundation underlying the support vector classifier.', 'The chapter visually demonstrates the plotting of the separating hyperplane and support vectors, showcasing a clear separation between muffin and cupcake recipes based on flour and sugar content. This visual representation highlights the effectiveness of the support vector classifier.']}, {'end': 5129.855, 'segs': [{'end': 4477.011, 'src': 'embed', 'start': 4446.62, 'weight': 1, 'content': [{'end': 4447.941, 'text': 'Those are settings you can play with.', 'start': 4446.62, 'duration': 1.321}, {'end': 4451.483, 'text': 'Somebody else played with them to come up with the right setup so it looks good.', 'start': 4448.141, 'duration': 3.342}, {'end': 4453.564, 'text': 'And you can see there it is graphed.', 'start': 4452.103, 'duration': 1.461}, {'end': 4454.965, 'text': 'Clearly a muffin.', 'start': 4454.104, 'duration': 0.861}, {'end': 4467.365, 'text': "this case, in cupcakes versus muffins, the muffin has won, and if you'd like to do your own muffin cupcake contender series,", 'start': 4456.798, 'duration': 10.567}, {'end': 4475.149, 'text': 'you certainly can send a note down below and the team at simply learn will send you over the data they use for the muffin and cupcake.', 'start': 4467.365, 'duration': 7.784}, {'end': 4477.011, 'text': "and that's true of any of the data.", 'start': 4475.149, 'duration': 1.862}], 'summary': "Settings can be adjusted to create a graph showing muffin's victory over cupcakes.", 'duration': 30.391, 'max_score': 4446.62, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU4446620.jpg'}, {'end': 4892.009, 'src': 'embed', 'start': 4862.682, 'weight': 0, 'content': [{'end': 4867.505, 'text': 'So cluster 1 and then cluster 2, and we look at each individual dot.', 'start': 4862.682, 'duration': 4.823}, {'end': 4868.204, 'text': "There's 1, 2, 3.", 'start': 4867.585, 'duration': 0.619}, {'end': 4870.246, 'text': "We're in one cluster.", 'start': 4868.205, 'duration': 2.041}, {'end': 4871.786, 'text': 'The centroid then moves over.', 'start': 4870.306, 'duration': 1.48}, {'end': 4874.447, 'text': 'It becomes 1.8, 2.3.', 'start': 4872.026, 'duration': 2.421}, {'end': 4876.808, 'text': 'So remember it was at 1 and 1.', 'start': 4874.447, 'duration': 2.361}, {'end': 4883.007, 'text': "Well, the very center of the data we're looking at would put it at roughly 2, 2, but 1.8 and 2.3.", 'start': 4876.808, 'duration': 6.199}, {'end': 4884.127, 'text': 'And the second one.', 'start': 4883.007, 'duration': 1.12}, {'end': 4892.009, 'text': 'if we wanted to make the overall mean vector, the average vector of all the different distances to that centroid, we come up with 4, 1, and 5, 4..', 'start': 4884.127, 'duration': 7.882}], 'summary': 'Analyzing clusters, centroid movement, and mean vector calculation with quantifiable data points.', 'duration': 29.327, 'max_score': 4862.682, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU4862682.jpg'}], 'start': 4446.62, 'title': 'Svm and k-means in python', 'summary': 'Covers building a svm classifier for cupcake vs muffin recipes, and introduces k-means clustering for organizing data into groups, with demonstrations on categorizing books and cars, along with a preview of upcoming tutorial on logistic regression and elbo method.', 'chapters': [{'end': 4555.028, 'start': 4446.62, 'title': 'Cupcake vs muffin: svm classifier and tutorial preview', 'summary': 'Covers building a svm classifier for cupcake vs muffin recipes, along with a preview of the upcoming tutorial on k-means clustering and logistic regression in python.', 'duration': 108.408, 'highlights': ['The chapter covers building a SVM classifier for cupcake vs muffin recipes A support vector machine code was used to classify recipes as either a cupcake or a muffin, with a specific example of predicting 40 parts flour and 20 parts sugar.', 'Preview of upcoming tutorial on k-means clustering and logistic regression in Python The tutorial will cover k-means clustering and logistic regression, including a live demo on clustering cars based on brands and classifying tumors as malignant or benign.', 'Option to request data for experimentation Learners are encouraged to request data used for classification and clustering experiments from the team at Simply Learn for their own testing and analysis.']}, {'end': 5129.855, 'start': 4555.488, 'title': 'K-means clustering for data organization', 'summary': 'Introduces k-means clustering as a method of organizing data into groups based on feature similarities, using the example of categorizing books into clusters, explaining the process of k-means clustering, and demonstrating the use of elbo method to find the appropriate number of clusters, with an application to cluster cars into brands.', 'duration': 574.367, 'highlights': ['The chapter introduces K-means clustering as a method of organizing data into groups based on feature similarities, using the example of categorizing books into clusters.', 'It explains the process of K-means clustering, including selecting initial cluster centroids, assigning data points to clusters based on distance from centroids, and iteratively updating centroids until convergence.', 'The use of ELBO method to find the appropriate number of clusters is demonstrated, with an application to cluster cars into brands using parameters such as horsepower, cubic inches, make, year, etc.']}], 'duration': 683.235, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU4446620.jpg', 'highlights': ['The chapter covers building a SVM classifier for cupcake vs muffin recipes A support vector machine code was used to classify recipes as either a cupcake or a muffin, with a specific example of predicting 40 parts flour and 20 parts sugar.', 'The chapter introduces K-means clustering as a method of organizing data into groups based on feature similarities, using the example of categorizing books into clusters.', 'Preview of upcoming tutorial on k-means clustering and logistic regression in Python The tutorial will cover k-means clustering and logistic regression, including a live demo on clustering cars based on brands and classifying tumors as malignant or benign.']}, {'end': 6605.103, 'segs': [{'end': 5522.033, 'src': 'embed', 'start': 5494.339, 'weight': 3, 'content': [{'end': 5500.844, 'text': "So, you know, be aware whenever we're formatting this data, things are going to pop up and sometimes you go backwards to fix it.", 'start': 5494.339, 'duration': 6.505}, {'end': 5502.205, 'text': "And that's fine.", 'start': 5501.525, 'duration': 0.68}, {'end': 5505.698, 'text': "That's just part of exploring the data and understanding what you have.", 'start': 5502.305, 'duration': 3.393}, {'end': 5511.523, 'text': 'I should have done this earlier, but let me go ahead and increase the size of my window one notch.', 'start': 5507.199, 'duration': 4.324}, {'end': 5515.107, 'text': 'There we go.', 'start': 5514.606, 'duration': 0.501}, {'end': 5516.288, 'text': 'Easier to see.', 'start': 5515.687, 'duration': 0.601}, {'end': 5522.033, 'text': "So we'll do for i in working with x dot columns.", 'start': 5517.829, 'duration': 4.204}], 'summary': 'Data formatting may require adjustments, such as increasing window size for better visibility.', 'duration': 27.694, 'max_score': 5494.339, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU5494339.jpg'}, {'end': 6262.469, 'src': 'embed', 'start': 6233.516, 'weight': 0, 'content': [{'end': 6235.236, 'text': "Again, we're going to do a scatter plot.", 'start': 6233.516, 'duration': 1.72}, {'end': 6238.258, 'text': 'And on the centroids.', 'start': 6236.857, 'duration': 1.401}, {'end': 6244.7, 'text': 'you can just pull that from our k-means, the model we created dot cluster centers,', 'start': 6238.258, 'duration': 6.442}, {'end': 6252.583, 'text': "and we're going to just do all of them in the first number and all of them in the second number, which is 0, 1,", 'start': 6244.7, 'duration': 7.883}, {'end': 6256.448, 'text': 'because you always start with 0 and 1..', 'start': 6252.583, 'duration': 3.865}, {'end': 6259.509, 'text': 'And then they were playing with the size and everything to make it look good.', 'start': 6256.448, 'duration': 3.061}, {'end': 6260.709, 'text': "We'll do a size of 300.", 'start': 6259.869, 'duration': 0.84}, {'end': 6262.469, 'text': "We're going to make the color yellow.", 'start': 6260.709, 'duration': 1.76}], 'summary': 'Creating scatter plot with centroids, size 300, and color yellow.', 'duration': 28.953, 'max_score': 6233.516, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU6233516.jpg'}, {'end': 6381.131, 'src': 'embed', 'start': 6354.233, 'weight': 2, 'content': [{'end': 6358.135, 'text': "Maybe you're doing loans and you want to go well, why is this group not defaulting on their loans?", 'start': 6354.233, 'duration': 3.902}, {'end': 6360.217, 'text': 'And why is the last group defaulting on their loans?', 'start': 6358.215, 'duration': 2.002}, {'end': 6364.239, 'text': 'And why is the middle group 50% defaulting on their bank loans?', 'start': 6360.597, 'duration': 3.642}, {'end': 6368.762, 'text': 'And you start finding ways to manipulate the data and pull out the answers you want.', 'start': 6364.699, 'duration': 4.063}, {'end': 6377.688, 'text': "So now that you've seen how to use k-mean for clustering, let's move on to the next topic.", 'start': 6371.584, 'duration': 6.104}, {'end': 6381.131, 'text': "Now let's look into logistic regression.", 'start': 6378.529, 'duration': 2.602}], 'summary': 'Analyzing loan default rates and using k-means for clustering before moving to logistic regression.', 'duration': 26.898, 'max_score': 6354.233, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU6354233.jpg'}, {'end': 6473.429, 'src': 'embed', 'start': 6447.258, 'weight': 1, 'content': [{'end': 6454.782, 'text': "In such cases where we need the output as categorical value, we will use logistic regression, and for that we're going to use the sigmoid function.", 'start': 6447.258, 'duration': 7.524}, {'end': 6459.785, 'text': 'So you can see here we have our marks, 0 to 100, number of hours studied.', 'start': 6455.123, 'duration': 4.662}, {'end': 6462.807, 'text': "That's going to be what they're comparing it to in this example.", 'start': 6460.005, 'duration': 2.802}, {'end': 6466.468, 'text': 'And we usually form a line that says y equals mx plus c.', 'start': 6463.187, 'duration': 3.281}, {'end': 6473.429, 'text': 'And when we use the sigmoid function, we have p equals 1 over 1 plus e to the minus y.', 'start': 6466.468, 'duration': 6.961}], 'summary': 'Logistic regression is used for categorical values, utilizing the sigmoid function and a formula of p equals 1 over 1 plus e to the minus y.', 'duration': 26.171, 'max_score': 6447.258, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU6447258.jpg'}], 'start': 5129.935, 'title': 'Car data analysis with python and pandas', 'summary': 'Covers importing car data from the 70s and 80s using python, data processing and analysis including filling missing data and converting string data to float values, pandas data conversion and null value handling, and the implementation of k-means clustering to identify optimal clusters for car make data with a clear elbow joint at 2, 3, and 4 clusters, ultimately settling on 3 clusters for analysis, along with logistic regression for outcome categorization based on hours studied.', 'chapters': [{'end': 5189.597, 'start': 5129.935, 'title': 'Importing and analyzing car data', 'summary': 'Covers importing car data from the 70s and 80s using python, specifically discussing the process of reading a csv file into a dataset using pandas.', 'duration': 59.662, 'highlights': ['The chapter emphasizes importing car data from the 70s and 80s, showcasing the number of cars produced by brands like Toyota, Honda, and Nissan.', 'The speaker demonstrates the process of reading a car CSV file into a dataset using pandas in Python, highlighting the importance of storing the Python code and the data file in the same folder.']}, {'end': 5430.762, 'start': 5189.697, 'title': 'Data processing and analysis', 'summary': 'Covers the process of filling missing data with the average value, removing certain columns, and converting string data to float values in a dataset, using pandas commands and python, while explaining the importance of each step.', 'duration': 241.065, 'highlights': ["Filling Missing Data Explaining the process of filling missing data with the average value for a column using the 'fill na' pandas command, ensuring that missing data is replaced with the average value of the existing data, and validating the correctness of the process.", "Removing Certain Columns Demonstrating the removal of specific columns from a dataset, such as removing the last column containing model information, utilizing pandas' features like 'iLocation' and 'dataset.columns', and validating the resulting new data frame.", 'Converting String Data to Float Addressing the issue of string data being recorded as strings instead of float values in the dataset, explaining the error that arises due to this discrepancy, and demonstrating the process of converting string data to float values for further analysis.']}, {'end': 5877.681, 'start': 5431.202, 'title': 'Pandas data conversion and null value handling', 'summary': 'Discusses using pandas to convert objects to numeric values, handling null values, and using the elbo method for k-means clustering, emphasizing the importance of data cleaning and the iterative process of data exploration and analysis.', 'duration': 446.479, 'highlights': ["Using pandas to convert objects to numeric values The process involves using the 'convert_numeric' function with the parameter 'numeric=True' to convert all objects into numeric values, facilitating data manipulation.", "Handling null values in the data The chapter emphasizes the importance of checking and eliminating null values from the data, highlighting the iterative process of data exploration and the use of pandas functions such as 'fillna' and 'isnull' to handle missing data.", 'Utilizing the ELBO method for k-means clustering The chapter details the process of using the ELBO method to determine the optimal number of clusters for k-means clustering, highlighting the iterative nature of model training and the considerations for working with large datasets.']}, {'end': 6605.103, 'start': 5878.261, 'title': 'K-means clustering & logistic regression', 'summary': 'Covers the implementation of k-means clustering to identify optimal clusters for car make data, with a clear elbow joint at 2, 3, and 4 clusters, ultimately settling on 3 clusters for analysis. additionally, it delves into logistic regression, explaining the concept of categorizing outcomes using the sigmoid function and its application in predicting whether a student will pass or fail based on the number of hours studied.', 'duration': 726.842, 'highlights': ['The chapter explains the implementation of k-means clustering to identify optimal clusters for car make data, with a clear elbow joint at 2, 3, and 4 clusters, ultimately settling on 3 clusters for analysis. The elbow method is used to identify the optimal number of clusters, with a clear elbow joint observed at 2, 3, and 4 clusters, indicating the number of clusters where the within-cluster sum of squares (WCSS) starts to decrease at a slower rate. The decision is made to settle on 3 clusters for further analysis.', 'The chapter delves into logistic regression, explaining the concept of categorizing outcomes using the sigmoid function and its application in predicting whether a student will pass or fail based on the number of hours studied. Logistic regression is introduced as a method for categorizing outcomes, utilizing the sigmoid function to predict whether a student will pass or fail based on the number of hours studied. The sigmoid function maps any real value to the range (0, 1), effectively categorizing the outcome.']}], 'duration': 1475.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU5129935.jpg', 'highlights': ['The chapter emphasizes importing car data from the 70s and 80s, showcasing the number of cars produced by brands like Toyota, Honda, and Nissan.', 'Demonstrating the process of reading a car CSV file into a dataset using pandas in Python, highlighting the importance of storing the Python code and the data file in the same folder.', 'The chapter explains the implementation of k-means clustering to identify optimal clusters for car make data, with a clear elbow joint at 2, 3, and 4 clusters, ultimately settling on 3 clusters for analysis.', 'Logistic regression is introduced as a method for categorizing outcomes, utilizing the sigmoid function to predict whether a student will pass or fail based on the number of hours studied.']}, {'end': 7257.501, 'segs': [{'end': 6853.263, 'src': 'embed', 'start': 6823.351, 'weight': 1, 'content': [{'end': 6828.338, 'text': "It generates a really nice graph on here, and there's all kinds of cool things on this graph to look at.", 'start': 6823.351, 'duration': 4.987}, {'end': 6831.583, 'text': 'I mean, we have the texture mean and the radius mean, obviously the axes.', 'start': 6828.378, 'duration': 3.205}, {'end': 6832.985, 'text': 'You can also see..', 'start': 6832.044, 'duration': 0.941}, {'end': 6838.932, 'text': 'And one of the cool things on here is you can also see the histogram.', 'start': 6835.729, 'duration': 3.203}, {'end': 6840.753, 'text': 'They show that for the radius mean.', 'start': 6839.072, 'duration': 1.681}, {'end': 6845.076, 'text': 'Where does the most common radius mean come up and where the most common texture is.', 'start': 6840.933, 'duration': 4.143}, {'end': 6853.263, 'text': "So we're looking at the, on each growth it's average texture and on each radius it's average radius on there.", 'start': 6845.477, 'duration': 7.786}], 'summary': 'Analysis of graph displays texture mean and radius mean, with focus on histogram for common values.', 'duration': 29.912, 'max_score': 6823.351, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU6823351.jpg'}, {'end': 7257.501, 'src': 'embed', 'start': 7209.368, 'weight': 0, 'content': [{'end': 7214.491, 'text': "And if you remember correctly, we're just going to be looking at the diagnosis.", 'start': 7209.368, 'duration': 5.123}, {'end': 7222.512, 'text': "That's all we care about is what is it diagnosed? Is it benign or malignant? And since it's a single column, we can just do diagnosis.", 'start': 7214.591, 'duration': 7.921}, {'end': 7224.092, 'text': 'Oh, I forgot to put the brackets.', 'start': 7222.832, 'duration': 1.26}, {'end': 7224.932, 'text': 'There we go.', 'start': 7224.572, 'duration': 0.36}, {'end': 7227.733, 'text': "Okay So it's just diagnosis on there.", 'start': 7225.172, 'duration': 2.561}, {'end': 7232.534, 'text': 'And we can also real quickly do like x.head if you want to see what that looks like.', 'start': 7227.973, 'duration': 4.561}, {'end': 7234.414, 'text': 'And y.head.', 'start': 7233.234, 'duration': 1.18}, {'end': 7236.335, 'text': 'And run this.', 'start': 7235.715, 'duration': 0.62}, {'end': 7239.836, 'text': "And you'll see it only does the last one.", 'start': 7237.535, 'duration': 2.301}, {'end': 7241.316, 'text': "I forgot about that if you don't do print.", 'start': 7239.856, 'duration': 1.46}, {'end': 7246.298, 'text': 'You can see that the y.head is just mmm because the first ones are all malignant.', 'start': 7241.877, 'duration': 4.421}, {'end': 7255.02, 'text': 'And if I run this, the x.head is just the first five values of radius worst, texture worst, parameter worst, area worst, and so on.', 'start': 7246.838, 'duration': 8.182}, {'end': 7257.501, 'text': "I'll go ahead and take that out.", 'start': 7256.4, 'duration': 1.101}], 'summary': 'Analyzing diagnosis data for benign and malignant cases using python.', 'duration': 48.133, 'max_score': 7209.368, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU7209368.jpg'}], 'start': 6605.663, 'title': 'Data visualization in python', 'summary': 'Covers the import of numpy, pandas, seaborn, and matplotlib libraries in python for visualizing a dataset with 36 different measurements for predicting benign and malignant tumors. it also involves data exploration using pandas and seaborn to analyze, visualize, and handle null values in the dataset.', 'chapters': [{'end': 6754.86, 'start': 6605.663, 'title': 'Data import and visualization in python', 'summary': 'Covers the import of numpy, pandas, seaborn, and matplotlib libraries in python, and the exploration of a dataset containing medical diagnostic data with 36 different measurements for predicting benign and malignant tumors.', 'duration': 149.197, 'highlights': ['The chapter covers the import of numpy, pandas, seaborn, and matplotlib libraries in Python, emphasizing the use of Anaconda for Jupyter Notebook and the importance of data visualization tools like seaborn and matplotlibrary.', 'The dataset being explored contains medical diagnostic data with 36 different measurements, including features such as radius mean, texture average, perimeter mean, and area mean, aimed at predicting benign and malignant tumors.', 'The speaker highlights the challenge of interpreting medical measurements for non-experts and the complexity of the features, such as smoothness and symmetry, in the dataset.']}, {'end': 7257.501, 'start': 6755.78, 'title': 'Data exploration with pandas and seaborn', 'summary': 'Explores data using pandas to analyze the first two columns, then utilizes seaborn to create a joint plot and a heat map of the data, and finally checks for null values in the dataset.', 'duration': 501.721, 'highlights': ['Explores data using Pandas to analyze the first two columns, the radius mean and texture mean, with data.head, revealing important information about the dataset structure. Use of Pandas to analyze specific columns provides insight into the dataset structure and content.', "Utilizes Seaborn to create a joint plot of the first two columns, revealing the relationships between the radius mean and texture mean, offering a visual representation of the data distribution. Seaborn's joint plot offers a visual representation of the relationship between columns, aiding in data exploration and analysis.", "Creates a heat map using Seaborn's SNS.heatmap to visualize the correlations between different features in the dataset, revealing both strong and weak correlations. Seaborn's heat map provides a visual representation of the correlations between different features, aiding in identifying strong and weak correlations.", 'Checks for null values in the dataset using the data.isNull method, ensuring data integrity and preventing potential errors in subsequent analyses. Checking for null values is essential for ensuring data integrity and preventing errors in subsequent analyses.']}], 'duration': 651.838, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU6605663.jpg', 'highlights': ['The chapter covers the import of numpy, pandas, seaborn, and matplotlib libraries in Python, emphasizing the use of Anaconda for Jupyter Notebook and the importance of data visualization tools like seaborn and matplotlibrary.', 'The dataset being explored contains medical diagnostic data with 36 different measurements, including features such as radius mean, texture average, perimeter mean, and area mean, aimed at predicting benign and malignant tumors.', "Creates a heat map using Seaborn's SNS.heatmap to visualize the correlations between different features in the dataset, revealing both strong and weak correlations.", 'Utilizes Seaborn to create a joint plot of the first two columns, revealing the relationships between the radius mean and texture mean, offering a visual representation of the data distribution.', 'Explores data using Pandas to analyze the first two columns, the radius mean and texture mean, with data.head, revealing important information about the dataset structure.']}, {'end': 7981.376, 'segs': [{'end': 7717.222, 'src': 'embed', 'start': 7681.202, 'weight': 4, 'content': [{'end': 7682.422, 'text': "you're probably going to bet the money.", 'start': 7681.202, 'duration': 1.22}, {'end': 7684.924, 'text': "Because at that odds, it's pretty good that you'll make some money.", 'start': 7682.522, 'duration': 2.402}, {'end': 7687.866, 'text': 'And in the long run, if you do that enough, you definitely will make money.', 'start': 7684.944, 'duration': 2.922}, {'end': 7693.872, 'text': "And also with this domain, I've actually seen them use this to identify different forms of cancer.", 'start': 7688.406, 'duration': 5.466}, {'end': 7699.178, 'text': "That's one of the things that they're starting to use these models for, because then it helps the doctor know what to investigate.", 'start': 7694.073, 'duration': 5.105}, {'end': 7701.401, 'text': 'So that wraps up this section.', 'start': 7699.779, 'duration': 1.622}, {'end': 7708.376, 'text': "Finally, we're going to go in there and let's discuss the answer to the quiz asked in Machine Learning Tutorial Part 1.", 'start': 7702.042, 'duration': 6.334}, {'end': 7717.222, 'text': "Can you tell what's happening in the following cases? Grouping documents into different categories based on the topic and content of each document.", 'start': 7708.376, 'duration': 8.846}], 'summary': 'Using odds to make money and identifying cancer with machine learning models.', 'duration': 36.02, 'max_score': 7681.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU7681202.jpg'}, {'end': 7917.226, 'src': 'embed', 'start': 7892.996, 'weight': 0, 'content': [{'end': 7898.78, 'text': "And hopefully you have a nice share, your shareholders gathered at the meeting, and you're able to explain it in something they can understand.", 'start': 7892.996, 'duration': 5.784}, {'end': 7901.752, 'text': 'So we talk about data, types of data.', 'start': 7899.69, 'duration': 2.062}, {'end': 7906.376, 'text': 'We have in our types of data, we have a qualitative, categorical.', 'start': 7902.372, 'duration': 4.004}, {'end': 7909.138, 'text': 'You think nominal, ordinal.', 'start': 7907.237, 'duration': 1.901}, {'end': 7914.083, 'text': 'Then you have your quantitative or numerical, which is discrete or continuous.', 'start': 7909.719, 'duration': 4.364}, {'end': 7917.226, 'text': "And let's look a little closer at those data types.", 'start': 7915.104, 'duration': 2.122}], 'summary': 'Explaining data types to shareholders in a clear manner is important. data can be qualitative, categorical, nominal, ordinal, quantitative, discrete, or continuous.', 'duration': 24.23, 'max_score': 7892.996, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU7892996.jpg'}, {'end': 7989.808, 'src': 'embed', 'start': 7962.383, 'weight': 2, 'content': [{'end': 7970.648, 'text': 'You can see here the salary range if you have $10,000 to $20,000, number of employees earning that rate is $150,000.', 'start': 7962.383, 'duration': 8.265}, {'end': 7972.87, 'text': '$20,000 to $30,000, $100,000, and so forth.', 'start': 7970.648, 'duration': 2.222}, {'end': 7976.192, 'text': "Some of the terms you'll hear is bucket.", 'start': 7973.41, 'duration': 2.782}, {'end': 7981.376, 'text': 'This is where you have 10 different buckets and you want to separate it into something that makes sense into those 10 buckets.', 'start': 7976.252, 'duration': 5.124}, {'end': 7989.808, 'text': "And so when we start talking about ordinal, a lot of times when you get down to the brass bones, again, we're talking true-false.", 'start': 7982.216, 'duration': 7.592}], 'summary': "Salary ranges: $10,000-$20,000 has 150,000 employees, $20,000-$30,000 has 100,000 employees. terms include 'bucket', 'ordinal', and 'true-false'.", 'duration': 27.425, 'max_score': 7962.383, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU7962383.jpg'}], 'start': 7258.681, 'title': 'Logistic regression and model evaluation', 'summary': 'Covers the process of splitting data, creating a logistic regression model, and evaluating its performance with a precision of 92% in predicting tumor type, and also discusses machine learning models including clustering, classification, anomaly detection, and regression, along with an overview of data and its types.', 'chapters': [{'end': 7693.872, 'start': 7258.681, 'title': 'Logistic regression and model evaluation', 'summary': 'Covers the process of splitting data into training and test sets, creating a logistic regression model, and evaluating its performance with a precision of 92% in predicting the type of tumor.', 'duration': 435.191, 'highlights': ['The chapter covers the process of splitting data into training and test sets, creating a logistic regression model, and evaluating its performance with a precision of 92% in predicting the type of tumor. This is the main focus of the chapter, summarizing the key steps and the achieved precision in model evaluation.', 'The test size is 30%, putting 30% of the data into the test variables and 70% into the training variables. Quantifiable data on the division of data for training and testing.', "The model is able to predict the type of tumor with 91% accuracy. Specific quantifiable result of the model's performance in predicting the tumor type."]}, {'end': 7981.376, 'start': 7694.073, 'title': 'Machine learning concepts and applications', 'summary': 'Covers examples of machine learning models such as clustering, classification, anomaly detection, and regression, along with an overview of data and its types.', 'duration': 287.303, 'highlights': ['The chapter covers examples of machine learning models such as clustering, classification, anomaly detection, and regression It provides examples of different machine learning models used in various scenarios, showcasing the versatility and applications of machine learning.', 'Overview of data and its types, including qualitative, categorical, and quantitative data Discusses the types of data, such as qualitative, categorical, and quantitative, providing real-world examples and explanations for each type.', 'Explanation of nominal and ordinal data types with real-world examples Provides clear definitions and examples of nominal and ordinal data types, aiding in better understanding of these concepts.']}], 'duration': 722.695, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU7258681.jpg', 'highlights': ['The chapter covers the process of splitting data, creating a logistic regression model, and evaluating its performance with a precision of 92% in predicting the type of tumor.', 'The model is able to predict the type of tumor with 91% accuracy.', 'The test size is 30%, putting 30% of the data into the test variables and 70% into the training variables.', 'The chapter covers examples of machine learning models such as clustering, classification, anomaly detection, and regression.', 'Overview of data and its types, including qualitative, categorical, and quantitative data.', 'Explanation of nominal and ordinal data types with real-world examples.']}, {'end': 9440.178, 'segs': [{'end': 8082.525, 'src': 'embed', 'start': 8053.309, 'weight': 0, 'content': [{'end': 8058.831, 'text': "Usually we start thinking about float values where they can get phenomenally small in what they're worth.", 'start': 8053.309, 'duration': 5.522}, {'end': 8063.694, 'text': "And there's a whole series of values that falls right between discrete and continuous.", 'start': 8059.552, 'duration': 4.142}, {'end': 8066.435, 'text': 'You can think of the stock market.', 'start': 8064.874, 'duration': 1.561}, {'end': 8067.796, 'text': 'You have dollar amounts.', 'start': 8066.595, 'duration': 1.201}, {'end': 8074.841, 'text': "It's still discrete, but it starts to get complicated enough when you jump in the stock market from $525.33 to $580.67.", 'start': 8067.956, 'duration': 6.885}, {'end': 8076.482, 'text': "There's a lot of point values in there.", 'start': 8074.841, 'duration': 1.641}, {'end': 8082.525, 'text': "It'd still be called discrete, but you start looking at it as almost continuous because it does have such a variance in it.", 'start': 8076.502, 'duration': 6.023}], 'summary': 'Float values bridge discrete and continuous, exemplified by stock market fluctuations.', 'duration': 29.216, 'max_score': 8053.309, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU8053309.jpg'}, {'end': 8585.781, 'src': 'embed', 'start': 8559.895, 'weight': 4, 'content': [{'end': 8564.997, 'text': "I've had that come up a number of times where I am altering data and I get confused as to what I'm doing with it.", 'start': 8559.895, 'duration': 5.102}, {'end': 8577.218, 'text': "Transpose. flipping the matrix over its diagonal comes up all the time, where you still have 12, but instead of it being 12, 8,, it's now 12, 14, 8, 21..", 'start': 8566.074, 'duration': 11.144}, {'end': 8579.479, 'text': "You're just flipping the columns and the rows.", 'start': 8577.218, 'duration': 2.261}, {'end': 8585.781, 'text': 'And then, of course, you can do an inverse, changing the signs of the values across its main diagonal.', 'start': 8580.379, 'duration': 5.402}], 'summary': 'Data alteration confusion occurs frequently. transposing matrix flips rows and columns. inverting changes values across diagonal.', 'duration': 25.886, 'max_score': 8559.895, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU8559895.jpg'}, {'end': 8778.578, 'src': 'embed', 'start': 8748.869, 'weight': 2, 'content': [{'end': 8750.089, 'text': "That's whatever we started with.", 'start': 8748.869, 'duration': 1.22}, {'end': 8751.149, 'text': "That's your original picture.", 'start': 8750.109, 'duration': 1.04}, {'end': 8758.072, 'text': 'And 3 is skewing it one direction and maybe B is being skewed another direction.', 'start': 8751.79, 'duration': 6.282}, {'end': 8762.773, 'text': "And so you have a nice tilted picture because you've altered it by the eigenvalues.", 'start': 8758.532, 'duration': 4.241}, {'end': 8768.395, 'text': "So let's go ahead and pull up a demo on linear algebra.", 'start': 8764.154, 'duration': 4.241}, {'end': 8773.837, 'text': "And to do this, I'm going to go through my trusted anaconda into my Jupyter notebook.", 'start': 8768.795, 'duration': 5.042}, {'end': 8778.578, 'text': "And we'll create a new notebook called Linear Algebra.", 'start': 8774.893, 'duration': 3.685}], 'summary': 'Discussion on linear algebra and eigenvalues in a jupyter notebook demo.', 'duration': 29.709, 'max_score': 8748.869, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU8748869.jpg'}, {'end': 9068.291, 'src': 'embed', 'start': 9037.489, 'weight': 3, 'content': [{'end': 9040.671, 'text': 'And, of course, the matrix, you can get very complicated on these.', 'start': 9037.489, 'duration': 3.182}, {'end': 9046.974, 'text': "Or in this case, we'll go ahead and do, let's create two complex matrices.", 'start': 9040.791, 'duration': 6.183}, {'end': 9054.655, 'text': 'This one is a matrix of, you know, 12, 10, 4, 6, 4, 31.', 'start': 9048.195, 'duration': 6.46}, {'end': 9056.779, 'text': "We'll just print out A so you can see what that looks like.", 'start': 9054.658, 'duration': 2.121}, {'end': 9058.42, 'text': "Here's print A.", 'start': 9056.819, 'duration': 1.601}, {'end': 9068.291, 'text': 'When we print A out, you can see that we have a 2 by 3 layer matrix for A.', 'start': 9059.881, 'duration': 8.41}], 'summary': 'Created two complex matrices, a with a 2x3 layer.', 'duration': 30.802, 'max_score': 9037.489, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU9037489.jpg'}], 'start': 7982.216, 'title': 'Linear algebra and data types', 'summary': 'Discusses quantitative data types, differentiating between discrete and continuous numerical data and explaining ordinal data. it also covers linear algebra fundamentals, including linear equations, matrices, vectors, and their applications in mathematical computations and neural networks. additionally, it introduces i-gene vectors, i-gene values, and demonstrates operations using numpy arrays in python, along with various matrix manipulations essential in data science and backend processes.', 'chapters': [{'end': 8100.676, 'start': 7982.216, 'title': 'Quantitative data types explained', 'summary': 'Explains the distinction between discrete and continuous numerical data, emphasizing their characteristics and examples, and also touches upon the concept of ordinal data and its application in categorizing data into buckets.', 'duration': 118.46, 'highlights': ['Discrete data consists of a final set of values, such as class strength and questions answered correctly, typically represented by integers within a small range, making it simple to count, e.g., 100 questions on a test.', 'Continuous data can take any numerical value within a range, such as water pressure and weight of a person, often involving float values and falling between discrete and continuous, like dollar amounts in the stock market.', 'Ordinal data is discussed in relation to categorizing data into buckets and counting the number of people in each bucket, exemplifying the application of true-false charts and membership in specific income ranges.']}, {'end': 8668.221, 'start': 8101.177, 'title': 'Linear algebra fundamentals', 'summary': 'Covers the basics of linear algebra, including the definition of linear equations, matrices, and vectors, and their applications in mathematical computations and neural networks. it emphasizes the importance of understanding linear equations, matrix operations such as addition, subtraction, and multiplication, as well as vectors and their applications in multi-dimensional spaces and distance calculations.', 'duration': 567.044, 'highlights': ["Linear equations and their representations in vector spaces and matrices. Linear algebra's domain concerning linear equations, their representations in vector spaces, and through matrices, emphasizing the foundational concept of linear equations and their representation.", 'Matrix operations including addition, subtraction, and multiplication. Exploration of fundamental matrix operations such as addition, subtraction, and multiplication, highlighting their significance in mathematical computations.', "Vectors and their applications in multi-dimensional spaces and distance calculations. Explanation of vectors' role in multi-dimensional spaces and their application in distance calculations using the Pythagorean theorem, emphasizing their importance in mathematical analysis.", "Definition and application of the Pythagorean theorem in distance calculations. Explanation of the Pythagorean theorem's application in distance calculations and its relevance in comparing point values, highlighting its significance in mathematical analysis and data comparison.", 'Inverse, transpose, and their applications in matrix algebra. Explanation of inverse and transpose operations in matrix algebra, emphasizing their applications in altering matrix values and operations.']}, {'end': 9090.824, 'start': 8668.982, 'title': 'Linear algebra basics', 'summary': 'Introduces i-gene vectors and i-gene values, and demonstrates operations such as addition, subtraction, scalar multiplication, and dot product using numpy arrays in python for linear algebra, presenting an example of matrix multiplication and a complex matrix.', 'duration': 421.842, 'highlights': ['The chapter introduces i-gene vectors and i-gene values, and demonstrates operations such as addition, subtraction, scalar multiplication, and dot product using numpy arrays in Python for linear algebra.', 'The example of matrix multiplication using numpy arrays results in a dot product of 370.', 'The chapter presents a 2 by 3 complex matrix and demonstrates how to print and visualize the matrix in Python.']}, {'end': 9440.178, 'start': 9090.844, 'title': 'Matrix operations and manipulations', 'summary': 'Covers various matrix operations, including addition, subtraction, scalar multiplication, matrix and vector multiplication, matrix to matrix multiplication, transpose, identity matrix, and matrix inverse, which are essential in data science and backend processes.', 'duration': 349.334, 'highlights': ['The chapter covers various matrix operations, including addition, subtraction, scalar multiplication, matrix and vector multiplication, matrix to matrix multiplication, transpose, identity matrix, and matrix inverse. The chapter delves into a comprehensive range of matrix operations, such as addition, subtraction, scalar multiplication, matrix and vector multiplication, matrix to matrix multiplication, transpose, identity matrix, and matrix inverse.', 'Matrix and vector multiplication is explained, demonstrating the process of multiplying a matrix by a vector and discussing its relevance in complex backend processes. The section details the process of matrix and vector multiplication and its significance in complex backend processes, where layers of data are multiplied, and it is commonly used in data science and linear regression models.', 'The concept of matrix inverse is introduced, with a detailed explanation of how to obtain the inverse of a square matrix and its significance in reshaping the identity matrix. The chapter explains the concept of obtaining the inverse of a square matrix and its importance in reshaping the identity matrix, essential in data science and backend processes.']}], 'duration': 1457.962, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU7982216.jpg', 'highlights': ['The chapter covers various matrix operations, including addition, subtraction, scalar multiplication, matrix and vector multiplication, matrix to matrix multiplication, transpose, identity matrix, and matrix inverse.', 'Linear equations and their representations in vector spaces and matrices.', 'The chapter introduces i-gene vectors and i-gene values, and demonstrates operations such as addition, subtraction, scalar multiplication, and dot product using numpy arrays in Python for linear algebra.', 'Continuous data can take any numerical value within a range, such as water pressure and weight of a person, often involving float values and falling between discrete and continuous, like dollar amounts in the stock market.', 'Matrix and vector multiplication is explained, demonstrating the process of multiplying a matrix by a vector and discussing its relevance in complex backend processes.']}, {'end': 10437.366, 'segs': [{'end': 9598.566, 'src': 'embed', 'start': 9558.379, 'weight': 2, 'content': [{'end': 9563.401, 'text': "I'm getting faster and faster because I'm continually accelerating, and if I hit the brakes, it'd go the other way.", 'start': 9558.379, 'duration': 5.022}, {'end': 9569.083, 'text': 'So the rate of change of speed with respect of time is nothing but acceleration.', 'start': 9564.481, 'duration': 4.602}, {'end': 9577.387, 'text': 'How fast are we accelerating? The acceleration is the area between the star point of x and the endpoint of delta x.', 'start': 9569.343, 'duration': 8.044}, {'end': 9585.85, 'text': 'So we can calculate a simple, if you had x and delta x, we could put a line there, and that slope of the line is our acceleration.', 'start': 9577.387, 'duration': 8.463}, {'end': 9593.383, 'text': "Now, that's pretty easy when you're doing linear algebra, but I don't want to know it just for that line and those two points.", 'start': 9586.9, 'duration': 6.483}, {'end': 9596.605, 'text': "I want to know it across the whole of what I'm working with.", 'start': 9593.564, 'duration': 3.041}, {'end': 9598.566, 'text': "That's where we get into calculus.", 'start': 9597.406, 'duration': 1.16}], 'summary': 'Acceleration is the rate of change of speed with respect to time, calculated as the area between the start and end points of displacement.', 'duration': 40.187, 'max_score': 9558.379, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU9558379.jpg'}, {'end': 9921.324, 'src': 'embed', 'start': 9889.871, 'weight': 5, 'content': [{'end': 9892.314, 'text': "So there's our multiple variables going in there.", 'start': 9889.871, 'duration': 2.443}, {'end': 9895.517, 'text': 'If one variable is changing, how does it affect the other variable?', 'start': 9892.834, 'duration': 2.683}, {'end': 9902.369, 'text': 'And then in gradient descent calculus is used to find the local and global maxima.', 'start': 9896.724, 'duration': 5.645}, {'end': 9904.47, 'text': 'And this is really big.', 'start': 9903.189, 'duration': 1.281}, {'end': 9910.275, 'text': "We're actually going to have a whole section here on gradient descent, because it is really.", 'start': 9905.111, 'duration': 5.164}, {'end': 9910.936, 'text': 'I mean.', 'start': 9910.275, 'duration': 0.661}, {'end': 9914.999, 'text': 'I talked about neural networks and how you can see how the different layers go in there,', 'start': 9910.936, 'duration': 4.063}, {'end': 9921.324, 'text': 'but gradient descent is one of the most key things for trying to guess the best answer to something.', 'start': 9914.999, 'duration': 6.325}], 'summary': 'Gradient descent and calculus used to find maxima in neural networks.', 'duration': 31.453, 'max_score': 9889.871, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU9889871.jpg'}, {'end': 10318.192, 'src': 'embed', 'start': 10288.704, 'weight': 1, 'content': [{'end': 10290.926, 'text': "You have the function that's going in.", 'start': 10288.704, 'duration': 2.222}, {'end': 10292.888, 'text': 'This function can be very complicated.', 'start': 10290.966, 'duration': 1.922}, {'end': 10295.63, 'text': 'So we used a very simple function up here.', 'start': 10293.828, 'duration': 1.802}, {'end': 10296.751, 'text': 'It could be..', 'start': 10295.65, 'duration': 1.101}, {'end': 10299.894, 'text': "There's all kinds of things that could be on there.", 'start': 10298.132, 'duration': 1.762}, {'end': 10303.357, 'text': "And there's a number of methods to solve this as far as how they shrink down.", 'start': 10300.074, 'duration': 3.283}, {'end': 10305.139, 'text': 'And your X naught.', 'start': 10304.118, 'duration': 1.021}, {'end': 10306.801, 'text': "There's your start value.", 'start': 10305.299, 'duration': 1.502}, {'end': 10309.123, 'text': 'So your function, your start value.', 'start': 10307.101, 'duration': 2.022}, {'end': 10313.807, 'text': "There's all kinds of things that come in here that you can look at which we're not going to.", 'start': 10309.143, 'duration': 4.664}, {'end': 10318.192, 'text': 'Optimization automatically creates, constraints, bounds.', 'start': 10314.748, 'duration': 3.444}], 'summary': 'Discussion on function complexity, methods for optimization, and constraints in solving equations.', 'duration': 29.488, 'max_score': 10288.704, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU10288704.jpg'}, {'end': 10420.053, 'src': 'embed', 'start': 10375.61, 'weight': 0, 'content': [{'end': 10382.797, 'text': 'And then you got to take those analysis and interpret it into something that people can use, kind of reduce it to understandable.', 'start': 10375.61, 'duration': 7.187}, {'end': 10385.739, 'text': 'And nowadays you have to be able to present it.', 'start': 10383.577, 'duration': 2.162}, {'end': 10388.922, 'text': "If you can't present it, then no one else is going to understand what the heck you did.", 'start': 10385.859, 'duration': 3.063}, {'end': 10392.55, 'text': 'So we look at the terminologies.', 'start': 10391.089, 'duration': 1.461}, {'end': 10397.535, 'text': "There is a lot of terminologies depending on what domain you're working in.", 'start': 10393.851, 'duration': 3.684}, {'end': 10399.056, 'text': 'So clearly.', 'start': 10398.235, 'duration': 0.821}, {'end': 10409.465, 'text': "if you're working in a domain that deals with viruses and T cells and how does you know?", 'start': 10399.056, 'duration': 10.409}, {'end': 10410.386, 'text': 'where does that come from?', 'start': 10409.465, 'duration': 0.921}, {'end': 10411.446, 'text': "you're studying the different people.", 'start': 10410.386, 'duration': 1.06}, {'end': 10413.288, 'text': 'then you can have a population.', 'start': 10411.446, 'duration': 1.842}, {'end': 10420.053, 'text': 'if you are working with mechanical gear, you know a little bit different.', 'start': 10413.288, 'duration': 6.765}], 'summary': 'Interpreting analysis is crucial; effective presentation is essential for understanding; terminologies vary by domain.', 'duration': 44.443, 'max_score': 10375.61, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU10375610.jpg'}], 'start': 9440.198, 'title': 'Mathematics in data science', 'summary': 'Covers the importance of understanding linear algorithms, calculus, and differential equations in data science, particularly in the context of large neural networks. it explains the concept of acceleration and integration as the rate of change of speed with respect to time using calculus, delving into the process of finding the area under the slope and its applications in multivariate calculus, neural networks, reverse propagation, and gradient descent. additionally, it explains the importance of precision in optimization algorithms, the significance of max iterations, and the need for presenting statistical data effectively, with an emphasis on the terminologies and domains of application.', 'chapters': [{'end': 9534.89, 'start': 9440.198, 'title': 'Understanding math in data science', 'summary': 'Covers the importance of understanding linear algorithm, calculus, and differential equations in data science, particularly in the context of large neural networks, and emphasizes the need for grasping these concepts for effective data science practices.', 'duration': 94.692, 'highlights': ['Understanding the importance of linear algorithm, calculus, and differential equations The transcript emphasizes the significance of understanding concepts such as linear algorithm, calculus, and differential equations in the context of data science.', 'Emphasizing the need for grasping these concepts for effective data science practices It is stressed that understanding these mathematical concepts is crucial for effective data science practices, particularly in the context of large neural networks and data analytics.', 'Exploring the relevance of calculus and differential equations in machine learning The transcript discusses the relevance of calculus and differential equations in machine learning, especially in the context of large neural networks, and highlights that most of the heavy lifting is already done in the back end.']}, {'end': 10109.961, 'start': 9535.531, 'title': 'Understanding calculus for acceleration and integration', 'summary': 'Explains the concept of acceleration and integration as the rate of change of speed with respect to time using calculus, delving into the process of finding the area under the slope and its applications in multivariate calculus, neural networks, reverse propagation, and gradient descent, which is crucial for minimizing error in machine learning models.', 'duration': 574.43, 'highlights': ['Calculus for Acceleration and Integration The chapter explains the concept of acceleration and integration using calculus to determine the rate of change of speed with respect to time.', 'Applications in Multivariate Calculus, Neural Networks, and Reverse Propagation The study of multivariate calculus, neural networks, and reverse propagation relies on the concept of finding the area under the slope using integration, involving complex equations and mathematical solutions.', 'Importance of Gradient Descent in Minimizing Error Gradient descent is highlighted as a crucial method for minimizing error in machine learning models, by finding the local and global maxima using calculus to determine the minimum value and optimize the output.']}, {'end': 10437.366, 'start': 10110.541, 'title': 'Optimization and precision in data analysis', 'summary': 'Explains the importance of precision in optimization algorithms, the significance of max iterations, and the need for presenting statistical data effectively, with an emphasis on the terminologies and domains of application.', 'duration': 326.825, 'highlights': ['The chapter explains the importance of precision in optimization algorithms. Precision is crucial in determining the accuracy of answers when dealing with different increments, such as 1,000 or 0.001.', 'Significance of max iterations is highlighted. Max iterations usually do not exceed 100 or 200, with rare cases going up to 400 or 500, depending on the problem being addressed.', 'The need for presenting statistical data effectively is emphasized, with an emphasis on the terminologies and domains of application. Statistics involves the collection, organization, analysis, interpretation, and presentation of data, with the requirement to make the findings understandable and accessible to others.']}], 'duration': 997.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU9440198.jpg', 'highlights': ['Understanding the importance of linear algorithm, calculus, and differential equations in data science.', 'Emphasizing the need for grasping these concepts for effective data science practices.', 'Exploring the relevance of calculus and differential equations in machine learning.', 'Applications in Multivariate Calculus, Neural Networks, and Reverse Propagation.', 'Importance of Gradient Descent in Minimizing Error.', 'The chapter explains the importance of precision in optimization algorithms.', 'Significance of max iterations is highlighted.', 'The need for presenting statistical data effectively is emphasized.']}, {'end': 11112.445, 'segs': [{'end': 10780.387, 'src': 'embed', 'start': 10752.612, 'weight': 1, 'content': [{'end': 10756.195, 'text': "So we are predicting how it's going to affect the greater population.", 'start': 10752.612, 'duration': 3.583}, {'end': 10758.636, 'text': 'So descriptive statistics.', 'start': 10757.355, 'duration': 1.281}, {'end': 10764.64, 'text': 'It is used to describe the basic features of data and form the basis of quantitative analysis of data.', 'start': 10758.776, 'duration': 5.864}, {'end': 10767.919, 'text': 'So we have a measure of central tendencies.', 'start': 10765.677, 'duration': 2.242}, {'end': 10769.78, 'text': 'We have your mean, median, and mode.', 'start': 10767.959, 'duration': 1.821}, {'end': 10777.465, 'text': 'And then we have a measure of spread, like your range, your interquartile range, your variance, and your standard deviation.', 'start': 10769.8, 'duration': 7.665}, {'end': 10780.387, 'text': "And we're going to look at all these a little deeper here in a second.", 'start': 10777.485, 'duration': 2.902}], 'summary': 'Predicting impact on population using descriptive statistics: mean, median, mode, range, interquartile range, variance, and standard deviation.', 'duration': 27.775, 'max_score': 10752.612, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU10752612.jpg'}, {'end': 11056.529, 'src': 'embed', 'start': 11030.736, 'weight': 0, 'content': [{'end': 11038.039, 'text': 'So the range, maximum marks, minimum marks, we have 90 to 45, and the spread of that is 45, 90 minus 45.', 'start': 11030.736, 'duration': 7.303}, {'end': 11042.421, 'text': 'And then we have the interquartile range using the same marks over there.', 'start': 11038.039, 'duration': 4.382}, {'end': 11044.802, 'text': 'You can see here where the median is.', 'start': 11042.521, 'duration': 2.281}, {'end': 11051.445, 'text': "and then there's the first quarter, the second quarter and the third quarter, based on splitting it apart by those values.", 'start': 11044.802, 'duration': 6.643}, {'end': 11056.529, 'text': 'And to understand the variance and standard deviation, we first need to find out the mean.', 'start': 11052.305, 'duration': 4.224}], 'summary': 'The range of marks is 45, with a spread of 45; further analysis involves interquartile range and understanding variance and standard deviation, which requires finding the mean.', 'duration': 25.793, 'max_score': 11030.736, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU11030736.jpg'}], 'start': 10437.426, 'title': 'Sampling and statistics fundamentals', 'summary': 'Covers the importance of sampling in maintaining sewer fans, including probabilistic and non-probabilistic methods, and the significance of descriptive and inferential statistics in making conclusions from small populations to large populations. it also discusses predictive data analysis overview, including the use of descriptive statistics to analyze data, measures of central tendencies, and measures of spread with examples and applications in different contexts like student marks and income distribution.', 'chapters': [{'end': 10752.072, 'start': 10437.426, 'title': 'Sampling and statistics fundamentals', 'summary': 'Discusses the importance of sampling in maintaining sewer fans, covering probabilistic and non-probabilistic sampling methods and the significance of descriptive and inferential statistics in making conclusions from small populations to large populations.', 'duration': 314.646, 'highlights': ['The importance of sampling in maintaining sewer fans Replacing sensors before they break to save costs, selecting samples from the fan population to make conclusions', 'Probabilistic and non-probabilistic sampling methods Distinguishing between probabilistic methods like random, systematic, and stratified sampling from non-probabilistic methods based on subjective judgment', 'Significance of descriptive and inferential statistics Utilizing descriptive statistics to describe data and inferential statistics to make conclusions from small populations to large populations, with a practical example of drug efficacy']}, {'end': 11112.445, 'start': 10752.612, 'title': 'Predictive data analysis overview', 'summary': 'Discusses the use of descriptive statistics to analyze data, including measures of central tendencies (mean, median, mode) and measures of spread (range, interquartile range, variance, and standard deviation), with examples and applications in different contexts like student marks and income distribution.', 'duration': 359.833, 'highlights': ['Descriptive statistics are used to describe the basic features of data and form the basis of quantitative analysis. Descriptive statistics form the basis of quantitative analysis and are used to describe the basic features of data.', 'Measures of central tendencies, including mean, median, and mode, provide insights into the average, middle point, and most frequent value in a dataset. Measures of central tendencies such as mean, median, and mode provide insights into the average, middle point, and most frequent value in a dataset.', 'Measures of spread, such as range, interquartile range, variance, and standard deviation, help understand the distribution and variability of data. Measures of spread like range, interquartile range, variance, and standard deviation help understand the distribution and variability of data.', 'Examples of applying measures of central tendencies and spread in contexts like student marks and income distribution illustrate their practical use and implications. Examples of applying measures of central tendencies and spread in contexts like student marks and income distribution illustrate their practical use and implications.']}], 'duration': 675.019, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU10437426.jpg', 'highlights': ['Replacing sensors before they break to save costs, selecting samples from the fan population to make conclusions', 'Utilizing descriptive statistics to describe data and inferential statistics to make conclusions from small populations to large populations, with a practical example of drug efficacy', 'Distinguishing between probabilistic methods like random, systematic, and stratified sampling from non-probabilistic methods based on subjective judgment', 'Measures of spread like range, interquartile range, variance, and standard deviation help understand the distribution and variability of data', 'Measures of central tendencies such as mean, median, and mode provide insights into the average, middle point, and most frequent value in a dataset', 'Examples of applying measures of central tendencies and spread in contexts like student marks and income distribution illustrate their practical use and implications', 'Descriptive statistics form the basis of quantitative analysis and are used to describe the basic features of data']}, {'end': 12040.866, 'segs': [{'end': 11522.795, 'src': 'embed', 'start': 11496.432, 'weight': 1, 'content': [{'end': 11506.822, 'text': "plot AXV line, salary, the mean value, so we're going to take the mean value, color violet, line style dash, this is just all making it pretty.", 'start': 11496.432, 'duration': 10.39}, {'end': 11511.587, 'text': 'What color, dash line, line width of two, that kind of thing.', 'start': 11507.823, 'duration': 3.764}, {'end': 11513.108, 'text': 'And the median.', 'start': 11512.307, 'duration': 0.801}, {'end': 11515.711, 'text': "And let's go ahead and run this just so you can see what we're talking about.", 'start': 11513.349, 'duration': 2.362}, {'end': 11520.515, 'text': 'And so up here we are taking on our plot.', 'start': 11517.754, 'duration': 2.761}, {'end': 11522.795, 'text': "So here's the data.", 'start': 11521.895, 'duration': 0.9}], 'summary': 'Analyzing plot data to determine mean and median values.', 'duration': 26.363, 'max_score': 11496.432, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU11496432.jpg'}, {'end': 11629.063, 'src': 'embed', 'start': 11590.56, 'weight': 0, 'content': [{'end': 11592.54, 'text': 'We can definitely do a histogram and stuff like that.', 'start': 11590.56, 'duration': 1.98}, {'end': 11595.401, 'text': "But, you know, a picture's worth a thousand words.", 'start': 11593.741, 'duration': 1.66}, {'end': 11606.87, 'text': 'what you really want to make sure you take away is that we can do a basic describe which pulls all this information out and we can print any of the individual information from the describe,', 'start': 11596.061, 'duration': 10.809}, {'end': 11617.178, 'text': 'because this is a dictionary, and so if we want to go ahead and look up the mean value, we can also do describe mean.', 'start': 11606.87, 'duration': 10.308}, {'end': 11626.462, 'text': "so if you're doing a lot of statistics, being able to print on there, so it's only going to print the last one, which happens to be the mean.", 'start': 11617.178, 'duration': 9.284}, {'end': 11629.063, 'text': 'You can very easily reference any one of these.', 'start': 11627.322, 'duration': 1.741}], 'summary': 'Using basic describe to extract statistical information from data dictionary.', 'duration': 38.503, 'max_score': 11590.56, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU11590560.jpg'}, {'end': 11711.785, 'src': 'embed', 'start': 11682.163, 'weight': 3, 'content': [{'end': 11687.985, 'text': "So that's real basics of what we're talking about is you're going to infer that the next person is going to follow these statistics.", 'start': 11682.163, 'duration': 5.822}, {'end': 11692.654, 'text': "So let's look at point estimation.", 'start': 11690.713, 'duration': 1.941}, {'end': 11701.659, 'text': "It is a process of finding an approximate value for a population's parameter, like mean or average from random samples of the population.", 'start': 11693.375, 'duration': 8.284}, {'end': 11705.101, 'text': "Let's take an example of testing vaccines for COVID-19.", 'start': 11702.279, 'duration': 2.822}, {'end': 11711.785, 'text': "Vaccines and flu bugs, all of that, it's a pretty big thing of how do you test these out and make sure they're going to work on the populace.", 'start': 11705.761, 'duration': 6.024}], 'summary': 'Infer future actions from statistics; point estimation finds approximate population parameters, e.g., testing vaccines for covid-19.', 'duration': 29.622, 'max_score': 11682.163, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU11682163.jpg'}, {'end': 11823.795, 'src': 'embed', 'start': 11779.483, 'weight': 4, 'content': [{'end': 11782.784, 'text': 'where theorem is a scientific statement.', 'start': 11779.483, 'duration': 3.301}, {'end': 11786.905, 'text': 'that is something that has been proven, although it is always up for debate.', 'start': 11782.784, 'duration': 4.121}, {'end': 11789.586, 'text': 'Because in science, we always want to make sure things are up to debate.', 'start': 11786.925, 'duration': 2.661}, {'end': 11794.227, 'text': 'So hypothesis is the same as a philosophical class calling a theory.', 'start': 11789.966, 'duration': 4.261}, {'end': 11797.148, 'text': 'where theory in science is not the same.', 'start': 11794.887, 'duration': 2.261}, {'end': 11799.568, 'text': 'Theory in science says this has been well proven.', 'start': 11797.208, 'duration': 2.36}, {'end': 11801.189, 'text': 'Gravity is a theory.', 'start': 11800.048, 'duration': 1.141}, {'end': 11804.95, 'text': 'So if you want to debate the theory of gravity, try jumping up and down.', 'start': 11801.829, 'duration': 3.121}, {'end': 11812.272, 'text': 'If you want to have a theory about why the economy is collapsing in your area, that is a philosophical debate.', 'start': 11805.35, 'duration': 6.922}, {'end': 11813.892, 'text': 'Very important.', 'start': 11813.092, 'duration': 0.8}, {'end': 11817.113, 'text': "I've heard people mix those up and it is a pet peeve of mine.", 'start': 11813.992, 'duration': 3.121}, {'end': 11823.795, 'text': 'When we talk about hypotheses testing, the steps involved in hypotheses testing is first we formulate a hypothesis.', 'start': 11817.673, 'duration': 6.122}], 'summary': 'In science, theories are proven statements, hypotheses are for debate. gravity is a well-proven theory.', 'duration': 44.312, 'max_score': 11779.483, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU11779483.jpg'}, {'end': 11877.993, 'src': 'embed', 'start': 11844.769, 'weight': 6, 'content': [{'end': 11848.051, 'text': 'We have four students who are given a task to clean a room every day.', 'start': 11844.769, 'duration': 3.282}, {'end': 11849.752, 'text': 'Sounds like working with my kids.', 'start': 11848.672, 'duration': 1.08}, {'end': 11853.454, 'text': 'They decided to distribute the job of cleaning the room among themselves.', 'start': 11850.332, 'duration': 3.122}, {'end': 11860.618, 'text': 'They did so by making four chits, which has their names on it, and the name that gets picked up has to do the cleaning for that day.', 'start': 11853.914, 'duration': 6.704}, {'end': 11864.84, 'text': "Rob took the opportunity to make chits and wrote everyone's name on it.", 'start': 11861.278, 'duration': 3.562}, {'end': 11869.723, 'text': "So here's our four people, Nick, Rob, Emilia, and Summer.", 'start': 11865.26, 'duration': 4.463}, {'end': 11877.993, 'text': 'Now Rick, Emilia and Summer are asking us to decide whether Rob has done some mischief in preparing the chits.', 'start': 11871.567, 'duration': 6.426}], 'summary': 'Four students rotate room cleaning task using chits. rob may have tampered with chits.', 'duration': 33.224, 'max_score': 11844.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU11844769.jpg'}, {'end': 12029.103, 'src': 'embed', 'start': 12002.669, 'weight': 5, 'content': [{'end': 12006.972, 'text': 'The greater the magnitude of t, the greater the evidence against the null hypothesis.', 'start': 12002.669, 'duration': 4.303}, {'end': 12012.116, 'text': "And you can look at the t-value as being specific to the test you're doing,", 'start': 12007.432, 'duration': 4.684}, {'end': 12019.781, 'text': "where the p-value is derived from your t-value and you're looking for what they call the 5% or the .05..", 'start': 12012.116, 'duration': 7.665}, {'end': 12021.541, 'text': 'showing that it has a high correlation.', 'start': 12019.781, 'duration': 1.76}, {'end': 12029.103, 'text': "So digging in deeper, let's assume that a new drug is developed with the goal of lowering the blood pressure more than the existing drug.", 'start': 12021.882, 'duration': 7.221}], 'summary': 'Higher t-value indicates stronger evidence against null hypothesis, with significance level at 5%, showing high correlation.', 'duration': 26.434, 'max_score': 12002.669, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU12002669.jpg'}], 'start': 11114.386, 'title': 'Using pandas and inferential statistics in python', 'summary': 'Demonstrates using pandas in python to calculate basic statistics and plotting salary distribution with an emphasis on inferential statistics. it also covers probability and hypothesis testing, illustrating decreasing probability and important terminologies in statistics.', 'chapters': [{'end': 11447.497, 'start': 11114.386, 'title': 'Exploring basic statistics using pandas in python', 'summary': 'Demonstrates how to use pandas in python to calculate basic statistics like mean, median, mode, range, and descriptive statistics on a sample data set, revealing insights into the income distribution and the importance of considering different measures of central tendency and spread in statistical analysis.', 'duration': 333.111, 'highlights': ['The chapter demonstrates how to use Pandas in Python to calculate basic statistics like mean, median, mode, range, and descriptive statistics on a sample data set. The demonstration includes using Pandas to calculate mean, median, mode, range, and descriptive statistics on a sample data set to showcase the application of Pandas for statistical analysis.', 'The average income in the sample data set is $71,000, with a median income of $54,000, and the most common income being $50,000. The average income in the sample data set is $71,000, the median income is $54,000, and the most common income is $50,000, revealing insights into the income distribution.', 'The range of incomes in the sample data set is 149,000, and the descriptive statistics including the mean, standard deviation, minimum, maximum, and quartiles are also calculated using Pandas. The range of incomes in the sample data set is 149,000, and the descriptive statistics including the mean, standard deviation, minimum, maximum, and quartiles are calculated using Pandas for further analysis.']}, {'end': 11844.609, 'start': 11449.179, 'title': 'Plotting salary distribution and inferential statistics', 'summary': 'Demonstrates plotting salary distribution using a histogram and inferential statistics, with an emphasis on point estimation, applications, and hypothesis testing.', 'duration': 395.43, 'highlights': ['The chapter shows how to plot salary distribution using a histogram and highlights the importance of visualizing data for better understanding and analysis.', "Inferential statistics is explained with a focus on point estimation, which involves finding an approximate value for a population's parameter from random samples.", 'Applications of inferential statistics, including hypotheses testing, confidence interval, binomial theorem, normal distribution, and central limit theorem, are discussed, providing a broad overview of its practical uses.', 'Hypothesis testing is detailed, emphasizing the steps involved in formulating, testing, and making decisions based on hypotheses, with a comparison of theory and hypothesis in scientific and philosophical contexts.']}, {'end': 12040.866, 'start': 11844.769, 'title': 'Probability and hypothesis in data science', 'summary': 'Discusses the probability of a person getting a job using chits, demonstrating a decreasing probability for rob to get the job, indicating his mischief. it also covers important terminologies in statistics like null hypothesis, alternative hypothesis, p-value, and t-value, and their significance in data science and drug development.', 'duration': 196.097, 'highlights': ["Probability of Rob getting the job The probability of Rob getting the job decreases every day, with a significant decrease to 0.032 by day 12, indicating his cheating as he wasn't chosen for 12 consecutive days.", 'Important terminologies in statistics It covers significant terminologies including null hypothesis, alternative hypothesis, p-value, and t-value, explaining their roles in data science and their significance in proving correlation and evidence against the null hypothesis.', 'P-value and t-value significance The p-value measures the probability of finding observed results when the null hypothesis is true, while the t-value represents the calculated difference in standard error, with a 5% or .05 significance indicating high correlation.']}], 'duration': 926.48, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU11114386.jpg', 'highlights': ['The chapter demonstrates using Pandas in Python to calculate basic statistics like mean, median, mode, range, and descriptive statistics on a sample data set.', 'The average income in the sample data set is $71,000, with a median income of $54,000, and the most common income being $50,000.', 'The range of incomes in the sample data set is 149,000, and the descriptive statistics including the mean, standard deviation, minimum, maximum, and quartiles are also calculated using Pandas.', 'The chapter shows how to plot salary distribution using a histogram and highlights the importance of visualizing data for better understanding and analysis.', "Inferential statistics is explained with a focus on point estimation, which involves finding an approximate value for a population's parameter from random samples.", 'Applications of inferential statistics, including hypotheses testing, confidence interval, binomial theorem, normal distribution, and central limit theorem, are discussed, providing a broad overview of its practical uses.', 'Hypothesis testing is detailed, emphasizing the steps involved in formulating, testing, and making decisions based on hypotheses, with a comparison of theory and hypothesis in scientific and philosophical contexts.', "The probability of Rob getting the job decreases every day, with a significant decrease to 0.032 by day 12, indicating his cheating as he wasn't chosen for 12 consecutive days.", 'It covers significant terminologies including null hypothesis, alternative hypothesis, p-value, and t-value, explaining their roles in data science and their significance in proving correlation and evidence against the null hypothesis.', 'The p-value measures the probability of finding observed results when the null hypothesis is true, while the t-value represents the calculated difference in standard error, with a 5% or .05 significance indicating high correlation.']}, {'end': 12880.781, 'segs': [{'end': 12071.233, 'src': 'embed', 'start': 12041.792, 'weight': 1, 'content': [{'end': 12049.919, 'text': 'Now if we get that, that says our null hypothesis is correct, there is no correlation, and the new drug is not doing its job.', 'start': 12041.792, 'duration': 8.127}, {'end': 12056.404, 'text': 'The alternative hypothesis, the new drug does significantly lower the blood pressure more than the existing drug.', 'start': 12050.379, 'duration': 6.025}, {'end': 12059.066, 'text': 'Yay, we got a new drug out there.', 'start': 12057.305, 'duration': 1.761}, {'end': 12062.169, 'text': "And that's our alternative hypothesis, or the H1 or HA.", 'start': 12059.287, 'duration': 2.882}, {'end': 12071.233, 'text': 'And we look at the p-value, results from the evidence like medical trials showing positive results which will reject the null hypothesis.', 'start': 12063.93, 'duration': 7.303}], 'summary': 'New drug significantly lowers blood pressure in medical trials.', 'duration': 29.441, 'max_score': 12041.792, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU12041792.jpg'}, {'end': 12215.869, 'src': 'embed', 'start': 12151.118, 'weight': 0, 'content': [{'end': 12154.6, 'text': "Once you've generated your hypothesis, we want to know the probability of something occurring.", 'start': 12151.118, 'duration': 3.482}, {'end': 12158.263, 'text': 'Probability is a measure of the likelihood of an event to occur.', 'start': 12154.841, 'duration': 3.422}, {'end': 12164.967, 'text': 'Any event can be predicted with total certainty and can only be predicted as a likelihood of its occurrence.', 'start': 12158.723, 'duration': 6.244}, {'end': 12168.369, 'text': 'So any event cannot be predicted with total certainty.', 'start': 12165.487, 'duration': 2.882}, {'end': 12170.931, 'text': 'It can only be predicted as a likelihood of its occurrence.', 'start': 12168.389, 'duration': 2.542}, {'end': 12176.956, 'text': "score prediction, how good you're going to do in whatever sport you're in.", 'start': 12172.072, 'duration': 4.884}, {'end': 12182.642, 'text': "weather prediction, stock prediction if you've studied physics in chaos theory,", 'start': 12176.956, 'duration': 5.686}, {'end': 12188.027, 'text': "even the location of the chair you're sitting on has a probability that it might move three feet over.", 'start': 12182.642, 'duration': 5.385}, {'end': 12194.173, 'text': 'granted that probability is one in like, I think we calculated as under one in trillions upon trillions.', 'start': 12188.027, 'duration': 6.146}, {'end': 12197.976, 'text': "so it's the better the probability, the more likely it's going to happen.", 'start': 12194.173, 'duration': 3.803}, {'end': 12201.658, 'text': "There are some things that have such a low probability that we don't see them.", 'start': 12198.076, 'duration': 3.582}, {'end': 12204.16, 'text': 'So we talk about a random variable.', 'start': 12202.299, 'duration': 1.861}, {'end': 12209.864, 'text': 'A random variable is a variable whose possible values are numerical outcomes of a random phenomena.', 'start': 12204.621, 'duration': 5.243}, {'end': 12212.486, 'text': 'So we have the coin toss.', 'start': 12210.265, 'duration': 2.221}, {'end': 12215.869, 'text': 'How many heads will occur in the series of 20 coin flips?', 'start': 12212.586, 'duration': 3.283}], 'summary': 'Probability measures event likelihood, applicable to sports, weather, stocks, and random phenomena.', 'duration': 64.751, 'max_score': 12151.118, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU12151118.jpg'}, {'end': 12607.929, 'src': 'embed', 'start': 12561.616, 'weight': 3, 'content': [{'end': 12567.339, 'text': "If you're doing stock market analysis, This means your predictions are probably not going to make you much money.", 'start': 12561.616, 'duration': 5.723}, {'end': 12572.74, 'text': 'Where if you have a very small deviation, you might be right on target and set to become a millionaire.', 'start': 12568.179, 'duration': 4.561}, {'end': 12575.201, 'text': 'Which leads us to the Z-score.', 'start': 12573.441, 'duration': 1.76}, {'end': 12579.322, 'text': 'Z-score tells you how far from the mean a data point is.', 'start': 12575.621, 'duration': 3.701}, {'end': 12582.923, 'text': 'It is measured in terms of standard deviations from the mean.', 'start': 12579.942, 'duration': 2.981}, {'end': 12587.064, 'text': 'Around 68% of the results are found between one standard deviation.', 'start': 12583.183, 'duration': 3.881}, {'end': 12591.806, 'text': 'Around 95% of the results are found between two standard deviations.', 'start': 12587.765, 'duration': 4.041}, {'end': 12594.006, 'text': 'And you read the symbols.', 'start': 12592.606, 'duration': 1.4}, {'end': 12596.167, 'text': 'Of course, they love to throw some Greek letters in there.', 'start': 12594.186, 'duration': 1.981}, {'end': 12598.847, 'text': 'We have mu minus two sigma.', 'start': 12596.347, 'duration': 2.5}, {'end': 12601.448, 'text': 'Mu is just a quick way.', 'start': 12599.448, 'duration': 2}, {'end': 12603.388, 'text': "It's a kind of funky U.", 'start': 12601.468, 'duration': 1.92}, {'end': 12604.509, 'text': 'It just means the mean.', 'start': 12603.388, 'duration': 1.121}, {'end': 12607.929, 'text': 'And then the sigma is the standard deviation.', 'start': 12605.409, 'duration': 2.52}], 'summary': 'Small deviation in stock predictions can lead to millionaire status. z-score measures distance from mean in standard deviations.', 'duration': 46.313, 'max_score': 12561.616, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU12561616.jpg'}], 'start': 12041.792, 'title': 'Probability and data analysis', 'summary': "Covers hypothesis testing, confidence intervals, probability, and binomial distribution, and explains concepts like skewed data, standard deviation, z-score, central limit theorem, and bayes' theorem, with practical examples and use cases.", 'chapters': [{'end': 12485.029, 'start': 12041.792, 'title': 'Hypothesis testing and probability in statistics', 'summary': 'Discusses hypothesis testing, confidence intervals, probability, and binomial distribution in statistics, including examples such as drug trials and football match outcomes, and the calculation of cumulative probabilities for specific outcomes.', 'duration': 443.237, 'highlights': ["The alternative hypothesis states that the new drug significantly lowers blood pressure more than the existing drug, with a focus on a 5% significance level for p-values and the use of t-values for comparing positive test results. The alternative hypothesis focuses on the new drug's ability to lower blood pressure significantly, with emphasis on a 5% significance level for p-values and the use of t-values for comparing positive test results.", 'The concept of confidence intervals is explained, with an example demonstrating how 95% of dog owners surveyed bought between 200 to 300 cans of dog food per year, showcasing a confidence interval of 200 to 300 cans with 95% certainty. The concept of confidence intervals is explained, with an example demonstrating how 95% of dog owners surveyed bought between 200 to 300 cans of dog food per year, showcasing a confidence interval of 200 to 300 cans with 95% certainty.', 'Probability is discussed as a measure of the likelihood of an event occurring, with examples including sports score predictions, weather forecasts, and the randomness of events such as the movement of a chair. Probability is discussed as a measure of the likelihood of an event occurring, with examples including sports score predictions, weather forecasts, and the randomness of events such as the movement of a chair.', 'The concept of random variables is introduced, with examples such as the number of heads in a series of coin flips, the selection of colored balls from a bag, and the sum of digits on two dice, highlighting the unpredictability of certain outcomes. The concept of random variables is introduced, with examples such as the number of heads in a series of coin flips, the selection of colored balls from a bag, and the sum of digits on two dice, highlighting the unpredictability of certain outcomes.', 'Binomial distribution is explained, focusing on the probability of success or failure in repeated experiments or trials, with an example of calculating the chances of a football team winning a series of matches. Binomial distribution is explained, focusing on the probability of success or failure in repeated experiments or trials, with an example of calculating the chances of a football team winning a series of matches.']}, {'end': 12880.781, 'start': 12485.029, 'title': 'Understanding probability and data analysis', 'summary': "Explains the importance of probability and data analysis, including concepts like skewed data, standard deviation, z-score, central limit theorem, and bayes' theorem, with practical examples and use cases.", 'duration': 395.752, 'highlights': ["The Z-score tells you how far from the mean a data point is, with 68% of results found between one standard deviation and 95% between two standard deviations. The Z-score provides a measure of a data point's distance from the mean and highlights that around 68% of results fall within one standard deviation and approximately 95% fall within two standard deviations.", 'The chapter emphasizes the importance of understanding skewed data and its impact on probability and data analysis. The importance of recognizing skewed data and its implications on probability and data analysis is highlighted, stressing the need for closer examination when dealing with skewed probabilities.', 'Conditional probability, as demonstrated through a use case of determining the chance of a person getting lung disease due to smoking, is discussed in detail, showcasing the practical application of probability concepts. The chapter delves into conditional probability through a use case, illustrating the calculation of the likelihood of a person developing lung disease due to smoking, demonstrating the practical application of probability concepts.']}], 'duration': 838.989, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU12041792.jpg', 'highlights': ['The alternative hypothesis states that the new drug significantly lowers blood pressure more than the existing drug, with a focus on a 5% significance level for p-values and the use of t-values for comparing positive test results.', 'The concept of confidence intervals is explained, with an example demonstrating how 95% of dog owners surveyed bought between 200 to 300 cans of dog food per year, showcasing a confidence interval of 200 to 300 cans with 95% certainty.', 'Probability is discussed as a measure of the likelihood of an event occurring, with examples including sports score predictions, weather forecasts, and the randomness of events such as the movement of a chair.', 'The concept of random variables is introduced, with examples such as the number of heads in a series of coin flips, the selection of colored balls from a bag, and the sum of digits on two dice, highlighting the unpredictability of certain outcomes.', 'Binomial distribution is explained, focusing on the probability of success or failure in repeated experiments or trials, with an example of calculating the chances of a football team winning a series of matches.', 'The Z-score tells you how far from the mean a data point is, with 68% of results found between one standard deviation and 95% between two standard deviations.', 'The chapter emphasizes the importance of understanding skewed data and its impact on probability and data analysis.', 'Conditional probability, as demonstrated through a use case of determining the chance of a person getting lung disease due to smoking, is discussed in detail, showcasing the practical application of probability concepts.']}, {'end': 14323.4, 'segs': [{'end': 12914.1, 'src': 'embed', 'start': 12881.926, 'weight': 0, 'content': [{'end': 12886.647, 'text': 'And you can just plug the numbers right in and we get a 3.33% chance.', 'start': 12881.926, 'duration': 4.721}, {'end': 12891.509, 'text': 'Hence, there is a 3.33% chance that a person who smokes will get a lung disease.', 'start': 12886.987, 'duration': 4.522}, {'end': 12897.61, 'text': "So we're going to pull up a little Python code, always my favorite, roll up the sleeves.", 'start': 12892.609, 'duration': 5.001}, {'end': 12902.752, 'text': "Keep in mind, we're going to be doing this kind of like the back end way.", 'start': 12898.491, 'duration': 4.261}, {'end': 12905.451, 'text': "so that you can see what's going on.", 'start': 12903.789, 'duration': 1.662}, {'end': 12914.1, 'text': "And then later on we're going to create, we'll get into another demo which shows you some of the tools that are already pre-built for this.", 'start': 12905.851, 'duration': 8.249}], 'summary': 'There is a 3.33% chance of a smoker getting a lung disease.', 'duration': 32.174, 'max_score': 12881.926, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU12881926.jpg'}, {'end': 13098.128, 'src': 'embed', 'start': 13067.611, 'weight': 7, 'content': [{'end': 13068.691, 'text': "That's why they put it in a set.", 'start': 13067.611, 'duration': 1.08}, {'end': 13072.632, 'text': 'And if you remember from range, it is up to seven.', 'start': 13069.331, 'duration': 3.301}, {'end': 13075.213, 'text': 'So this is going to be one, two, three, four, five, six.', 'start': 13072.833, 'duration': 2.38}, {'end': 13077.094, 'text': 'It will not include the seven.', 'start': 13075.694, 'duration': 1.4}, {'end': 13079.255, 'text': 'And the same thing for our dice B.', 'start': 13077.674, 'duration': 1.581}, {'end': 13088.201, 'text': "And then what we're going to do is we're going to create a list which is the product of A and B.", 'start': 13081.656, 'duration': 6.545}, {'end': 13089.922, 'text': "So it's A plus B.", 'start': 13088.201, 'duration': 1.721}, {'end': 13098.128, 'text': "Now, if we go ahead and run this, it'll print that out and you'll see, in this case, when they say product, because it's an iteration tool,", 'start': 13089.922, 'duration': 8.206}], 'summary': 'Creating a list as the product of a and b, up to seven, resulting in a plus b.', 'duration': 30.517, 'max_score': 13067.611, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU13067611.jpg'}, {'end': 13331.823, 'src': 'embed', 'start': 13300.798, 'weight': 1, 'content': [{'end': 13304.3, 'text': 'so we have 36 total options.', 'start': 13300.798, 'duration': 3.502}, {'end': 13311.642, 'text': 'we have 12 that are multiple, that add up to a multiple of three, and we can easily convert,', 'start': 13304.3, 'duration': 7.342}, {'end': 13319.424, 'text': 'compute the probability of this by simply taking the length of our favorable outcome over the length of the event space.', 'start': 13311.642, 'duration': 7.782}, {'end': 13331.823, 'text': 'and if we print it out and put that in there probability last lines, we just type it in, we end up with a .3333 chance.', 'start': 13322.219, 'duration': 9.604}], 'summary': 'Out of 36 options, 12 add up to a multiple of three, resulting in a probability of .3333.', 'duration': 31.025, 'max_score': 13300.798, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU13300798.jpg'}, {'end': 14037.449, 'src': 'embed', 'start': 14008.388, 'weight': 4, 'content': [{'end': 14011.43, 'text': "That's usually what people are talking about when they're pulling this in.", 'start': 14008.388, 'duration': 3.042}, {'end': 14019.614, 'text': "And one of the nice things about the Gaussian, if you go to their website, to sklearn, the Naive Bayes Gaussian, There's a lot of cool features.", 'start': 14012.11, 'duration': 7.504}, {'end': 14021.716, 'text': 'One of them is you can do partial fit on here.', 'start': 14019.714, 'duration': 2.002}, {'end': 14026.68, 'text': "That means if you have a huge amount of data, you don't have to process it all at once.", 'start': 14022.797, 'duration': 3.883}, {'end': 14031.524, 'text': 'You can batch it into the Gaussian NB model.', 'start': 14027.2, 'duration': 4.324}, {'end': 14037.449, 'text': "And there's many other different things you can do with it as far as fitting the data and how you manipulate it.", 'start': 14031.824, 'duration': 5.625}], 'summary': 'Gaussian nb model allows partial fit for processing large data batches and offers various data manipulation features.', 'duration': 29.061, 'max_score': 14008.388, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU14008388.jpg'}, {'end': 14132.806, 'src': 'embed', 'start': 14104.058, 'weight': 2, 'content': [{'end': 14109.244, 'text': "Here's predicted, so true positive, false positive, false negative, true negative.", 'start': 14104.058, 'duration': 5.186}, {'end': 14118.041, 'text': 'And if we go ahead and run this, there we have it, 65, 3, 7, 25.', 'start': 14111.746, 'duration': 6.295}, {'end': 14125.424, 'text': "And in this particular prediction, we had 65 were predicted the truth as far as a purchase, they're gonna make a purchase.", 'start': 14118.041, 'duration': 7.383}, {'end': 14127.324, 'text': 'And we guessed three wrong.', 'start': 14126.104, 'duration': 1.22}, {'end': 14132.806, 'text': 'And then we had 25 we predicted would not purchase and seven of them did.', 'start': 14128.325, 'duration': 4.481}], 'summary': 'Prediction results show 65 true positives, 3 false positives, 7 false negatives, and 25 true negatives.', 'duration': 28.748, 'max_score': 14104.058, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU14104058.jpg'}, {'end': 14281.165, 'src': 'embed', 'start': 14252.712, 'weight': 5, 'content': [{'end': 14255.096, 'text': "And then we're going to create the contour.", 'start': 14252.712, 'duration': 2.384}, {'end': 14260.158, 'text': "That's that nice line that's drawn down the middle on here with the red green.", 'start': 14256.837, 'duration': 3.321}, {'end': 14263.959, 'text': "That's what this is doing right here with the reshape.", 'start': 14261.459, 'duration': 2.5}, {'end': 14268.121, 'text': 'And notice that we had to do the dot T.', 'start': 14264.34, 'duration': 3.781}, {'end': 14278.964, 'text': 'If you remember from NumPy, if you did the NumPy module, you end up with pairs, you know, X1, X2, X1, X2, next row, and so forth.', 'start': 14268.121, 'duration': 10.843}, {'end': 14281.165, 'text': "You have to flip it so it's all one row.", 'start': 14279.225, 'duration': 1.94}], 'summary': 'Creating contour with reshape and dot t in numpy.', 'duration': 28.453, 'max_score': 14252.712, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU14252712.jpg'}], 'start': 12881.926, 'title': 'Python set, iteration, probability, and naive bayes model', 'summary': 'Covers the use of sets and iteration in python, demonstrates probability calculations for rolling dice, explains confusion matrix and naive bayes classifier, and includes practical demonstrations of model creation and visualization, with specific focus on accuracy and performance metrics such as probabilities and true positive/negative predictions.', 'chapters': [{'end': 13188.689, 'start': 12881.926, 'title': 'Python set and iteration demo', 'summary': 'Demonstrates the use of sets in python to handle unique values and explains the concept of iteration using dice as an example, showcasing the creation of a tuple of all possible outcomes of two dice with 36 combinations.', 'duration': 306.763, 'highlights': ['The chapter demonstrates the use of sets in Python to handle unique values, showcasing the creation of a set with unique values and the conversion of a list to a set, deleting duplicate elements. Demonstrates the creation of a set with unique values and the deletion of duplicate elements when converting a list to a set.', 'The chapter explains the concept of iteration in Python using dice as an example, showcasing the creation of a tuple of all possible outcomes of two dice with 36 combinations. Showcases the creation of a tuple representing all possible outcomes of two dice, totaling 36 combinations.', 'The chapter also covers the creation of an event space using the product of dice faces, repeating the process for different possible variables. Demonstrates the creation of an event space using the product of dice faces, repeating the process for different possible variables.']}, {'end': 13481.812, 'start': 13189.23, 'title': 'Probability of rolling sums', 'summary': 'Demonstrates the calculation of probabilities for rolling dice, showing the outcomes for multiples of three and five, with a probability of .3333 for multiples of three and 11.62% for sums which are multiples of five but not three, based on 36 and 7,776 total options respectively.', 'duration': 292.582, 'highlights': ['The favorable outcome for multiples of three is 12 out of 36 total options, resulting in a probability of .3333 or roughly a third. The favorable outcome for multiples of three is 12 out of 36 total options, resulting in a probability of .3333.', 'The probability for rolling sums which are multiples of five but not three is 11.62%, based on 904 favorable outcomes out of 7,776 total options. The probability for rolling sums which are multiples of five but not three is 11.62%, based on 904 favorable outcomes out of 7,776 total options.', 'The chapter also emphasizes that calculating probabilities is based on the number of options and the desired outcomes, leading to a confusion matrix for further analysis. The chapter also emphasizes that calculating probabilities is based on the number of options and the desired outcomes, leading to a confusion matrix for further analysis.']}, {'end': 13862.792, 'start': 13482.814, 'title': 'Understanding confusion matrix and naive bayes classifier', 'summary': 'Explains the concept of confusion matrix in evaluating classification model performance, emphasizing the importance of minimizing false positives in medical situations, and then delves into the use of naive bayes classifier with a practical demonstration on a social network ads dataset.', 'duration': 379.978, 'highlights': ['The chapter explains the significance of confusion matrix in evaluating classification model performance and emphasizes the importance of minimizing false positives, especially in medical situations, to ensure the validity of test results.', "It discusses the application of confusion matrix in cancer prediction, highlighting the critical need to avoid false negatives and the importance of accuracy, precision, and recall metrics in evaluating the model's performance.", 'The chapter introduces the concept of Naive Bayes classifier and its assumption of independent features, and then proceeds to demonstrate its application using a practical example of a social network ads dataset, focusing on predicting user purchases based on gender, age, and estimated salary.', 'The practical demonstration includes importing necessary libraries, reading the dataset, and extracting relevant features for training the Naive Bayes classifier, providing a step-by-step guide for implementing the classification model.', 'The chapter also mentions the availability of various classification methods in scikit-learn, with a specific focus on Naive Bayes classifier for its simplified predictions based on independent feature assumptions.']}, {'end': 14323.4, 'start': 13864.072, 'title': 'Naive bayes model and visualization', 'summary': 'Explains the process of importing and preprocessing data, splitting it into training and testing sets, scaling features, creating a gaussian naive bayes model, running predictions, generating a confusion matrix, and visualizing the results through scatter plots, with a focus on the 65 true positive predictions and the 25 true negative predictions.', 'duration': 459.328, 'highlights': ['The chapter explains the process of importing and preprocessing data, splitting it into training and testing sets, scaling features, creating a Gaussian Naive Bayes model, running predictions, generating a confusion matrix, and visualizing the results through scatter plots, with a focus on the 65 true positive predictions and the 25 true negative predictions.', 'The process includes importing and preprocessing data, splitting it into training and testing sets, with 25% going to the test set and 75% to the training set, creating a Gaussian Naive Bayes model, and running predictions to generate a confusion matrix.', "The focus is on the 65 true positive predictions and the 25 true negative predictions, indicating the model's accuracy in predicting purchases.", "The visualization involves scatter plots showing the estimated salary and age data, with green dots representing purchases and red dots representing non-purchases, highlighting the overlap between the two categories and the model's limitations in accurately classifying all data points."]}], 'duration': 1441.474, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU12881926.jpg', 'highlights': ['The chapter demonstrates the use of sets in Python to handle unique values and the conversion of a list to a set, deleting duplicate elements.', 'The chapter explains the concept of iteration in Python using dice as an example, showcasing the creation of a tuple representing all possible outcomes of two dice, totaling 36 combinations.', 'The favorable outcome for multiples of three is 12 out of 36 total options, resulting in a probability of .3333.', 'The probability for rolling sums which are multiples of five but not three is 11.62%, based on 904 favorable outcomes out of 7,776 total options.', 'The chapter explains the significance of confusion matrix in evaluating classification model performance and emphasizes the importance of minimizing false positives, especially in medical situations, to ensure the validity of test results.', "It discusses the application of confusion matrix in cancer prediction, highlighting the critical need to avoid false negatives and the importance of accuracy, precision, and recall metrics in evaluating the model's performance.", 'The chapter introduces the concept of Naive Bayes classifier and its assumption of independent features, and then proceeds to demonstrate its application using a practical example of a social network ads dataset, focusing on predicting user purchases based on gender, age, and estimated salary.', 'The chapter explains the process of importing and preprocessing data, splitting it into training and testing sets, scaling features, creating a Gaussian Naive Bayes model, running predictions, generating a confusion matrix, and visualizing the results through scatter plots, with a focus on the 65 true positive predictions and the 25 true negative predictions.']}, {'end': 15558.84, 'segs': [{'end': 14541.68, 'src': 'embed', 'start': 14512.518, 'weight': 2, 'content': [{'end': 14514.779, 'text': 'Implementation of linear regression.', 'start': 14512.518, 'duration': 2.261}, {'end': 14516.52, 'text': 'Now we get into my favorite part.', 'start': 14514.919, 'duration': 1.601}, {'end': 14521.802, 'text': "Let's understand how multiple linear regression works by implementing it in Python.", 'start': 14516.88, 'duration': 4.922}, {'end': 14528.624, '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': 14522.042, 'duration': 6.582}, {'end': 14530.905, 'text': "We're going to start looking at the expenditure of the company.", 'start': 14528.824, 'duration': 2.081}, {'end': 14532.085, 'text': "We're going to go back to that.", 'start': 14531.005, 'duration': 1.08}, {'end': 14533.466, 'text': "We're going to predict its profit.", 'start': 14532.245, 'duration': 1.221}, {'end': 14541.68, 'text': "But instead of predicting it just on the R&D, we're going to look at other factors like administration costs, marketing costs, and so on.", 'start': 14533.726, 'duration': 7.954}], 'summary': 'Implementing multiple linear regression in python to predict company profit using various factors.', 'duration': 29.162, 'max_score': 14512.518, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU14512518.jpg'}, {'end': 14640.173, 'src': 'embed', 'start': 14587.32, 'weight': 0, 'content': [{'end': 14592.464, 'text': "Let me go ahead and paste our First piece of code in there, and let's walk through what libraries we're importing.", 'start': 14587.32, 'duration': 5.144}, {'end': 14599.469, 'text': "First, we're going to import numpy as np, and then I want you to skip one line and look at import pandas as pd.", 'start': 14592.604, 'duration': 6.865}, {'end': 14603.111, 'text': 'These are very common tools that you need with most of your linear regression.', 'start': 14599.749, 'duration': 3.362}, {'end': 14610.315, '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': 14603.351, 'duration': 6.964}, {'end': 14612.016, 'text': 'so you always import that right off the beginning.', 'start': 14610.315, 'duration': 1.701}, {'end': 14615.919, 'text': "Pandas, although you don't have to have it for your sklearn libraries.", 'start': 14612.176, 'duration': 3.743}, {'end': 14622.203, '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': 14615.919, 'duration': 6.284}, {'end': 14624.525, 'text': 'and it has a lot of tools also in addition to that.', 'start': 14622.203, 'duration': 2.322}, {'end': 14628.207, 'text': "So we usually like to use the pandas when we can, and I'll show you what that looks like.", 'start': 14624.725, 'duration': 3.482}, {'end': 14632.931, 'text': 'The other three lines are for us to get a visual of this data and take a look at it.', 'start': 14628.528, 'duration': 4.403}, {'end': 14640.173, 'text': "So we're going to import matplotlibrary.pyplot as plt and then seaborn as sns.", 'start': 14633.371, 'duration': 6.802}], 'summary': 'Import numpy and pandas for linear regression data manipulation and visualization.', 'duration': 52.853, 'max_score': 14587.32, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU14587320.jpg'}, {'end': 14815.778, 'src': 'embed', 'start': 14786.314, 'weight': 6, 'content': [{'end': 14790.278, 'text': "You have your rows, and remember, when we're programming, we always start with zero.", 'start': 14786.314, 'duration': 3.964}, {'end': 14791.359, 'text': "We don't start with one.", 'start': 14790.358, 'duration': 1.001}, {'end': 14793.841, 'text': 'So it shows the first five rows.', 'start': 14791.579, 'duration': 2.262}, {'end': 14796.441, 'text': '0, 1, 2, 3, 4.', 'start': 14793.861, 'duration': 2.58}, {'end': 14798.245, 'text': 'And then it shows your different columns.', 'start': 14796.443, 'duration': 1.802}, {'end': 14803.109, 'text': 'R&D spend, administration, marketing spend, state, profit.', 'start': 14798.685, 'duration': 4.424}, {'end': 14806.112, 'text': 'It even notes that the top are column names.', 'start': 14803.309, 'duration': 2.803}, {'end': 14811.477, 'text': "It was never told that, but Pandas is able to recognize a lot of things that they're not the same as the data rows.", 'start': 14806.472, 'duration': 5.005}, {'end': 14815.778, 'text': "Why don't we go ahead and open this file up in a CSV so you can actually see the raw data.", 'start': 14811.817, 'duration': 3.961}], 'summary': 'Pandas displays first 5 rows and columns of data, starting with 0, not 1.', 'duration': 29.464, 'max_score': 14786.314, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU14786314.jpg'}], 'start': 14323.4, 'title': 'Linear regression and data visualization', 'summary': 'Covers visualizing test set results, implementing multiple linear regression in python, predicting company profit with precision of 90%, recall of 0.96, and accuracy of about 90%, importing and analyzing a dataset using pandas, visualizing data using seaborn, preparing data for linear regression, and creating a linear regression model with a single click and one line of code, producing 200 test set results.', 'chapters': [{'end': 14677.443, 'start': 14323.4, 'title': 'Visualizing test results and implementing multiple linear regression', 'summary': 'Covers visualizing test set results, including 25% of the data, and implementing multiple linear regression in python, aiming to predict company profit based on factors like r&d, administration, and marketing costs, with precision of 90%, recall of 0.96, and accuracy of about 90%.', 'duration': 354.043, 'highlights': ['Visualizing test set results with 25% of the data compared to 75%, showing the effectiveness of the estimates and the importance of graphs and numbers in conveying the accuracy of the model. Visualizing the test set results with 25% of the data as compared to 75%, demonstrating the effectiveness of the estimates and the significance of graphs and numbers in conveying the accuracy of the model.', 'Implementing multiple linear regression in Python to predict company profit based on factors like R&D, administration, and marketing costs, with precision of 90%, recall of 0.96, and accuracy of about 90%. Implementing multiple linear regression in Python to predict company profit based on factors like R&D, administration, and marketing costs, achieving a precision of 90%, recall of 0.96, and accuracy of about 90%.', 'Importing numpy as np and pandas as pd for data manipulation, and matplotlibrary.pyplot as plt and seaborn as sns for visualization, with emphasis on the significance of these tools for linear regression and data representation. Importing numpy as np and pandas as pd for data manipulation, and matplotlibrary.pyplot as plt and seaborn as sns for visualization, highlighting the significance of these tools for linear regression and data representation.']}, {'end': 14857.031, 'start': 14677.563, 'title': 'Importing and analyzing data in pandas', 'summary': 'Covers the process of importing and analyzing a dataset using pandas, including setting up variables, loading the dataset, and extracting independent and dependent variables, and provides insights into how pandas presents the data in a readable format.', 'duration': 179.468, 'highlights': ['The chapter covers the process of importing and analyzing a dataset using Pandas. It explains the steps involved in setting up variables, loading the dataset, and extracting independent and dependent variables.', 'Pandas presents the data in a readable format resembling an Excel spreadsheet. It demonstrates how Pandas displays the dataset with rows and columns, and the ability to recognize column names.', 'Insights into the dataset are provided, highlighting the difficulty in interpreting raw data. It emphasizes the challenge of interpreting numerical data without contextual understanding, emphasizing the need for tools like Pandas.']}, {'end': 15315.451, 'start': 14872.461, 'title': 'Visualizing and preparing data for linear regression', 'summary': 'Discusses visualizing data using seaborn to understand the correlation between variables, and preparing the data for linear regression by using label and one hot encoders, as well as splitting the data into training and testing sets.', 'duration': 442.99, 'highlights': ['The chapter discusses visualizing data using seaborn to understand the correlation between variables The chapter explains how seaborn is used to visualize data, particularly the correlation between variables like R&D spending, administration, marketing spending, and profit.', 'Preparing the data for linear regression by using label and one hot encoders The transcript details the process of preparing the data for linear regression by utilizing label encoder and one hot encoder to transform categorical variables into numerical format, ensuring the data is ready for analysis.', "Splitting the data into training and testing sets for linear regression The chapter emphasizes the importance of splitting the data into training and testing sets, using the train_test_split function from the sklearn module, to ensure ethical and accurate assessment of the model's fit."]}, {'end': 15558.84, 'start': 15315.831, 'title': 'Creating linear regression model', 'summary': 'Covers creating a linear regression model using sklearn.linear_model, fitting the data, making predictions, and calculating the coefficients and intercepts, simplifying the process with the computer. the model is created and fitted with a single click and one line of code, producing 200 test set results.', 'duration': 243.009, 'highlights': ['The model is created and fitted with a single click and one line of code, simplifying the process with the computer, and producing 200 test set results (Y predict) for different test variables.', 'The chapter covers creating a linear regression model using sklearn.linear_model, fitting the data, making predictions, and calculating the coefficients and intercepts, simplifying the process with the computer.', 'The regressor equals the linear regression model that has all the math built in, eliminating the need to compute it individually and fitting the data to the linear regression model using X train and Y train.', "The regressor.coefficient and regressor.intercept are used to calculate the coefficients and intercepts, providing a quick flash at what's going on behind the line and fitting variables into the formula y=slope1*column1 + slope2*column2 + ... + c, the coefficient."]}], 'duration': 1235.44, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU14323400.jpg', 'highlights': ['Implementing multiple linear regression in Python to predict company profit based on factors like R&D, administration, and marketing costs, achieving a precision of 90%, recall of 0.96, and accuracy of about 90%.', 'Preparing the data for linear regression by utilizing label encoder and one hot encoder to transform categorical variables into numerical format, ensuring the data is ready for analysis.', "The chapter emphasizes the importance of splitting the data into training and testing sets, using the train_test_split function from the sklearn module, to ensure ethical and accurate assessment of the model's fit.", 'The model is created and fitted with a single click and one line of code, simplifying the process with the computer, and producing 200 test set results (Y predict) for different test variables.', 'The chapter covers creating a linear regression model using sklearn.linear_model, fitting the data, making predictions, and calculating the coefficients and intercepts, simplifying the process with the computer.', 'Insights into the dataset are provided, highlighting the difficulty in interpreting raw data and emphasizing the need for tools like Pandas.', 'The chapter discusses visualizing data using seaborn to understand the correlation between variables like R&D spending, administration, marketing spending, and profit.', 'Importing numpy as np and pandas as pd for data manipulation, and matplotlibrary.pyplot as plt and seaborn as sns for visualization, highlighting the significance of these tools for linear regression and data representation.', 'The chapter covers the process of importing and analyzing a dataset using Pandas, explaining the steps involved in setting up variables, loading the dataset, and extracting independent and dependent variables.']}, {'end': 17163.926, 'segs': [{'end': 15629.742, 'src': 'embed', 'start': 15600.224, 'weight': 4, 'content': [{'end': 15602.726, 'text': 'And when we go ahead and run this, we see we get a .9352.', 'start': 15600.224, 'duration': 2.502}, {'end': 15603.326, 'text': "That's the R2 score.", 'start': 15602.726, 'duration': 0.6}, {'end': 15613.694, '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': 15606.448, 'duration': 7.246}, {'end': 15618.938, 'text': 'O and higher shows that this is a very valid prediction based on the R2 score.', 'start': 15614.274, 'duration': 4.664}, {'end': 15625.961, 'text': 'And if R squared value of 0.91 or 9.2 as we got on our model, because remember it does have a random generation involved.', 'start': 15619.258, 'duration': 6.703}, {'end': 15629.742, 'text': 'This proves the model is a good model, which means success!', 'start': 15626.121, 'duration': 3.621}], 'summary': 'Model achieved r2 score of 0.9352, indicating high validity and success.', 'duration': 29.518, 'max_score': 15600.224, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU15600224.jpg'}, {'end': 15666.618, 'src': 'embed', 'start': 15637.685, 'weight': 1, 'content': [{'end': 15645.249, 'text': "Let's take an example and see how we can apply logistic regression to predict the number that is shown in the image.", 'start': 15637.685, 'duration': 7.564}, {'end': 15647.81, 'text': 'So this is actually a live demo.', 'start': 15645.489, 'duration': 2.321}, {'end': 15652.172, 'text': 'I will take you into Jupyter Notebook and show the code.', 'start': 15648.03, 'duration': 4.142}, {'end': 15656.174, 'text': 'But before that, let me take you through a couple of slides to explain what we are trying to do.', 'start': 15652.312, 'duration': 3.862}, {'end': 15666.618, 'text': "So let's say you have an 8x8 image and the image has a number 1, 2, 3, 4 and you need to train your model to predict what this number is.", 'start': 15656.474, 'duration': 10.144}], 'summary': 'Using logistic regression to predict numbers in images.', 'duration': 28.933, 'max_score': 15637.685, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU15637685.jpg'}, {'end': 16627.282, 'src': 'embed', 'start': 16599.579, 'weight': 0, 'content': [{'end': 16607.383, 'text': "So it's like 38 plus 44 plus 43, and so on, and divide that by the total number of test observations.", 'start': 16599.579, 'duration': 7.804}, {'end': 16611.287, 'text': 'that will give you the percentage accuracy using a confusion matrix.', 'start': 16607.383, 'duration': 3.904}, {'end': 16618.854, 'text': 'Now let us visualize this confusion matrix in a slightly more sophisticated way using a heat map.', 'start': 16611.647, 'duration': 7.207}, {'end': 16623.297, 'text': "So we will create a heat map with some, we'll add some colors as well.", 'start': 16619.054, 'duration': 4.243}, {'end': 16627.282, 'text': "It's like a more visually, visually more appealing.", 'start': 16623.859, 'duration': 3.423}], 'summary': 'Calculate accuracy as 42% and visualize confusion matrix with heatmap.', 'duration': 27.703, 'max_score': 16599.579, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU16599579.jpg'}], 'start': 15559.12, 'title': 'Logistic regression and model validation', 'summary': 'Covers the importance of r squared value in model validation, achieving a .9352 r2 score, logistic regression for image prediction with 94% accuracy, training a model to recognize digits using 8x8 pixel image dataset, and understanding confusion matrix metrics for evaluating classifier performance.', 'chapters': [{'end': 15637.465, 'start': 15559.12, 'title': 'Validating model with r squared value', 'summary': "Discusses the importance of r squared value in validating the model's prediction accuracy, demonstrating a .9352 r2 score and emphasizing the significance of achieving an r squared value in the upper 90s for a very valid prediction.", 'duration': 78.345, 'highlights': ["The R2 score of .9352 is obtained, indicating a high level of accuracy in the model's predictions.", 'Emphasizing the significance of achieving an R squared value in the upper 90s for a very valid prediction.', "Highlighting the importance of R squared value in validating the model's prediction accuracy."]}, {'end': 15963.377, 'start': 15637.685, 'title': 'Logistic regression for image prediction', 'summary': "Demonstrates the application of logistic regression to predict numbers in images, achieving an accuracy of about 94% using confusion matrix, and explains the significance of the matrix in evaluating the model's accuracy.", 'duration': 325.692, 'highlights': ['The model achieves an accuracy of about 94% using the confusion matrix. The logistic regression model achieves an accuracy of about 94% when evaluating the prediction of numbers in images using the confusion matrix.', 'The confusion matrix is used for identifying the accuracy of a classification model. The confusion matrix is utilized to determine the accuracy of a classification model, such as logistic regression, by comparing predicted and actual values.', "The significance of the confusion matrix lies in having the maximum numbers in its diagonal. The confusion matrix's key aspect is having the maximum numbers in its diagonal, indicating accurate predictions, while the off-diagonal cells represent misclassifications."]}, {'end': 16330.536, 'start': 15963.457, 'title': 'Logistic regression for digit recognition', 'summary': "Discusses training a logistic regression model to recognize digits from 0 to 9 using an 8x8 pixel image dataset with 1383 entries in the training set and 414 in the test set, and the model's performance is evaluated.", 'duration': 367.079, 'highlights': ['The dataset consists of 1383 entries in the training set and 414 in the test set, with each image being 8x8 pixels in size. The training set contains 1383 entries and the test set contains 414 entries, with each image being 8x8 pixels.', 'The logistic regression model is used to recognize digits from 0 to 9 based on the pixel activation patterns, and the training and testing process is outlined. The logistic regression model is trained to recognize digits based on the activation patterns of the pixels, and the process of training and testing the model is explained.', 'The logistic regression library from scikit-learn is imported and an instance of logistic regression model is created for training. The logistic regression library from scikit-learn is imported, and an instance of the logistic regression model is created for training.']}, {'end': 17163.926, 'start': 16331.016, 'title': 'Logistic regression and confusion matrix', 'summary': 'Covers the training process of a logistic regression model, testing its accuracy, understanding confusion matrix, and the importance of confusion matrix metrics in evaluating classifier performance, with a focus on accuracy, precision, recall, and f1 score.', 'duration': 832.91, 'highlights': ['The model has performed up to 94% accuracy, determined by the score method and confirmed using a confusion matrix. The logistic regression model achieved a 94% accuracy, calculated by the score method and verified through a confusion matrix.', 'Confusion matrix is crucial for visualizing and identifying individual class predictions and errors in a classification problem, providing insights into true positives, true negatives, false positives, and false negatives. The confusion matrix is essential for visualizing and identifying individual class predictions and errors in a classification problem, revealing true positives, true negatives, false positives, and false negatives.', "Confusion matrix metrics, including accuracy, precision, recall, and F1 score, play a vital role in evaluating the performance of classifiers, offering insights into the model's ability to classify values correctly and predict positive values accurately. Confusion matrix metrics such as accuracy, precision, recall, and F1 score are crucial for evaluating classifier performance, providing insights into the model's ability to classify values correctly and predict positive values accurately."]}], 'duration': 1604.806, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU15559120.jpg', 'highlights': ['The logistic regression model achieves an accuracy of about 94% using the confusion matrix.', "The R2 score of .9352 is obtained, indicating a high level of accuracy in the model's predictions.", "The confusion matrix's key aspect is having the maximum numbers in its diagonal, indicating accurate predictions.", 'The logistic regression model is trained to recognize digits based on the activation patterns of the pixels.', 'Confusion matrix metrics such as accuracy, precision, recall, and F1 score are crucial for evaluating classifier performance.']}, {'end': 18498.12, 'segs': [{'end': 17279.076, 'src': 'embed', 'start': 17214.921, 'weight': 1, 'content': [{'end': 17221.643, 'text': "because it does a better job guessing who speaks English and has a higher accuracy, because in this case that is what we're looking for.", 'start': 17214.921, 'duration': 6.722}, {'end': 17231.225, 'text': "So with that we'll go ahead and pull up a demo so you can see what this looks like in the Python setup and in the actual coding.", 'start': 17223.143, 'duration': 8.082}, {'end': 17234.28, 'text': "For this, we'll go into Anaconda Navigator.", 'start': 17231.718, 'duration': 2.562}, {'end': 17244.77, 'text': "If you're not familiar with Anaconda, it's a really good tool to use as far as doing display and demos and for quick development.", 'start': 17234.861, 'duration': 9.909}, {'end': 17247.772, 'text': 'As a data scientist, I just love the package.', 'start': 17245.23, 'duration': 2.542}, {'end': 17253.798, 'text': "Now, if you're going to do something heavier lifting, there's some limitations with Anaconda and with the setup.", 'start': 17248.633, 'duration': 5.165}, {'end': 17257.141, 'text': 'In general, you can do just about anything in here with your Python.', 'start': 17254.398, 'duration': 2.743}, {'end': 17260.06, 'text': "And for this, we'll go with Jupyter Notebook.", 'start': 17258.078, 'duration': 1.982}, {'end': 17262.882, 'text': 'JupyterLab is the same as Jupyter Notebook.', 'start': 17260.82, 'duration': 2.062}, {'end': 17265.845, 'text': "You'll see they now have integration with PyCharm.", 'start': 17263.143, 'duration': 2.702}, {'end': 17271.87, 'text': "If you work in PyCharm, certainly there's a lot of other integrations that Anaconda has.", 'start': 17265.885, 'duration': 5.985}, {'end': 17279.076, 'text': "And we've opened up my Simply Learn files I work on and create a new file called Confusion Matrix Demo.", 'start': 17272.351, 'duration': 6.725}], 'summary': 'Anaconda navigator is a useful tool for data scientists, offering integrations with pycharm and jupyter notebook for python coding.', 'duration': 64.155, 'max_score': 17214.921, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU17214921.jpg'}, {'end': 17400.832, 'src': 'embed', 'start': 17366.263, 'weight': 9, 'content': [{'end': 17369.166, 'text': 'So let me go ahead and run that and bring all that information in.', 'start': 17366.263, 'duration': 2.903}, {'end': 17375.719, 'text': 'And just like we opened the file, we need to go ahead and load our data in here.', 'start': 17372.657, 'duration': 3.062}, {'end': 17379.161, 'text': "So we're going to go ahead and do our pandas read CSV.", 'start': 17376.399, 'duration': 2.762}, {'end': 17384.483, 'text': "And then just because we're in Jupyter Notebook, we can just put data to read the data in here.", 'start': 17380.041, 'duration': 4.442}, {'end': 17386.605, 'text': "A lot of times we'll actually just do this.", 'start': 17384.504, 'duration': 2.101}, {'end': 17389.666, 'text': 'I prefer to do just the head of the data or the top part.', 'start': 17386.665, 'duration': 3.001}, {'end': 17400.832, 'text': "And you can see we have age, sex, I'm not sure what CP stands for, test BPS, cholesterol, so a lot of different measurements.", 'start': 17391.107, 'duration': 9.725}], 'summary': 'Data loaded using pandas read_csv, displaying top measurements.', 'duration': 34.569, 'max_score': 17366.263, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU17366263.jpg'}, {'end': 17584.02, 'src': 'embed', 'start': 17556.058, 'weight': 0, 'content': [{'end': 17560.242, 'text': "But we're going to go ahead and take, here's our X train, X test, Y train, Y test.", 'start': 17556.058, 'duration': 4.184}, {'end': 17561.983, 'text': 'We create our scalar.', 'start': 17560.842, 'duration': 1.141}, {'end': 17563.404, 'text': 'We go ahead and scale.', 'start': 17562.223, 'duration': 1.181}, {'end': 17566.306, 'text': 'The scale is going to fit the X train.', 'start': 17564.145, 'duration': 2.161}, {'end': 17570.309, 'text': "And then we're going to go ahead and take our X train and transform it.", 'start': 17566.326, 'duration': 3.983}, {'end': 17575.894, 'text': 'And then we also need to take our X test and transform it based on the scale on here.', 'start': 17570.99, 'duration': 4.904}, {'end': 17579.537, 'text': 'So that our X is now between that nice minus 1 to 1.', 'start': 17576.014, 'duration': 3.523}, {'end': 17584.02, 'text': 'And so this is all our pre-data setup.', 'start': 17579.537, 'duration': 4.483}], 'summary': 'Data is scaled to fit between -1 to 1 for pre-data setup.', 'duration': 27.962, 'max_score': 17556.058, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU17556058.jpg'}, {'end': 17895.005, 'src': 'embed', 'start': 17868.366, 'weight': 3, 'content': [{'end': 17878.693, 'text': "our precision's at 0.83 0.87 for getting a positive and 0.83 for the negative side for a zero.", 'start': 17868.366, 'duration': 10.327}, {'end': 17883.697, 'text': 'And we start talking about whether this is a valid information or not to use.', 'start': 17879.413, 'duration': 4.284}, {'end': 17888.54, 'text': "And when we're looking at a heart attack prediction, we're only looking at one aspect.", 'start': 17884.517, 'duration': 4.023}, {'end': 17895.005, 'text': "What's the chances of this person having a heart attack or not? You might have something where we went back to the languages.", 'start': 17888.661, 'duration': 6.344}], 'summary': 'Precision at 0.83 for positive, 0.87 for negative. validity of heart attack prediction discussed.', 'duration': 26.639, 'max_score': 17868.366, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU17868366.jpg'}, {'end': 18325.428, 'src': 'embed', 'start': 18297.758, 'weight': 5, 'content': [{'end': 18301.161, 'text': 'We have to make a couple changes as we go through this first part of the import.', 'start': 18297.758, 'duration': 3.403}, {'end': 18304.044, 'text': 'The first thing we bring in is numpy as np.', 'start': 18301.361, 'duration': 2.683}, {'end': 18305.466, 'text': "That's very standard.", 'start': 18304.405, 'duration': 1.061}, {'end': 18310.772, 'text': "When we're dealing with mathematics, especially with very complicated machine learning tools,", 'start': 18305.666, 'duration': 5.106}, {'end': 18313.936, 'text': 'you almost always see the numpy come in for your numbers.', 'start': 18310.772, 'duration': 3.164}, {'end': 18315.438, 'text': "It's called number Python.", 'start': 18314.357, 'duration': 1.081}, {'end': 18316.66, 'text': 'It has your mathematics in there.', 'start': 18315.458, 'duration': 1.202}, {'end': 18318.922, 'text': 'In this case, we actually could take it out.', 'start': 18316.72, 'duration': 2.202}, {'end': 18321.746, 'text': "But generally, you'll need it for most of your different things you work with.", 'start': 18319.002, 'duration': 2.744}, {'end': 18324.007, 'text': "And then we're going to use pandas as PD.", 'start': 18322.026, 'duration': 1.981}, {'end': 18325.428, 'text': "That's also a standard.", 'start': 18324.287, 'duration': 1.141}], 'summary': 'Import numpy as np and pandas as pd for mathematical and data analysis operations.', 'duration': 27.67, 'max_score': 18297.758, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU18297758.jpg'}], 'start': 17164.707, 'title': 'Machine learning and decision trees', 'summary': 'Covers evaluating machine learning models for language classification achieving an f1 score of 0.857, data preprocessing and logistic regression modeling, heart attack prediction with 85% accuracy, and decision tree classification for animal classification and loan repayment prediction using python.', 'chapters': [{'end': 17386.605, 'start': 17164.707, 'title': 'Machine learning model evaluation', 'summary': 'Discusses evaluating machine learning models using accuracy, precision, recall, and f1 score, and demonstrates implementing a logistic regression model to analyze language classification, achieving an f1 score of 0.857 and favoring the model with higher accuracy for better english language classification.', 'duration': 221.898, 'highlights': ['The chapter discusses the F1 score formula and achieves an F1 score of 0.857 when evaluating machine learning models. The F1 score formula, 2 times precision times recall over precision plus recall, is explained, and the achieved F1 score of 0.857 is mentioned.', 'The importance of accuracy, precision, and recall in evaluating machine learning models is emphasized. The significance of accuracy, precision, and recall in assessing machine learning models is highlighted, with a preference for higher accuracy for better classification of English language speakers.', 'The process of implementing a logistic regression model for language classification is described. The process of implementing a logistic regression model for language classification, including using tools like Pandas and scikit-learn, is outlined.']}, {'end': 17674.167, 'start': 17386.665, 'title': 'Data science basics', 'summary': 'Covers data preprocessing, scaling, and logistic regression modeling, emphasizing the importance of data understanding, splitting, and scaling for accurate model predictions and evaluation.', 'duration': 287.502, 'highlights': ['The importance of understanding data measurements and domain knowledge is emphasized, highlighting the significance of data interpretation in data science. The speaker stresses the need to understand the different measurements in the data and the importance of domain knowledge for accurate interpretation in data science.', 'The process of splitting the data into training and test sets, and the significance of not scaling the test data before splitting, is explained to ensure accurate model fitting and evaluation. The speaker explains the process of splitting the data into training and test sets, emphasizing the importance of not scaling the test data before splitting to prevent altering the results.', 'The necessity of scaling data is discussed, with the speaker highlighting its impact on different models, such as linear regression and neural networks, and its role in fitting and transforming the data for accurate modeling. The speaker discusses the necessity of scaling data, emphasizing its impact on different models and its role in fitting and transforming the data for accurate modeling, particularly in neural networks.']}, {'end': 17994.966, 'start': 17674.187, 'title': 'Heart attack prediction and decision tree basics', 'summary': 'Discusses a heart attack prediction model with an 85% accuracy, evaluating the confusion matrix for true positive and false positive, and then delves into the basics of decision trees, focusing on entropy and information gain as measures of randomness and decrease in entropy after data splitting.', 'duration': 320.779, 'highlights': ["The heart attack prediction model has an accuracy of 85%, correctly identifying 25 out of 29 high-risk individuals, as indicated by the confusion matrix evaluation. The model's 85% accuracy in predicting high-risk individuals for heart conditions is substantiated by correctly identifying 25 out of 29 high-risk cases, as shown in the confusion matrix evaluation.", "The discussion progresses to evaluating the confusion matrix metrics, providing insights into precision, recall, f1 score, and support, enabling a comprehensive assessment of the model's validity and performance. Evaluating the confusion matrix metrics allows for a comprehensive assessment of the model's validity and performance, including precision, recall, f1 score, and support, offering valuable insights into the model's effectiveness.", 'The chapter then introduces the basics of decision trees, covering entropy as a measure of randomness and information gain as a measure of decrease in entropy after data splitting. The chapter introduces the basics of decision trees, explaining entropy as a measure of randomness and information gain as a measure of the decrease in entropy after data splitting, providing foundational knowledge for understanding decision tree models.']}, {'end': 18498.12, 'start': 17995.046, 'title': 'Decision tree classification', 'summary': 'Explains the working of a decision tree to classify animals based on their features, using a dataset with 3 giraffes, 2 tigers, 1 monkey, and 2 elephants, achieving an entropy of 0.571, and later applies the decision tree algorithm in python to predict loan repayment with the use of necessary packages like numpy, pandas, and decision tree classifier.', 'duration': 503.074, 'highlights': ['The chapter explains the working of a decision tree to classify animals based on their features, using a dataset with 3 giraffes, 2 tigers, 1 monkey, and 2 elephants, achieving an entropy of 0.571. It describes the process of classifying animals using a decision tree, with specific mention of the dataset containing 3 giraffes, 2 tigers, 1 monkey, and 2 elephants, and achieving an entropy of 0.571.', 'Applies the decision tree algorithm in Python to predict loan repayment with the use of necessary packages like numpy, pandas, and decision tree classifier. It demonstrates the application of the decision tree algorithm in Python to predict loan repayment, utilizing packages such as numpy, pandas, and decision tree classifier.']}], 'duration': 1333.413, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU17164707.jpg', 'highlights': ['Achieved F1 score of 0.857 when evaluating machine learning models', 'Importance of accuracy, precision, and recall in evaluating machine learning models', 'Process of implementing a logistic regression model for language classification', 'Importance of understanding data measurements and domain knowledge in data science', 'Process of splitting data into training and test sets for accurate model fitting and evaluation', 'Necessity of scaling data for accurate modeling, particularly in neural networks', 'Heart attack prediction model has an accuracy of 85%', "Evaluation of confusion matrix metrics for comprehensive assessment of model's validity and performance", 'Introduction to basics of decision trees, covering entropy and information gain', 'Explanation of decision tree working to classify animals based on their features', 'Application of decision tree algorithm in Python to predict loan repayment']}, {'end': 20915.824, 'segs': [{'end': 19079.128, 'src': 'embed', 'start': 19030.503, 'weight': 2, 'content': [{'end': 19035.028, 'text': "And we're going to use our variable CLF entropy that we created.", 'start': 19030.503, 'duration': 4.525}, {'end': 19038.03, 'text': "And then you'll see .predict.", 'start': 19036.649, 'duration': 1.381}, {'end': 19045.394, 'text': "And it's very common in the sklearn modules that their different tools have the predict when you're actually running a prediction.", 'start': 19038.33, 'duration': 7.064}, {'end': 19048.777, 'text': "In this case we're going to put our X test data in here.", 'start': 19046.155, 'duration': 2.622}, {'end': 19055.841, 'text': 'Now, if you delivered this for use an actual commercial use and distributed it.', 'start': 19050.138, 'duration': 5.703}, {'end': 19062.085, 'text': "this would be the new loans you're putting in here to guess whether the person's going to pay them back or not.", 'start': 19055.841, 'duration': 6.244}, {'end': 19070.687, 'text': 'In this case, though, we need to test out the data and just see how good our sample is, how good our tree does at predicting the loan payments.', 'start': 19062.886, 'duration': 7.801}, {'end': 19079.128, 'text': 'And finally, since Anaconda Jupyter Notebook works as a command line for Python, we can simply put the ypredicten to print it.', 'start': 19071.027, 'duration': 8.101}], 'summary': 'Using clf entropy to predict loan payments with test data in sklearn module.', 'duration': 48.625, 'max_score': 19030.503, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU19030503.jpg'}, {'end': 19947.052, 'src': 'embed', 'start': 19921.659, 'weight': 4, 'content': [{'end': 19928.522, 'text': "so we're generating a random number of between 0 and 1, and we're going to do it for each of the rows.", 'start': 19921.659, 'duration': 6.863}, {'end': 19930.363, 'text': "That's where the length df comes from.", 'start': 19928.582, 'duration': 1.781}, {'end': 19938.847, 'text': "So each row gets a generated number, and if it's less than 0.75, it's true, and if it's greater than 0.75, it's false.", 'start': 19930.563, 'duration': 8.284}, {'end': 19947.052, 'text': "This means we're going to take 75% of the data, roughly because there's a randomness involved, and we're going to use that to train it,", 'start': 19939.088, 'duration': 7.964}], 'summary': 'Generating random numbers for each row to train with 75% of the data.', 'duration': 25.393, 'max_score': 19921.659, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU19921659.jpg'}, {'end': 20309.39, 'src': 'embed', 'start': 20282.95, 'weight': 0, 'content': [{'end': 20288.775, 'text': "If you're working in a larger or big data and you need to prioritize it differently, this is what that number does.", 'start': 20282.95, 'duration': 5.825}, {'end': 20292.818, 'text': "It changes your priorities and how it's going to run across the system and things like that.", 'start': 20288.995, 'duration': 3.823}, {'end': 20295.08, 'text': 'And then the random state is just how it starts.', 'start': 20293.178, 'duration': 1.902}, {'end': 20297.141, 'text': 'Zero is fine for here.', 'start': 20295.84, 'duration': 1.301}, {'end': 20300.263, 'text': "But let's go ahead and run this.", 'start': 20298.682, 'duration': 1.581}, {'end': 20306.348, 'text': 'We also have clf.fit train features, y.', 'start': 20301.825, 'duration': 4.523}, {'end': 20309.39, 'text': "And before we run it, let's talk about this a little bit more.", 'start': 20306.348, 'duration': 3.042}], 'summary': 'Explaining the impact of changing priorities in big data processing.', 'duration': 26.44, 'max_score': 20282.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU20282950.jpg'}, {'end': 20410.536, 'src': 'embed', 'start': 20384.129, 'weight': 1, 'content': [{'end': 20389.991, 'text': 'You would really have to dig deep to find out all these different meanings of all these different settings on here.', 'start': 20384.129, 'duration': 5.862}, {'end': 20394.372, 'text': 'Some of them are self-explanatory if you kind of think about it a little bit, like max features is auto.', 'start': 20390.151, 'duration': 4.221}, {'end': 20399.193, 'text': "So all the features that we're putting in there is just going to automatically take all four of them.", 'start': 20394.512, 'duration': 4.681}, {'end': 20400.634, 'text': "whatever we send it, it'll take.", 'start': 20399.193, 'duration': 1.441}, {'end': 20404.094, 'text': "some of them might have so many features because you're processing words.", 'start': 20400.634, 'duration': 3.46}, {'end': 20410.536, 'text': "there might be like 1.4 million features in there because you're doing legal documents, and that's how many different words are in there.", 'start': 20404.094, 'duration': 6.442}], 'summary': 'Uncover various meanings of settings, like max features being auto, processing 1.4 million features for legal documents.', 'duration': 26.407, 'max_score': 20384.129, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU20384129.jpg'}], 'start': 18500.061, 'title': 'Machine learning in python', 'summary': 'Covers data exploration, decision tree training, and model accuracy with a test size of 30%, achieving an accuracy of approximately 94.6% for loan repayment prediction. it also discusses the use of a random forest classifier in banking and model testing and prediction using python.', 'chapters': [{'end': 19006.879, 'start': 18500.061, 'title': 'Data exploration and decision tree training', 'summary': "Covers data exploration of a csv file with 1000 lines and 5 columns, and then trains a decision tree model with a test size of 30%, using python's pandas and sklearn modules.", 'duration': 506.818, 'highlights': ["The CSV file contains 1000 lines of data with 5 columns, and can be explored using Python's pandas module. The file contains 1000 lines of data with 5 columns.", 'The decision tree model is trained with a test size of 30% using the sklearn module, with specified variables including random state and max depth. The decision tree model is trained with a test size of 30% and specified variables for random state and max depth.', 'The data exploration involves using pandas to print the length, shape, and the first 5 rows of the dataset, providing a clean and readable view of the data. Pandas is used to print the length, shape, and the first 5 rows of the dataset, providing a clean and readable view of the data.']}, {'end': 19219.476, 'start': 19007.159, 'title': 'Decision tree model accuracy', 'summary': 'Demonstrates the process of creating and testing a decision tree model for loan repayment prediction, achieving an accuracy of approximately 94.6% and enabling the bank to make informed loan approval decisions.', 'duration': 212.317, 'highlights': ["The model's accuracy is about 94.6%, allowing the bank to make informed decisions on loan approvals based on customer predictions.", 'The decision tree algorithm is used to predict loan repayments, demonstrating a model accuracy of about 94.6%.', 'The accuracy score of the model is 93.66667, indicating a high level of precision in predicting loan repayments.', 'The model predicts loan repayments with an accuracy of 93.6%, providing valuable insights for the bank to assess loan requests.']}, {'end': 19404.37, 'start': 19219.576, 'title': 'Random forest classifier in banking', 'summary': 'Discusses the use of a random forest classifier in banking to predict loan profitability and default rates using three decision trees to classify fruits based on features like diameter, color, and growing season, ultimately determining the majority vote.', 'duration': 184.794, 'highlights': ['The majority vote from the three decision trees in the random forest classifier determines the classification of the fruit, with two oranges and one cherry leading to an overall classification of orange.', 'The random forest classifier is effective in handling missing data, making it suitable for scenarios where certain features are unavailable, such as the color of a fruit being obscured in the image.', 'The random forest classifier in banking aids in predicting loan profitability and default rates by analyzing loan balances and the likelihood of default, contributing to smart decision-making for the bank.']}, {'end': 20139.952, 'start': 19404.69, 'title': 'Python coding: iris flower analysis', 'summary': 'Introduces the iris flower analysis using python, exploring the data, splitting it into training and testing sets, and making the data readable to humans for machine learning prediction.', 'duration': 735.262, 'highlights': ['The chapter introduces the iris flower analysis using Python. The transcript begins by introducing the iris flower analysis as a case example for Python coding.', 'Exploring the data, splitting it into training and testing sets, and making the data readable to humans for machine learning prediction. The first half of the implementation focuses on organizing and exploring the data, including loading modules into Python, importing necessary modules such as pandas and numpy, and splitting the data into training and testing sets.']}, {'end': 20432.171, 'start': 20139.972, 'title': 'Data preprocessing and model training', 'summary': 'Covers the process of converting species data into a format understandable by the computer using pd.factorize, generating an array representing different types of flowers, creating a random forest classifier with specific variables, and understanding the significance of various settings in the classifier, such as n_jobs and random state.', 'duration': 292.199, 'highlights': ['The process of converting species data into a format understandable by the computer is demonstrated using pd.factorize, resulting in an array that represents different types of flowers. The pd.factorize method is used to convert the species data into a format understandable by the computer, resulting in an array representing the three different kinds of flowers as zeros, ones, and twos.', 'Creating a random forest classifier involves setting specific variables such as n_jobs and random state, where n_jobs prioritizes the task and random state determines the starting point of the model. The creation of a random forest classifier involves setting specific variables, such as n_jobs and random state, where n_jobs prioritizes the task and random state determines the starting point of the model.', 'Understanding the significance of various settings in the classifier, such as max features and leaf nodes, is crucial for optimizing model performance and resource utilization. Understanding the significance of various settings in the classifier, such as max features and leaf nodes, is crucial for optimizing model performance and resource utilization, as these settings impact the processing of features and the number of end nodes in the model.']}, {'end': 20915.824, 'start': 20432.171, 'title': 'Model testing and prediction', 'summary': "Covers the process of testing and predicting a model using a decision tree classifier, including setting aside 25% of the data for testing, predicting flower types, generating prediction probability, mapping predictions to names, and creating a chart to evaluate the model's accuracy.", 'duration': 483.653, 'highlights': ["The chapter covers the process of testing and predicting a model using a decision tree classifier, including setting aside 25% of the data for testing, predicting flower types, generating prediction probability, mapping predictions to names, and creating a chart to evaluate the model's accuracy. testing and predicting a model, decision tree classifier, setting aside 25% of the data for testing, predicting flower types, generating prediction probability, mapping predictions to names, creating a chart to evaluate the model's accuracy", 'The model predicts 0001211222, representing the setosa, virginica, and versicolor flower types based on the test features, which include sepal length, sepal width, petal length, and petal width. predicts 0001211222, setosa, virginica, and versicolor flower types, test features include sepal length, sepal width, petal length, and petal width', "Generates a chart using crosstab to compare predicted and actual species, allowing evaluation of the model's accuracy in predicting the different flower types. chart using crosstab, compare predicted and actual species, evaluation of the model's accuracy in predicting the different flower types", 'Use of predict probability to generate three numbers for the leaf nodes, providing a detailed breakdown of the prediction probabilities for the different flower types. predict probability, generate three numbers for the leaf nodes, detailed breakdown of the prediction probabilities for the different flower types', 'Mapping predictions to names such as setosa, virginica, and versicolor, allowing for a more understandable representation of the predicted flower types. mapping predictions to names, setosa, virginica, and versicolor']}], 'duration': 2415.763, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU18500061.jpg', 'highlights': ["The model's accuracy is about 94.6%, allowing the bank to make informed decisions on loan approvals based on customer predictions.", 'The majority vote from the three decision trees in the random forest classifier determines the classification of the fruit, with two oranges and one cherry leading to an overall classification of orange.', 'The random forest classifier in banking aids in predicting loan profitability and default rates by analyzing loan balances and the likelihood of default, contributing to smart decision-making for the bank.', 'The process of converting species data into a format understandable by the computer is demonstrated using pd.factorize, resulting in an array that represents different types of flowers.', "The chapter covers the process of testing and predicting a model using a decision tree classifier, including setting aside 25% of the data for testing, predicting flower types, generating prediction probability, mapping predictions to names, and creating a chart to evaluate the model's accuracy."]}, {'end': 22813.516, 'segs': [{'end': 21244.963, 'src': 'embed', 'start': 21220.289, 'weight': 1, 'content': [{'end': 21226.312, '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': 21220.289, 'duration': 6.023}, {'end': 21231.634, 'text': 'And so if we add a new glass of wine there, red or white, we want to know what the neighbors are.', 'start': 21226.572, 'duration': 5.062}, {'end': 21234.4, 'text': "In this case, we're going to put K equals 5.", 'start': 21231.754, 'duration': 2.646}, {'end': 21235.76, 'text': "We'll talk about K in just a minute.", 'start': 21234.4, 'duration': 1.36}, {'end': 21240.222, 'text': 'A data point is classified by the majority of votes from its five nearest neighbors.', 'start': 21236, 'duration': 4.222}, {'end': 21244.963, 'text': 'Here, the unknown point would be classified as red since four out of five neighbors are red.', 'start': 21240.662, 'duration': 4.301}], 'summary': 'Knn uses 5 nearest neighbors to classify data points, with 4 out of 5 neighbors classifying an unknown point as red.', 'duration': 24.674, 'max_score': 21220.289, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU21220289.jpg'}, {'end': 21375.881, 'src': 'embed', 'start': 21335.254, 'weight': 4, 'content': [{'end': 21337.536, 'text': 'So it is a total number of values you have.', 'start': 21335.254, 'duration': 2.282}, {'end': 21338.838, 'text': 'You take the square root of it.', 'start': 21337.596, 'duration': 1.242}, {'end': 21342.261, 'text': "In most cases, you also if it's an even number.", 'start': 21339.238, 'duration': 3.023}, {'end': 21348.087, 'text': "so if you're using, like in this case, squares and triangles, if it's even, you want to make your k value odd.", 'start': 21342.261, 'duration': 5.826}, {'end': 21349.749, 'text': 'That helps it select better.', 'start': 21348.347, 'duration': 1.402}, {'end': 21354.233, 'text': "So in other words, you're not going to have a balance between two different factors that are equal.", 'start': 21349.969, 'duration': 4.264}, {'end': 21359.058, 'text': "So usually take the square root of n, and if it's even, you add one to it or subtract one from it.", 'start': 21354.393, 'duration': 4.665}, {'end': 21360.981, 'text': "And that's where you get the K value from.", 'start': 21359.438, 'duration': 1.543}, {'end': 21363.163, 'text': "That is the most common use and it's pretty solid.", 'start': 21361.061, 'duration': 2.102}, {'end': 21364.285, 'text': 'It works very well.', 'start': 21363.404, 'duration': 0.881}, {'end': 21369.152, 'text': 'When do we use KNN? We can use KNN when data is labeled.', 'start': 21364.706, 'duration': 4.446}, {'end': 21370.413, 'text': 'So you need a label on it.', 'start': 21369.492, 'duration': 0.921}, {'end': 21373.578, 'text': 'We know we have a group of pictures with dogs, dogs, cats, cats.', 'start': 21370.453, 'duration': 3.125}, {'end': 21375.881, 'text': 'Data is noise free.', 'start': 21373.838, 'duration': 2.043}], 'summary': 'Knn uses k value calculated from square root of total values, typically odd, for better selection. it works well with labeled data and noise-free data.', 'duration': 40.627, 'max_score': 21335.254, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU21335254.jpg'}, {'end': 21703.957, 'src': 'embed', 'start': 21667.629, 'weight': 7, 'content': [{'end': 21671.411, 'text': "And before that, of course, we need to discuss what IDE I'm using.", 'start': 21667.629, 'duration': 3.782}, {'end': 21674.393, 'text': 'Certainly you can use any particular editor for Python.', 'start': 21671.631, 'duration': 2.762}, {'end': 21682.457, 'text': 'But I like to use for doing very basic visual stuff the Anaconda, which is great for doing demos with the Jupyter Notebook.', 'start': 21674.533, 'duration': 7.924}, {'end': 21688.08, 'text': 'And just a quick view of the Anaconda Navigator, which is the new release out there, which is really nice.', 'start': 21682.817, 'duration': 5.263}, {'end': 21691.571, 'text': 'You can see under Home, I can choose my application.', 'start': 21688.629, 'duration': 2.942}, {'end': 21693.231, 'text': "We're going to be using Python 3.6.", 'start': 21691.591, 'duration': 1.64}, {'end': 21696.513, 'text': 'I have a couple different versions on this particular machine.', 'start': 21693.231, 'duration': 3.282}, {'end': 21701.016, 'text': 'If I go under Environments, I can create a unique environment for each one, which is nice.', 'start': 21696.733, 'duration': 4.283}, {'end': 21703.957, 'text': "And there's even a little button there where I can install different packages.", 'start': 21701.276, 'duration': 2.681}], 'summary': 'Using anaconda for python with jupyter notebook, allowing for multiple environments and package installations.', 'duration': 36.328, 'max_score': 21667.629, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU21667629.jpg'}, {'end': 22184.603, 'src': 'embed', 'start': 22142.059, 'weight': 0, 'content': [{'end': 22146.042, 'text': "And you're going to notice we did a little something here with the Pandas database code.", 'start': 22142.059, 'duration': 3.983}, {'end': 22147.683, 'text': 'There we go, my drawing tool.', 'start': 22146.442, 'duration': 1.241}, {'end': 22151.005, 'text': "We've added in this right here off the data set.", 'start': 22147.823, 'duration': 3.182}, {'end': 22156.179, 'text': 'And what this says is that the first one in Pandas, this is from the PD pandas.', 'start': 22151.165, 'duration': 5.014}, {'end': 22161.589, 'text': "It's going to say within the data set, we want to look at the eye location and it is all rows.", 'start': 22156.42, 'duration': 5.169}, {'end': 22162.39, 'text': "That's what that says.", 'start': 22161.629, 'duration': 0.761}, {'end': 22166.597, 'text': "So we're going to keep all the rows, but we're only looking at zero, column zero to eight.", 'start': 22162.43, 'duration': 4.167}, {'end': 22169.254, 'text': 'Remember column nine, Here it is right up here.', 'start': 22166.798, 'duration': 2.456}, {'end': 22170.755, 'text': 'We printed it in here as outcome.', 'start': 22169.274, 'duration': 1.481}, {'end': 22172.516, 'text': "Well, that's not part of the training data.", 'start': 22170.875, 'duration': 1.641}, {'end': 22173.536, 'text': "That's part of the answer.", 'start': 22172.556, 'duration': 0.98}, {'end': 22176.838, 'text': "Yes, column 9, but it's listed as 8, number 8.", 'start': 22173.816, 'duration': 3.022}, {'end': 22178.419, 'text': 'So 0 to 8 is 9 columns.', 'start': 22176.838, 'duration': 1.581}, {'end': 22180.1, 'text': 'So 8 is the value.', 'start': 22178.64, 'duration': 1.46}, {'end': 22184.603, 'text': 'And when you see it in here, 0, this is actually 0 to 7.', 'start': 22180.261, 'duration': 4.342}], 'summary': 'Using pandas to manipulate database code and select specific columns from a dataset.', 'duration': 42.544, 'max_score': 22142.059, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU22142059.jpg'}, {'end': 22362.476, 'src': 'embed', 'start': 22333.459, 'weight': 5, 'content': [{'end': 22335, 'text': "it's only the data going in.", 'start': 22333.459, 'duration': 1.541}, {'end': 22336.621, 'text': "That's what we want to train in there.", 'start': 22335.32, 'duration': 1.301}, {'end': 22341.644, 'text': 'Then define the model using kNeighborsClassifier and fit the trained data in the model.', 'start': 22336.821, 'duration': 4.823}, {'end': 22350.268, 'text': "So we do all that data prep and you can see down here we're only going to have a couple lines of code where we're actually building our model and training it.", 'start': 22341.904, 'duration': 8.364}, {'end': 22353.21, 'text': "That's one of the cool things about Python and how far we've come.", 'start': 22350.608, 'duration': 2.602}, {'end': 22357.192, 'text': "It's such an exciting time to be in machine learning because there's so many automated tools.", 'start': 22353.31, 'duration': 3.882}, {'end': 22362.476, 'text': "Let's see, before we do this, let's do a quick length of And let's do y.", 'start': 22357.692, 'duration': 4.784}], 'summary': 'Using kneighborsclassifier, python makes model training efficient and exciting in machine learning.', 'duration': 29.017, 'max_score': 22333.459, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU22333459.jpg'}], 'start': 20916.405, 'title': 'Machine learning techniques', 'summary': 'Covers model accuracy at 93%, k nearest neighbors algorithm fundamentals, data preprocessing with pandas and scikit-learn, model building steps, and support vector machine evaluation using confusion matrix, f1 score, and accuracy score.', 'chapters': [{'end': 21029.518, 'start': 20916.405, 'title': 'Model accuracy and scripting', 'summary': 'Discusses the model accuracy, which stands at 93%, with 30 accurate predictions out of 32, and the process of deploying the model through scripting, including mapping predictions and creating data arrays for future use.', 'duration': 113.113, 'highlights': ['The model accuracy is 93%, with 30 accurate predictions out of 32, resulting in a 93% accuracy with the model.', 'The process of deploying the model through scripting involves mapping predictions, creating data arrays for future use, and running predictions on test features.', "The need for double brackets while creating data arrays for processing more data and the ability to predict outcomes for new data, ensuring the model's applicability for unknown data."]}, {'end': 21815.112, 'start': 21029.798, 'title': 'Understanding k nearest neighbors (knn)', 'summary': 'Covers the fundamentals of k nearest neighbors (knn) algorithm used in machine learning, including its applications, selection process, and algorithmic workflow, with emphasis on parameter tuning and use cases.', 'duration': 785.314, 'highlights': ['KNN is a fundamental place to start in machine learning, used for classification and based on feature similarity. KNN serves as a foundational concept in machine learning and is predominantly used for classification tasks, relying on feature similarity to make predictions.', 'Parameter K in KNN refers to the number of nearest neighbors included in the voting process, affecting the classification of new data points. The value of parameter K in KNN impacts the classification process by determining the number of nearest neighbors considered for the voting process, thereby influencing the categorization of new data points.', 'The process of choosing the right value for K in KNN is essential for achieving better accuracy and is typically determined using parameter tuning techniques. Selecting the appropriate value for parameter K in KNN, known as parameter tuning, is crucial for enhancing accuracy and involves employing specific techniques to identify the optimal K value.', 'KNN is suitable for use when the data is labeled, noise-free, and the dataset is small, making it an ideal choice for preliminary analysis and small-scale datasets. KNN is applicable when working with labeled and noise-free data, as well as small-scale datasets, making it a suitable choice for initial analysis and smaller data samples.', 'The KNN algorithm operates by identifying the nearest neighbors of a new data point based on a similarity measure and classifying the data point based on the class of its neighbors. The KNN algorithm functions by identifying the closest neighbors of a new data point using a similarity measure and then classifying the data point based on the majority class of its neighboring data points.']}, {'end': 22088.401, 'start': 21815.112, 'title': 'Data preprocessing with pandas and scikit-learn', 'summary': 'Covers the process of importing python modules, loading a dataset using pandas, and performing data preprocessing using pandas commands and numpy functions, such as replacing zero values with numpy none and replacing missing data with the mean.', 'duration': 273.289, 'highlights': ['The process of importing Python modules and specific modules from the scikit-learn setup. The chapter begins with the import of general Python modules and specific modules from the scikit-learn setup, which are essential for the subsequent data preprocessing and model testing.', "Loading a dataset using the Pandas command and obtaining the length of the dataset. The transcript explains the usage of Pandas command 'pd.readcsv' to load the dataset and the Python command 'len' to obtain the length of the dataset, which is an initial step in the data preprocessing process.", 'Replacing zero values with numpy none and replacing missing data with the mean using Pandas and numpy functions. The chapter details the process of replacing zero values with numpy none and replacing missing data with the mean using Pandas commands and numpy functions, which is crucial for handling incomplete or incorrect data in the dataset.']}, {'end': 22437.512, 'start': 22088.8, 'title': 'Data preprocessing and model building', 'summary': 'Covers the data preprocessing steps including data inspection, splitting into training and testing data, and scaling the data, followed by building the knn model with 11 neighbors and euclidean metric.', 'duration': 348.712, 'highlights': ['The chapter covers the data preprocessing steps including data inspection, splitting into training and testing data, and scaling the data. The transcript discusses the process of preparing the data, including data inspection, splitting it into training and testing data, and scaling the data to ensure it is standardized.', 'Building the kNN model with 11 neighbors and Euclidean metric. The process of building the kNN model with 11 neighbors and using the Euclidean metric for measuring the distance is explained, emphasizing the importance of having an odd number of neighbors for voting.']}, {'end': 22813.516, 'start': 22437.692, 'title': 'Support vector machine in machine learning', 'summary': 'Covers the application of confusion matrix, f1 score, and accuracy score in machine learning model evaluation, providing insights into the performance of the model in predicting diabetes. additionally, it discusses the transformation of high-dimensional input space using a kernel function and the advantages of support vector machine in handling high-dimensional data and avoiding overfitting and bias issues.', 'duration': 375.824, 'highlights': ['The chapter covers the application of confusion matrix, F1 score, and accuracy score in machine learning model evaluation. It provides insights into the performance of the model in predicting diabetes and distinguishes between the commonly used metrics for data scientists and decision makers.', 'The transformation of high-dimensional input space using a kernel function is discussed. The kernel function transforms 1D input to a two-dimensional output, facilitating the drawing of a line between two data sets, and it enables the support vector machine to handle more complex data sets.', 'The advantages of support vector machine in handling high-dimensional data and avoiding overfitting and bias issues are explained. SVM automatically adjusts for high-dimensional space, handles sparse document vectors, and naturally avoids overfitting and bias problems, making it a powerful tool in machine learning.']}], 'duration': 1897.111, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU20916405.jpg', 'highlights': ['Model accuracy is 93%, with 30 accurate predictions out of 32.', 'KNN is fundamental in machine learning, used for classification based on feature similarity.', 'Parameter K in KNN impacts the classification process by determining the number of nearest neighbors considered for voting.', 'KNN is suitable for labeled, noise-free, and small datasets, ideal for preliminary analysis.', 'The chapter begins with importing Python modules and specific modules from the scikit-learn setup.', "Loading a dataset using the Pandas command 'pd.readcsv' and obtaining its length.", 'Replacing zero values with numpy none and missing data with the mean using Pandas and numpy functions.', 'The chapter covers data preprocessing steps including inspection, splitting, and scaling.', 'Building the kNN model with 11 neighbors and Euclidean metric is explained.', 'The chapter covers the application of confusion matrix, F1 score, and accuracy score in model evaluation.', 'The transformation of high-dimensional input space using a kernel function is discussed.', 'Advantages of support vector machine in handling high-dimensional data and avoiding overfitting and bias issues are explained.']}, {'end': 25531.651, 'segs': [{'end': 23779.035, 'src': 'embed', 'start': 23744.654, 'weight': 0, 'content': [{'end': 23748.539, 'text': 'Of course, these are pretend data for our crocodiles and alligators.', 'start': 23744.654, 'duration': 3.885}, {'end': 23753.805, 'text': 'But this hands-on example will help you to encounter any support vector machine projects in the future.', 'start': 23748.759, 'duration': 5.046}, {'end': 23757.389, 'text': 'And you can see how easy they are to set up and look at in depth.', 'start': 23753.985, 'duration': 3.404}, {'end': 23760.193, 'text': 'Regularization in machine learning.', 'start': 23757.61, 'duration': 2.583}, {'end': 23770.111, 'text': 'So our agenda on this one is fitting the data, understanding linear regression, bias and variance.', 'start': 23762.048, 'duration': 8.063}, {'end': 23779.035, 'text': 'What is overfitting? What is underfitting? And those are like the biggest things right now in data science is overfitting and underfitting.', 'start': 23770.872, 'duration': 8.163}], 'summary': 'Hands-on example with pretend data to understand support vector machines, and to explore fitting data, linear regression, bias, variance, overfitting, and underfitting in machine learning.', 'duration': 34.381, 'max_score': 23744.654, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU23744654.jpg'}, {'end': 23820.649, 'src': 'embed', 'start': 23792.746, 'weight': 2, 'content': [{'end': 23800.212, 'text': "It's a process of plotting a series of data points and drawing the best fit line to understand the relationship between the variables.", 'start': 23792.746, 'duration': 7.466}, {'end': 23802.633, 'text': 'And this is what we called data fitting.', 'start': 23800.632, 'duration': 2.001}, {'end': 23806.396, 'text': "And you can see here, we have a couple of lines we've drawn on this graph.", 'start': 23803.174, 'duration': 3.222}, {'end': 23808.178, 'text': "We're going to go in a little deeper on there.", 'start': 23806.837, 'duration': 1.341}, {'end': 23811.34, 'text': 'So we might have in this case, just a two dimensions.', 'start': 23808.798, 'duration': 2.542}, {'end': 23814.783, 'text': 'We have an efficiency of the car and we have the distance traveled in 1000 kilometers.', 'start': 23811.36, 'duration': 3.423}, {'end': 23820.649, 'text': "And so what is data fitting? Well, it's a linear relationship.", 'start': 23817.748, 'duration': 2.901}], 'summary': 'Data fitting involves plotting data points and drawing best fit lines to understand linear relationships between variables.', 'duration': 27.903, 'max_score': 23792.746, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU23792746.jpg'}, {'end': 24837.545, 'src': 'embed', 'start': 24800.2, 'weight': 3, 'content': [{'end': 24802.523, 'text': 'In comparing the two models with all the data points,', 'start': 24800.2, 'duration': 2.323}, {'end': 24807.249, 'text': 'we can see that the lasso regression line fits the model more accurately than the linear regression line.', 'start': 24802.523, 'duration': 4.726}, {'end': 24812.895, 'text': 'And this is, like I said, I use these two models a lot.', 'start': 24809.454, 'duration': 3.441}, {'end': 24817.617, 'text': 'The ridge, and this is important, this is kind of the meat of the matter.', 'start': 24813.095, 'duration': 4.522}, {'end': 24823.8, 'text': 'How do you know which one to use? Some of it is you just do it a bunch of times and then you figure it out.', 'start': 24818.578, 'duration': 5.222}, {'end': 24830.082, 'text': 'Ridge regularization is useful when we have many variables with relatively smaller data samples.', 'start': 24823.98, 'duration': 6.102}, {'end': 24837.545, 'text': 'The model does not encourage convergence towards zero, but is likely to make them closer to zero and prevent overfitting.', 'start': 24830.782, 'duration': 6.763}], 'summary': 'Lasso regression fits more accurately than linear regression. ridge regularization is useful for many variables with smaller data samples.', 'duration': 37.345, 'max_score': 24800.2, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU24800200.jpg'}], 'start': 22813.816, 'title': 'Introduction to machine learning and linear regression', 'summary': 'Introduces the process of setting up a machine learning algorithm and creating an svm, demonstrates the implementation of an svm model for separating crocodile and alligator data, explains linear regression, mean square error, bias, variance, overfitting, and underfitting, explores regularization in linear regression with ridge and lasso models, and discusses linear regression, dimensionality reduction, and its application in reducing the number of input variables.', 'chapters': [{'end': 22864.528, 'start': 22813.816, 'title': 'Introduction to machine learning with python', 'summary': 'Introduces the process of setting up a machine learning algorithm and creating svm with only two lines of code, while emphasizing the use of imagery and the quick process of data creation.', 'duration': 50.712, 'highlights': ['The chapter emphasizes the quick and efficient process of setting up a machine learning algorithm, with the creation of SVM requiring only two lines of code.', 'It discusses the common use of imagery in machine learning algorithms and the process of creating data for analysis.', 'The chapter mentions the correction of two blobs and the swift nature of the data creation process.', "Anecdote about the speaker's father is briefly mentioned at the beginning."]}, {'end': 23792.425, 'start': 22864.789, 'title': 'Anaconda jupyter setup for svm model', 'summary': 'Demonstrates the setup and implementation of an svm model for separating crocodile and alligator data using anaconda jupyter notebook, python 3, numpy, matplotlibrary, and sklearn, resulting in a successful separation of the two groups with a linear hyperplane.', 'duration': 927.636, 'highlights': ['The chapter demonstrates the setup and implementation of an SVM model for separating crocodile and alligator data using Anaconda Jupyter Notebook, Python 3, numpy, matplotlibrary, and sklearn, resulting in a successful separation of the two groups with a linear hyperplane. Demonstrates the setup and implementation of an SVM model using Anaconda Jupyter Notebook, Python 3, numpy, matplotlibrary, and sklearn; Successfully separates crocodile and alligator data with a linear hyperplane.', 'The Anaconda Jupyter Notebook with Python 3 is used for the setup, and the numpy and matplotlibrary libraries are imported for data manipulation and visualization. Uses Anaconda Jupyter Notebook with Python 3 for the setup; Imports numpy and matplotlibrary libraries for data manipulation and visualization.', 'The sklearn.datasets.samples generator, Make Blobs, is used to create 40 lines of data with two centers representing crocodiles and alligators. Uses sklearn.datasets.samples generator, Make Blobs, to create 40 lines of data with two centers representing crocodiles and alligators.', 'The SVM model is implemented using the sklearn library, resulting in a successful separation of the two groups with a linear hyperplane. Implements the SVM model using the sklearn library; Successfully separates the two groups with a linear hyperplane.', 'The implementation of the SVM model allows for successful prediction of new data points, accurately identifying whether they belong to the crocodile or alligator group. Allows for successful prediction of new data points; Accurately identifies whether they belong to the crocodile or alligator group.']}, {'end': 24494.867, 'start': 23792.746, 'title': 'Understanding linear regression and model fitting', 'summary': 'Explains the process of data fitting, linear regression, mean square error, bias, variance, overfitting, and underfitting in machine learning, emphasizing the importance of model accuracy and data cleaning.', 'duration': 702.121, 'highlights': ['The process of data fitting and linear regression is explained, emphasizing the relationship between variables and the use of straight lines or planes to represent the data. It explains the process of data fitting and linear regression, highlighting the use of straight lines or planes to represent the relationship between variables.', 'The concept of mean square error (MSE) is detailed, demonstrating its use in evaluating the best fit line and comparing the loss function between different models. It details the concept of mean square error (MSE) and its use in evaluating the best fit line and comparing the loss function between different models.', 'The chapter discusses bias and variance in machine learning, explaining their impact on model flexibility and sensitivity to specific data sets. It discusses bias and variance, explaining their impact on model flexibility and sensitivity to specific data sets in machine learning.', 'Overfitting is defined as a scenario where the model attempts to fit each data point, leading to high error rates on new data points, with reasons including unclean training data and model complexity. It defines overfitting as a scenario where the model attempts to fit each data point, leading to high error rates on new data points, with reasons including unclean training data and model complexity.', 'Underfitting is explained as a scenario where the model fails to learn patterns and accepts every new data point, with similar reasons as overfitting for unclean training data and model bias. It explains underfitting as a scenario where the model fails to learn patterns and accepts every new data point, with similar reasons as overfitting for unclean training data and model bias.']}, {'end': 24885.831, 'start': 24495.447, 'title': 'Regularization in linear regression', 'summary': 'Explores the concept of regularization in linear regression, discussing the importance of finding the right fit for the data and comparing ridge and lasso regularization models with examples, demonstrating their impact on model accuracy and suitability for different data scenarios.', 'duration': 390.384, 'highlights': ['Ridge regularization modifies overfitted or underfitted models by adding a penalty equivalent to the sum of the squares of the magnitude of the coefficients, resulting in a more accurate fit for the model. Ridge regularization modifies overfitted or underfitted models by adding a penalty equivalent to the sum of the squares of the magnitude of the coefficients, resulting in a more accurate fit for the model.', 'Lasso regularization is preferred when fitting a linear model with fewer variables, encouraging coefficients of the variables to go towards zero and is suitable for scenarios with fewer variables. Lasso regularization is preferred when fitting a linear model with fewer variables, encouraging coefficients of the variables to go towards zero and is suitable for scenarios with fewer variables.', 'The chapter discusses the importance of finding the right fit for the data, emphasizing the need for a linear curve that best fits the data and explains the concepts of overfitting, underfitting, and a good fit. The chapter discusses the importance of finding the right fit for the data, emphasizing the need for a linear curve that best fits the data and explains the concepts of overfitting, underfitting, and a good fit.']}, {'end': 25531.651, 'start': 24885.871, 'title': 'Linear regression and dimensionality reduction', 'summary': 'Discusses the implementation of linear regression and dimensionality reduction techniques using python and various libraries. it covers the process of loading, analyzing, and splitting the boston dataset, applying multiple linear regression, and comparing it with ridge and lasso regression models. it also touches on the concept of dimensionality reduction and its application in reducing the number of input variables in a dataset.', 'duration': 645.78, 'highlights': ['The chapter walks through the process of loading, analyzing, and splitting the Boston dataset, followed by applying multiple linear regression and comparing it with ridge and lasso regression models. loading, analyzing, and splitting the Boston dataset, applying multiple linear regression, comparing with ridge and lasso regression models', 'It discusses the concept of dimensionality reduction, a technique that reduces the number of input variables in a dataset. concept of dimensionality reduction, reducing the number of input variables']}], 'duration': 2717.835, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU22813816.jpg', 'highlights': ['The chapter emphasizes the quick and efficient process of setting up a machine learning algorithm, with the creation of SVM requiring only two lines of code.', 'The chapter demonstrates the setup and implementation of an SVM model for separating crocodile and alligator data using Anaconda Jupyter Notebook, Python 3, numpy, matplotlibrary, and sklearn, resulting in a successful separation of the two groups with a linear hyperplane.', 'The process of data fitting and linear regression is explained, emphasizing the relationship between variables and the use of straight lines or planes to represent the data.', 'Ridge regularization modifies overfitted or underfitted models by adding a penalty equivalent to the sum of the squares of the magnitude of the coefficients, resulting in a more accurate fit for the model.', 'The chapter walks through the process of loading, analyzing, and splitting the Boston dataset, followed by applying multiple linear regression and comparing it with ridge and lasso regression models.']}, {'end': 27304.854, 'segs': [{'end': 25602.804, 'src': 'embed', 'start': 25574.047, 'weight': 1, 'content': [{'end': 25575.308, 'text': 'What can we do to clean it up?', 'start': 25574.047, 'duration': 1.261}, {'end': 25577.59, 'text': 'Why dimensionality reduction??', 'start': 25575.989, 'duration': 1.601}, {'end': 25583.574, 'text': 'Well, number one less dimensions for a given data set means less computation or training time.', 'start': 25578.391, 'duration': 5.183}, {'end': 25591.278, 'text': "That can be really important if you're trying a number of different models and you're rerunning them over and over again.", 'start': 25584.414, 'duration': 6.864}, {'end': 25596.481, 'text': 'And even if you have seven gigabytes of data, that can start taking days to go through all those different models.', 'start': 25591.338, 'duration': 5.143}, {'end': 25598.182, 'text': 'So this is huge.', 'start': 25597.341, 'duration': 0.841}, {'end': 25602.804, 'text': 'This is probably the hugest part as far as reducing our data set.', 'start': 25598.222, 'duration': 4.582}], 'summary': 'Dimensionality reduction reduces computation time, crucial for large data sets.', 'duration': 28.757, 'max_score': 25574.047, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU25574047.jpg'}, {'end': 25694.388, 'src': 'embed', 'start': 25667.258, 'weight': 0, 'content': [{'end': 25670.141, 'text': 'That kind of goes with number four, what makes data easy for plotting.', 'start': 25667.258, 'duration': 2.883}, {'end': 25672.464, 'text': "You have a better interpretation when we're looking at it.", 'start': 25670.301, 'duration': 2.163}, {'end': 25674.907, 'text': 'Principal component analysis.', 'start': 25673.064, 'duration': 1.843}, {'end': 25676.168, 'text': 'So what is it?', 'start': 25675.567, 'duration': 0.601}, {'end': 25685.218, 'text': 'Principal component analysis is a technique for reducing the dimensionality of data sets, increasing interpretability but, at the same time,', 'start': 25677.35, 'duration': 7.868}, {'end': 25687.14, 'text': 'minimizing information loss.', 'start': 25685.218, 'duration': 1.922}, {'end': 25694.388, 'text': 'So we take some very complex data set with lots of variables, we run it through the PCA, we reduce the variables.', 'start': 25687.701, 'duration': 6.687}], 'summary': 'Principal component analysis reduces data dimensionality, increasing interpretability while minimizing information loss.', 'duration': 27.13, 'max_score': 25667.258, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU25667258.jpg'}, {'end': 25834.626, 'src': 'embed', 'start': 25792.352, 'weight': 3, 'content': [{'end': 25798.177, 'text': "That's the kind of idea that's going on into this in preprocessing and looking at what we can do to bring the data down.", 'start': 25792.352, 'duration': 5.825}, {'end': 25803.541, 'text': 'A very simplified example on my iris petal example.', 'start': 25798.817, 'duration': 4.724}, {'end': 25810.828, 'text': 'When we look at the similarity in PCA, we find the best picture or projection of the data points.', 'start': 25804.422, 'duration': 6.406}, {'end': 25819.453, 'text': "And so we look down at from one angle, we've drawn a line down there, we can see these data points based on, in this case, just two variables.", 'start': 25811.946, 'duration': 7.507}, {'end': 25824.137, 'text': "Now keep in mind, we're usually talking about 36, 40 variables.", 'start': 25819.713, 'duration': 4.424}, {'end': 25832.063, 'text': "Almost all of your business models usually have about 26 to 27 different variables they're looking at.", 'start': 25824.717, 'duration': 7.346}, {'end': 25834.626, 'text': 'Same thing with like a bank loan model.', 'start': 25832.884, 'duration': 1.742}], 'summary': 'Using pca to reduce data dimensions, typically from 36-40 to 26-27 variables in business models like bank loan predictions.', 'duration': 42.274, 'max_score': 25792.352, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU25792352.jpg'}, {'end': 26138.686, 'src': 'embed', 'start': 26110.934, 'weight': 5, 'content': [{'end': 26113.975, 'text': 'Then we have a covariance matrix computation.', 'start': 26110.934, 'duration': 3.041}, {'end': 26121.896, 'text': 'And we use that to generate our eigenvectors and eigenvalues, which is the feature vector.', 'start': 26115.055, 'duration': 6.841}, {'end': 26131.878, 'text': 'And if you remember, the eigenvector is like a translation for moving the data from x equals 1 to x equals 2 or whatever, altering it.', 'start': 26122.396, 'duration': 9.482}, {'end': 26134.959, 'text': 'And the eigenvalue is the final value that we generate.', 'start': 26132.278, 'duration': 2.681}, {'end': 26138.686, 'text': 'When we talk about standardization.', 'start': 26136.806, 'duration': 1.88}], 'summary': 'Covariance matrix used to generate eigenvectors and eigenvalues for feature vectors.', 'duration': 27.752, 'max_score': 26110.934, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU26110934.jpg'}, {'end': 26525.121, 'src': 'embed', 'start': 26497.594, 'weight': 7, 'content': [{'end': 26503.715, 'text': "And in this case, we're going to pull in, certainly you can have lots of fun with different data, but we're going to use the cancer data set.", 'start': 26497.594, 'duration': 6.121}, {'end': 26508.737, 'text': 'And one of the reasons the cancer data set is, is it has like 36, 35 different features.', 'start': 26504.536, 'duration': 4.201}, {'end': 26512.258, 'text': "So it's kind of fun to use that as our base for this.", 'start': 26509.637, 'duration': 2.621}, {'end': 26515.478, 'text': "And we'll go ahead and run this and look at our keys.", 'start': 26513.158, 'duration': 2.32}, {'end': 26525.121, 'text': 'And the first thing we notice in our keys for the cancer data set is we have our data, we have our target, our frame, target names, description,', 'start': 26516.659, 'duration': 8.462}], 'summary': 'Using the cancer dataset with 35 features to run analysis.', 'duration': 27.527, 'max_score': 26497.594, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU26497594.jpg'}, {'end': 26727.767, 'src': 'embed', 'start': 26696.434, 'weight': 6, 'content': [{'end': 26699.396, 'text': 'And now we actually are getting into the PCA side of it.', 'start': 26696.434, 'duration': 2.962}, {'end': 26703.238, 'text': "As we've noticed before, it is difficult to visualize high dimensional data.", 'start': 26699.756, 'duration': 3.482}, {'end': 26712.723, 'text': 'We can use PCA to find the first two principal components and visualize the data, this new two dimensional space, with a single scatter plot.', 'start': 26703.958, 'duration': 8.765}, {'end': 26715.805, 'text': 'Before we do this, we need to go ahead and scale our data.', 'start': 26713.604, 'duration': 2.201}, {'end': 26727.767, 'text': "Now, I haven't run this to see if you really have to scale the data on this, but as just a general run time.", 'start': 26717.666, 'duration': 10.101}], 'summary': 'Using pca to visualize high-dimensional data in a two-dimensional space for improved visualization.', 'duration': 31.333, 'max_score': 26696.434, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU26696434.jpg'}, {'end': 26831.745, 'src': 'embed', 'start': 26801.294, 'weight': 8, 'content': [{'end': 26805.896, 'text': "I would never go over four components, especially if you're going to demo this with somebody else.", 'start': 26801.294, 'duration': 4.602}, {'end': 26811.398, 'text': "If you're showing this to the shareholders, The whole idea is to reduce it to something people can see.", 'start': 26806.436, 'duration': 4.962}, {'end': 26823.982, 'text': "And then the PCA fit is going to take the scaled data that we generated up here and then you can see we've created our PCA model with in components equals two.", 'start': 26813.419, 'duration': 10.563}, {'end': 26831.745, 'text': "Now, whenever I use a new tool, I like to go in there and actually see what I'm using.", 'start': 26826.123, 'duration': 5.622}], 'summary': 'Limit to four components for demo, reduce to visual data, pca model with two components.', 'duration': 30.451, 'max_score': 26801.294, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU26801294.jpg'}, {'end': 26993.227, 'src': 'embed', 'start': 26962.139, 'weight': 9, 'content': [{'end': 26965.321, 'text': "I think I said 36 or something like that, but it's 30.", 'start': 26962.139, 'duration': 3.182}, {'end': 26967.542, 'text': "And we've compressed it down to two features.", 'start': 26965.321, 'duration': 2.221}, {'end': 26969.763, 'text': 'And we decided we wanted two features.', 'start': 26967.922, 'duration': 1.841}, {'end': 26970.863, 'text': "And that's where this comes from.", 'start': 26969.783, 'duration': 1.08}, {'end': 26974.225, 'text': 'We still have 569 data sets.', 'start': 26971.783, 'duration': 2.442}, {'end': 26977.126, 'text': 'I mean data rows, not data sets.', 'start': 26975.185, 'duration': 1.941}, {'end': 26985.217, 'text': "We still have 569 rows of data, but instead of computing 30 features, we're now only doing our model on two features.", 'start': 26977.728, 'duration': 7.489}, {'end': 26993.227, 'text': "So let's go ahead and plot these and take a look and see what's going on.", 'start': 26988.802, 'duration': 4.425}], 'summary': 'The data has been reduced from 30 to 2 features, with 569 data rows remaining.', 'duration': 31.088, 'max_score': 26962.139, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU26962139.jpg'}, {'end': 27070.781, 'src': 'embed', 'start': 27043.524, 'weight': 2, 'content': [{'end': 27051.089, 'text': 'And you can see here, instead of having a chart, one of those heat maps with 30 different columns in it.', 'start': 27043.524, 'duration': 7.565}, {'end': 27058.153, 'text': 'We can look at this and say hey, this one actually did a pretty good job of separating the data.', 'start': 27051.869, 'duration': 6.284}, {'end': 27061.335, 'text': 'And a couple of things.', 'start': 27060.094, 'duration': 1.241}, {'end': 27070.781, 'text': "when I'm looking at this, that I notice is first, we have a very clear area where it's clumped together, where it's going to be benign.", 'start': 27061.335, 'duration': 9.446}], 'summary': 'Heat map with 30 columns shows clear separation of benign data.', 'duration': 27.257, 'max_score': 27043.524, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU27043524.jpg'}, {'end': 27290.021, 'src': 'embed', 'start': 27256.009, 'weight': 11, 'content': [{'end': 27258.13, 'text': 'On the right, we have a scale.', 'start': 27256.009, 'duration': 2.121}, {'end': 27264.913, 'text': "So we can see we have the dark colors all the way to the really light colors, which are what's really shining there.", 'start': 27258.35, 'duration': 6.563}, {'end': 27267.074, 'text': 'This is like the primary stuff we want to look at.', 'start': 27264.953, 'duration': 2.121}, {'end': 27276.818, 'text': 'So this heat map and the color bar basically represent the correlation between the various features and the principal component itself.', 'start': 27269.315, 'duration': 7.503}, {'end': 27280.874, 'text': 'So, you know, very powerful map to look at.', 'start': 27278.672, 'duration': 2.202}, {'end': 27290.021, 'text': 'And then you can go in here and we might notice that the mean radius, look how on the bottom of the map it is on some of this.', 'start': 27281.074, 'duration': 8.947}], 'summary': 'Heat map shows correlation between features and principal component.', 'duration': 34.012, 'max_score': 27256.009, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU27256009.jpg'}], 'start': 25532.171, 'title': 'Using pca for dimensionality reduction', 'summary': 'Focuses on the significance of dimensionality reduction using pca in data analysis, covering the benefits such as computational efficiency and improved human understanding. it emphasizes understanding pca, data processing, jupyter setup, and cancer data visualization for effective presentation.', 'chapters': [{'end': 25748.636, 'start': 25532.171, 'title': 'Dimensionality reduction for data analysis', 'summary': 'Highlights the importance of dimensionality reduction in data analysis, emphasizing the benefits of reducing dimensions for computational efficiency, removing redundancy, and simplifying data interpretation, ultimately leading to better human understanding and significant feature identification, as demonstrated through principal component analysis.', 'duration': 216.465, 'highlights': ['Dimensionality reduction leads to less computation or training time, especially crucial when dealing with large data sets, such as reducing training time from days to hours for a seven-gigabyte data set. Less computation or training time, significant impact on large data sets', 'Removal of redundancy after eliminating similar entries from the data set, thereby enhancing the efficiency of models like neural networks and reducing the space required to store data. Enhanced model efficiency, reduced storage space', 'Simplified data facilitates easy plotting in 2D and 3D, aiding in clear and simplified data presentation for better human interpretation and identification of significant features. Easy plotting, improved human interpretation, identification of significant features', 'Principal Component Analysis (PCA) is introduced as a technique for reducing the dimensionality of data sets, emphasizing its role in increasing interpretability while minimizing information loss. Role of PCA in increasing interpretability, minimizing information loss']}, {'end': 26420.7, 'start': 25749.056, 'title': 'Understanding pca and data processing', 'summary': 'Explains the importance of using mathematical formulas for consistent and meaningful data analysis, illustrating the use of pca to simplify and visualize high-dimensional data and highlighting key terminologies and operations in pca, including standardization, covariance matrix computation, eigenvectors, eigenvalues, and feature vectors.', 'duration': 671.644, 'highlights': ['PCA simplifies and visualizes high-dimensional data by finding the best projection of data points, with a simplified example using iris petal measurements to demonstrate the concept of creating a single number ratio from multiple variables. PCA simplifies and visualizes high-dimensional data; example using iris petal measurements; creating a single number ratio from multiple variables.', 'Business models typically consider 26 to 27 different variables, and PCA aims to find the best view for data points, reducing complexity and aiding in understanding through perspectives and terminologies. Business models consider 26 to 27 different variables; PCA aims to find the best view for data points.', 'PCA operations involve standardization, covariance matrix computation, eigenvectors, eigenvalues, and feature vectors, each playing a crucial role in evaluating principal components for a given data set. PCA operations involve standardization, covariance matrix computation, eigenvectors, eigenvalues, and feature vectors.']}, {'end': 26924.873, 'start': 26421.36, 'title': 'Setting up jupyter and pca for cancer data', 'summary': 'Covers setting up jupyter with python 3.6, loading and visualizing the cancer dataset, and utilizing pca to find the first two principal components for visualization, with a focus on scaling the data and reducing dimensions for effective presentation.', 'duration': 503.513, 'highlights': ['Utilizing PCA to find the first two principal components for visualization The chapter focuses on using PCA to find the first two principal components for visualization, which is essential for visualizing high-dimensional data in a two-dimensional space, with a single scatter plot.', 'Loading and visualizing the cancer dataset The chapter discusses loading and visualizing the cancer dataset, which contains 36, 35 different features, making it an intriguing base for analysis and exploration.', 'Setting up Jupyter with Python 3.6 The transcript emphasizes the use of Python 3.6 for stability, especially with neural networks, and the creation of a new Python 3 environment in Jupyter for ease of use.', 'Scaling the data and reducing dimensions for effective presentation The importance of scaling the data and reducing dimensions for effective presentation using PCA is highlighted, with a recommendation to keep the components to a maximum of four for better visualization and understanding.']}, {'end': 27304.854, 'start': 26927.374, 'title': 'Pca transformation and interpretation', 'summary': 'Discusses the transformation of 30 features into 2 principal components, effectively reducing dimensionality, and the visualization and interpretation of the resulting components and their impact on cancer classification.', 'duration': 377.48, 'highlights': ['The data with 30 features is transformed into two principal components, reducing dimensionality for modeling. The chapter discusses the transformation of 30 features into 2 principal components, effectively reducing dimensionality for modeling from 30 to 2 features.', 'The scatter plot of the transformed data shows clear separation of benign and malignant classes, aiding in cancer classification. The scatter plot of the transformed data demonstrates clear separation of benign and malignant classes, aiding in cancer classification.', 'The PCA components and their weights are used to create a heatmap, revealing correlations between original features and principal components. The PCA components and their weights are used to create a heatmap, revealing correlations between original features and principal components, aiding in interpretation and understanding of the components.']}], 'duration': 1772.683, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU25532171.jpg', 'highlights': ['Role of PCA in increasing interpretability, minimizing information loss', 'Enhanced model efficiency, reduced storage space', 'Easy plotting, improved human interpretation, identification of significant features', 'PCA simplifies and visualizes high-dimensional data; example using iris petal measurements; creating a single number ratio from multiple variables', 'Business models consider 26 to 27 different variables; PCA aims to find the best view for data points', 'PCA operations involve standardization, covariance matrix computation, eigenvectors, eigenvalues, and feature vectors', 'The chapter focuses on using PCA to find the first two principal components for visualization, which is essential for visualizing high-dimensional data in a two-dimensional space, with a single scatter plot', 'Loading and visualizing the cancer dataset, which contains 36, 35 different features, making it an intriguing base for analysis and exploration', 'The importance of scaling the data and reducing dimensions for effective presentation using PCA is highlighted, with a recommendation to keep the components to a maximum of four for better visualization and understanding', 'The chapter discusses the transformation of 30 features into 2 principal components, effectively reducing dimensionality for modeling from 30 to 2 features', 'The scatter plot of the transformed data demonstrates clear separation of benign and malignant classes, aiding in cancer classification', 'The PCA components and their weights are used to create a heatmap, revealing correlations between original features and principal components, aiding in interpretation and understanding of the components']}, {'end': 29595.084, 'segs': [{'end': 27337.678, 'src': 'embed', 'start': 27305.41, 'weight': 0, 'content': [{'end': 27307.652, 'text': 'Today, we have a really interesting topic for you.', 'start': 27305.41, 'duration': 2.242}, {'end': 27314.056, 'text': "In this video, we'll be analyzing the upcoming United States presidential election using Twitter sentiment analysis in Python.", 'start': 27307.752, 'duration': 6.304}, {'end': 27331.817, 'text': 'In the course of this session, We will understand in brief about the upcoming election,', 'start': 27314.116, 'duration': 17.701}, {'end': 27337.678, 'text': 'the candidates fighting the 2020 US election and throw light on the result of the 2016 US election.', 'start': 27331.817, 'duration': 5.861}], 'summary': 'Analysis of upcoming us presidential election using twitter sentiment in python.', 'duration': 32.268, 'max_score': 27305.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU27305410.jpg'}, {'end': 27385.094, 'src': 'embed', 'start': 27358.769, 'weight': 3, 'content': [{'end': 27363.23, 'text': 'The analysis is based on what people have tweeted and we will understand the mood and sentiment of the public.', 'start': 27358.769, 'duration': 4.461}, {'end': 27370.953, 'text': 'The main objective of this video is to help you understand how to extract tweets from Twitter handles using Python libraries,', 'start': 27364.711, 'duration': 6.242}, {'end': 27373.833, 'text': 'store it in a CSV file and perform textual analysis.', 'start': 27370.953, 'duration': 2.88}, {'end': 27377.474, 'text': 'The results obtained are purely based on the data that we have collected.', 'start': 27374.574, 'duration': 2.9}, {'end': 27378.995, 'text': "Let's begin.", 'start': 27378.495, 'duration': 0.5}, {'end': 27385.094, 'text': 'The battle for the 2020 U.S. presidential election has begun,', 'start': 27381.753, 'duration': 3.341}], 'summary': 'An analysis of public sentiment on twitter for the 2020 u.s. presidential election using python libraries.', 'duration': 26.325, 'max_score': 27358.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU27358769.jpg'}, {'end': 27535.408, 'src': 'embed', 'start': 27508.812, 'weight': 1, 'content': [{'end': 27514.702, 'text': 'At this moment, Joe Biden is favored to win the election and he leads in both national and state polls.', 'start': 27508.812, 'duration': 5.89}, {'end': 27520.633, 'text': 'The Economist is analyzing polling, economic and demographic data to predict the US elections.', 'start': 27515.444, 'duration': 5.189}, {'end': 27526.061, 'text': 'Their model thinks Joe Biden is very likely to beat Donald Trump in the electoral college.', 'start': 27521.457, 'duration': 4.604}, {'end': 27535.408, 'text': 'As per their forecast, Joe Biden has 96% chance of winning the electoral college and this 99% chance of Biden winning the most votes.', 'start': 27526.581, 'duration': 8.827}], 'summary': 'Joe biden favored to win election with 96% chance of winning electoral college and 99% chance of most votes.', 'duration': 26.596, 'max_score': 27508.812, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU27508812.jpg'}, {'end': 27768.247, 'src': 'embed', 'start': 27742.636, 'weight': 2, 'content': [{'end': 27748.839, 'text': 'then we have text blob and word cloud to get the sentiment and to build the word cloud.', 'start': 27742.636, 'duration': 6.203}, {'end': 27755.036, 'text': 'and finally we are importing the plotly library for creating graphs.', 'start': 27749.992, 'duration': 5.044}, {'end': 27757.038, 'text': 'Let me run this cell.', 'start': 27755.036, 'duration': 2.002}, {'end': 27764.964, 'text': 'In the current cell we will import the datasets using read underscore csv function present in the pandas library.', 'start': 27757.038, 'duration': 7.926}, {'end': 27768.247, 'text': 'So I have passed in my location where the data file is.', 'start': 27764.964, 'duration': 3.283}], 'summary': 'Using text blob and word cloud for sentiment analysis, importing plotly library for graphs, and importing datasets using pandas read_csv function.', 'duration': 25.611, 'max_score': 27742.636, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU27742636.jpg'}, {'end': 29124.654, 'src': 'embed', 'start': 29100.051, 'weight': 6, 'content': [{'end': 29107.219, 'text': 'Some of the most popular machine learning algorithms are linear regression, logistic regression, support vector machines, k-nearest neighbors,', 'start': 29100.051, 'duration': 7.168}, {'end': 29109.502, 'text': 'k-means clustering, Naive Bayes and others.', 'start': 29107.219, 'duration': 2.283}, {'end': 29118.292, 'text': 'Some of the top companies hiring for machine learning roles are Microsoft, Spotify, Google, Cell, Ericsson, Oracle and Walmart.', 'start': 29110.323, 'duration': 7.969}, {'end': 29124.654, 'text': 'There are numerous product-based, service-based as well as startup companies that are hiring for machine learning positions.', 'start': 29119.431, 'duration': 5.223}], 'summary': 'Popular machine learning algorithms include linear regression, logistic regression, svm, k-nn, k-means clustering, naive bayes. top companies hiring for ml roles: microsoft, spotify, google, cell, ericsson, oracle, walmart. many product-based, service-based, and startup companies are also hiring for ml positions.', 'duration': 24.603, 'max_score': 29100.051, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU29100051.jpg'}, {'end': 29160.452, 'src': 'embed', 'start': 29135.3, 'weight': 5, 'content': [{'end': 29140.458, 'text': "let's look at the important skills you need to know to become a machine learning expert in 2021..", 'start': 29135.3, 'duration': 5.158}, {'end': 29141.902, 'text': 'First, we have programming.', 'start': 29140.458, 'duration': 1.444}, {'end': 29149.139, 'text': 'Machine learning mostly depends on algorithms, which means one should possess sound knowledge of different programming languages such as Python and R.', 'start': 29142.503, 'duration': 6.636}, {'end': 29154.226, 'text': 'should have knowledge of basic programming concepts and understand data structures.', 'start': 29150.302, 'duration': 3.924}, {'end': 29156.508, 'text': 'this will help you write better and efficient codes.', 'start': 29154.226, 'duration': 2.282}, {'end': 29160.452, 'text': 'you should also know about searching, sorting and optimization algorithms.', 'start': 29156.508, 'duration': 3.944}], 'summary': 'To become a machine learning expert in 2021, you need sound knowledge of programming languages such as python and r, understanding of basic programming concepts, and familiarity with searching, sorting, and optimization algorithms.', 'duration': 25.152, 'max_score': 29135.3, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU29135300.jpg'}, {'end': 29337.164, 'src': 'embed', 'start': 29311.171, 'weight': 4, 'content': [{'end': 29317.855, 'text': 'According to Glassdoor, in the United States, a machine learning engineer can earn around $114,000 per annum.', 'start': 29311.171, 'duration': 6.684}, {'end': 29320.836, 'text': 'While in India, you can earn nearly Rs.', 'start': 29318.655, 'duration': 2.181}, {'end': 29322.657, 'text': '7,73,000 per annum.', 'start': 29320.856, 'duration': 1.801}, {'end': 29328.481, 'text': 'This salary may vary based on your experience, the industry you are applying for and the company policy.', 'start': 29323.418, 'duration': 5.063}, {'end': 29330.442, 'text': 'Now moving on to the final section.', 'start': 29328.941, 'duration': 1.501}, {'end': 29334.203, 'text': 'Let me tell you how simply learn can help you start your career in machine learning.', 'start': 29331.141, 'duration': 3.062}, {'end': 29337.164, 'text': 'So let me take you to our website first.', 'start': 29335.323, 'duration': 1.841}], 'summary': 'Machine learning engineers earn $114,000 in the us and rs. 7,73,000 in india annually. simply learn provides career start in machine learning.', 'duration': 25.993, 'max_score': 29311.171, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU29311171.jpg'}, {'end': 29395.369, 'src': 'embed', 'start': 29366.294, 'weight': 7, 'content': [{'end': 29368.276, 'text': "And let's open the second link as well.", 'start': 29366.294, 'duration': 1.982}, {'end': 29376.987, 'text': 'So, so this is the postgraduate program in AI and machine learning, which is in collaboration with Purdue University and IBM.', 'start': 29369.277, 'duration': 7.71}, {'end': 29384.482, 'text': 'If I scroll down, You can see the key features of this course.', 'start': 29378.629, 'duration': 5.853}, {'end': 29392.487, 'text': "So we'll get Purdue alumni association membership, industry recognized IBM certificates, enrollment to Simply Learn's job assist.", 'start': 29385.243, 'duration': 7.244}, {'end': 29395.369, 'text': "there's 25 plus hands-on projects on GPU enabled labs.", 'start': 29392.487, 'duration': 2.882}], 'summary': 'Postgraduate program in ai and machine learning with purdue university and ibm includes 25+ hands-on projects on gpu enabled labs.', 'duration': 29.075, 'max_score': 29366.294, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU29366294.jpg'}], 'start': 27305.41, 'title': 'Us election twitter sentiment analysis and machine learning roadmap', 'summary': 'Covers twitter sentiment analysis for the 2020 u.s. presidential election, including the sentiment for trump and biden, and a machine learning roadmap for 2021, highlighting the demand, skills, algorithms, companies hiring, and salaries in the field.', 'chapters': [{'end': 27378.995, 'start': 27305.41, 'title': 'Us election twitter sentiment analysis', 'summary': 'Analyzes the upcoming united states presidential election using twitter sentiment analysis in python, covering the candidates, election forecast, and 2016 election results, with a focus on extracting tweets, storing in csv, and performing textual analysis for learning purposes.', 'duration': 73.585, 'highlights': ['The analysis covers the upcoming United States presidential election, including the candidates and the result of the 2016 US election, and utilizes Twitter sentiment analysis in Python.', 'The video emphasizes learning, stating that it is solely for educational purposes and does not exhibit bias towards any party or candidate in the United States.', 'The chapter focuses on the process of extracting tweets from Twitter handles using Python libraries, storing them in a CSV file, and performing textual analysis for understanding the public sentiment.', 'The analysis also involves looking at the election forecast by popular news agencies and poll analysis websites such as FiveThirtyEight to provide a comprehensive view of the election scenario.']}, {'end': 27719.506, 'start': 27381.753, 'title': 'Us 2020 election: trump vs biden', 'summary': 'Discusses the 2020 u.s. presidential election, including key candidates, past election results, polling forecasts, and sentiment analysis, with joe biden being favored to win the election according to various polls and forecasts.', 'duration': 337.753, 'highlights': ["Joe Biden is favored to win the election and leads in both national and state polls, with a 96% chance of winning the electoral college according to The Economist's forecast. Joe Biden's lead in national and state polls, 96% chance of winning electoral college.", "According to The Guardian, Joe Biden is leading Donald Trump in national polls, but it does not guarantee his victory, similar to Hillary Clinton's lead in 2016. Joe Biden's lead in national polls, cautionary comparison to Hillary Clinton's lead in 2016.", "The BBC forecast indicates that Joe Biden is leading national presidential polls with 51% and is set to be the next US President. Joe Biden's lead in national presidential polls, forecast of his victory."]}, {'end': 28112.335, 'start': 27720.266, 'title': 'Sentiment analysis and data visualization', 'summary': 'Demonstrates the implementation of sentiment analysis and data visualization using python libraries such as numpy, pandas, matplotlib, seaborn, text blob, word cloud, and plotly. it involves importing datasets, analyzing tweet sentiments for donald trump and joe biden, adding a polarity column to the data frames, and classifying tweets as positive, negative, or neutral based on polarity values.', 'duration': 392.069, 'highlights': ['The chapter demonstrates the implementation of sentiment analysis and data visualization using Python libraries such as numpy, pandas, matplotlib, seaborn, text blob, word cloud, and plotly. It involves using multiple Python libraries for sentiment analysis and data visualization, showcasing a comprehensive approach to the topic.', 'It involves importing datasets, analyzing tweet sentiments for Donald Trump and Joe Biden, adding a polarity column to the data frames, and classifying tweets as positive, negative, or neutral based on polarity values. The implementation includes importing datasets, analyzing tweet sentiments for Donald Trump and Joe Biden, adding a polarity column to the data frames, and classifying tweets based on their polarity values, providing a practical demonstration of sentiment analysis in action.', 'The chapter demonstrates the implementation of sentiment analysis and data visualization using Python libraries such as numpy, pandas, matplotlib, seaborn, text blob, word cloud, and plotly. It showcases the utilization of various Python libraries for sentiment analysis and data visualization, indicating a comprehensive approach to the topic.']}, {'end': 29022.42, 'start': 28112.335, 'title': 'Twitter sentiment analysis demo', 'summary': 'Covers the process of classifying tweets, creating visualizations, and understanding public sentiment through twitter sentiment analysis, with key highlights including creating bar plots based on tweet polarity, analyzing polarity percentage, and visualizing public sentiment.', 'duration': 910.085, 'highlights': ['The chapter covers the process of classifying tweets, creating visualizations, and understanding public sentiment through Twitter sentiment analysis, with key highlights including creating bar plots based on tweet polarity, analyzing polarity percentage, and visualizing public sentiment.', 'A user-defined function is created to generate a bar plot based on tweet polarity, displaying the count of positive, neutral, and negative tweets for both Donald Trump and Joe Biden, with quantifiable data showing tweet counts.', 'The demonstration includes the creation of visualizations such as distribution plot and box plot for both Donald Trump and Joe Biden, showcasing the distribution and outliers in the tweet polarity data.', 'A function is defined to create a cross table using the group by function, displaying the count of expressions for both Donald Trump and Joe Biden, providing insights into tweet classifications.', 'The process of finding the polarity percentage for negative and positive tweets is demonstrated, including the creation of a bar graph to visually represent the negative and positive polarity percentage for Donald Trump and Joe Biden, with quantifiable data showing the percentage of positive and negative tweets for each candidate.', 'The chapter delves into determining the public sentiment based on Trump and Biden tweets, calculating the total positive polarity for both candidates, and visualizing the public sentiment through a horizontal bar graph, with quantifiable data displaying the percentage of public sentiment for each candidate.', 'A user-defined function is created to plot a table displaying the most positive tweets for both Donald Trump and Joe Biden, as well as the most negative tweets, providing insights into the sentiment of the top tweets for each candidate.', 'The demonstration concludes with the creation of a word cloud to visualize the most frequently occurring words in the tweet replies for both Donald Trump and Joe Biden, providing insights into the prominent words in the tweet data.']}, {'end': 29595.084, 'start': 29023.2, 'title': 'Machine learning roadmap 2021', 'summary': "Discusses the machine learning roadmap for 2021, including the increasing demand for machine learning professionals, essential skills required, popular machine learning algorithms, companies actively hiring, and the salary of a machine learning engineer, while also highlighting simplilearn's machine learning courses and their key features.", 'duration': 571.884, 'highlights': ['Machine learning engineers can earn around $114,000 per annum in the United States, and nearly Rs. 7,73,000 per annum in India, according to Glassdoor. The average annual salary for machine learning engineers in the United States is approximately $114,000, and in India, it is around Rs. 7,73,000.', 'The chapter provides an overview of essential skills required to become a machine learning expert in 2021, including programming, mathematics and statistics, database and SQL, data wrangling, data visualization, and machine learning techniques and algorithms. The chapter outlines the essential skills needed to become a machine learning expert in 2021, covering programming, mathematics and statistics, database and SQL, data wrangling, data visualization, and machine learning techniques and algorithms.', 'The chapter discusses popular machine learning algorithms such as linear regression, logistic regression, support vector machines, k-nearest neighbors, k-means clustering, Naive Bayes, and others, as well as companies actively hiring for machine learning roles, including Microsoft, Spotify, Google, Cell, Ericsson, Oracle, and Walmart. The chapter covers popular machine learning algorithms like linear regression, logistic regression, support vector machines, k-nearest neighbors, k-means clustering, and Naive Bayes. It also mentions companies actively hiring for machine learning roles, including Microsoft, Spotify, Google, Cell, Ericsson, Oracle, and Walmart.', "The chapter highlights SimpliLearn's machine learning courses, including a postgraduate program in AI and machine learning in collaboration with Purdue University and IBM, as well as a machine learning certification course, detailing their key features and skills covered. The chapter emphasizes SimpliLearn's machine learning courses, such as a postgraduate program in AI and machine learning in collaboration with Purdue University and IBM, and a machine learning certification course, outlining their key features and skills covered."]}], 'duration': 2289.674, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/eq7KF7JTinU/pics/eq7KF7JTinU27305410.jpg', 'highlights': ['The analysis covers the upcoming United States presidential election, including the candidates and the result of the 2016 US election, and utilizes Twitter sentiment analysis in Python.', "Joe Biden is favored to win the election and leads in both national and state polls, with a 96% chance of winning the electoral college according to The Economist's forecast.", 'The chapter demonstrates the implementation of sentiment analysis and data visualization using Python libraries such as numpy, pandas, matplotlib, seaborn, text blob, word cloud, and plotly.', 'The chapter covers the process of classifying tweets, creating visualizations, and understanding public sentiment through Twitter sentiment analysis, with key highlights including creating bar plots based on tweet polarity, analyzing polarity percentage, and visualizing public sentiment.', 'Machine learning engineers can earn around $114,000 per annum in the United States, and nearly Rs. 7,73,000 per annum in India, according to Glassdoor.', 'The chapter provides an overview of essential skills required to become a machine learning expert in 2021, including programming, mathematics and statistics, database and SQL, data wrangling, data visualization, and machine learning techniques and algorithms.', 'The chapter discusses popular machine learning algorithms such as linear regression, logistic regression, support vector machines, k-nearest neighbors, k-means clustering, Naive Bayes, and others, as well as companies actively hiring for machine learning roles, including Microsoft, Spotify, Google, Cell, Ericsson, Oracle, and Walmart.', "The chapter highlights SimpliLearn's machine learning courses, including a postgraduate program in AI and machine learning in collaboration with Purdue University and IBM, as well as a machine learning certification course, detailing their key features and skills covered."]}], 'highlights': ['The chapter covers the basics of machine learning, including essential applications, vital algorithms such as k-nearest neighbors, and the concepts of supervised and unsupervised learning.', 'Real-world applications of machine learning such as virtual personal assistants, traffic predictions, and surge pricing models are discussed.', 'Machine learning is the science of making computers learn and act like humans by feeding data and information without being explicitly programmed.', 'The process of machine learning involves defining objectives, collecting and preparing data, selecting and training an algorithm, testing the model, running predictions, and deploying the model.', 'Reinforcement learning is fundamental and applicable to AI and machine learning, representing a significant advancement.', 'Linear regression is a widely used algorithm in statistics and machine learning, predicting outcomes based on a linear relationship between input and output variables.', 'Understanding supervised and unsupervised learning is crucial for any project, with supervised learning involving labeled data and direct feedback, and unsupervised learning focusing on finding hidden structures in unlabeled data, essential for any project.', 'Explains the fundamental concept of support vector machines and the goal of choosing a hyperplane with the greatest possible margin.', 'Describes the practical application of support vector machines in classifying muffin and cupcake recipes using Python in a Jupyter Notebook environment.', 'The chapter covers building a SVM classifier for cupcake vs muffin recipes A support vector machine code was used to classify recipes as either a cupcake or a muffin, with a specific example of predicting 40 parts flour and 20 parts sugar.', 'The chapter introduces K-means clustering as a method of organizing data into groups based on feature similarities, using the example of categorizing books into clusters.', 'The chapter emphasizes importing car data from the 70s and 80s, showcasing the number of cars produced by brands like Toyota, Honda, and Nissan.', 'The chapter covers the import of numpy, pandas, seaborn, and matplotlib libraries in Python, emphasizing the use of Anaconda for Jupyter Notebook and the importance of data visualization tools like seaborn and matplotlibrary.', 'The chapter covers the process of splitting data, creating a logistic regression model, and evaluating its performance with a precision of 92% in predicting the type of tumor.', 'The chapter covers various matrix operations, including addition, subtraction, scalar multiplication, matrix and vector multiplication, matrix to matrix multiplication, transpose, identity matrix, and matrix inverse.', 'Understanding the importance of linear algorithm, calculus, and differential equations in data science.', 'Replacing sensors before they break to save costs, selecting samples from the fan population to make conclusions', 'The chapter demonstrates using Pandas in Python to calculate basic statistics like mean, median, mode, range, and descriptive statistics on a sample data set.', 'The alternative hypothesis states that the new drug significantly lowers blood pressure more than the existing drug, with a focus on a 5% significance level for p-values and the use of t-values for comparing positive test results.', 'The chapter demonstrates the use of sets in Python to handle unique values and the conversion of a list to a set, deleting duplicate elements.', 'Implementing multiple linear regression in Python to predict company profit based on factors like R&D, administration, and marketing costs, achieving a precision of 90%, recall of 0.96, and accuracy of about 90%.', 'The logistic regression model achieves an accuracy of about 94% using the confusion matrix.', 'Achieved F1 score of 0.857 when evaluating machine learning models', "The model's accuracy is about 94.6%, allowing the bank to make informed decisions on loan approvals based on customer predictions.", 'Model accuracy is 93%, with 30 accurate predictions out of 32.', 'The chapter emphasizes the quick and efficient process of setting up a machine learning algorithm, with the creation of SVM requiring only two lines of code.', 'The analysis covers the upcoming United States presidential election, including the candidates and the result of the 2016 US election, and utilizes Twitter sentiment analysis in Python.']}