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Artificial Intelligence Course | AI Full Course | Artificial Intelligence Tutorial | Simplilearn

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This video on Artificial Intelligence Course will help us understand the basics of artificial intelligence. We will look at the future of AI and listen to some of the industry experts and learn what they have to say about AI. You will see the top 10 applications of AI in 2021. Then, we will understand Machine Learning and Deep Learning and the different algorithms used to build AI models. Finally, you will learn the Top 10 Artificial Intelligence Technologies In 2021. Let's begin this Artificial Intelligence Course Video!
Here are the topics covered in this Artificial Intelligence Course
00:00:00 Artificial Intelligence in 5 min
00:05:59 Future Of Artificial Intelligence
00:13:20 Artificial Intelligence Application 2021
00:25:38 Should we be afraid of Artificial Intelligence
00:38:21 What is Artificial Intelligence
00:48:13 Machine Learning Part 1
01:21:57 Linear Regression Analysis
01:41:34 Decision Tree
01:58:12 Machine Learning Part 2
02:51:14 KNN algorithm Using Python
03:17:40 Mathematics For Machine Learning
05:07:53 Deep Learning Tutorial
05:52:46 TensorFlow 2.0 Tutorial for Beginners
07:18:44 Top 10 Artificial Intelligence Technologies in 2021
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#ArtificialIntelligenceCourse #ArtificialIntelligenceTutorial #ArtificialIntelligenceFullCourse #AIFullCourse #AIForBeginners #ArtificialIntelligenceTutorialForBeginners #Simplilearn
What is Artificial Intelligence?
Artificial Intelligence or AI is the combination of algorithms used for the purpose of creating intelligent machines that have the same skills as a human being. AI has made significant advances in the past few years and has impacted both our everyday lives and business in big ways. It is being widely used in every sector of business, such as Healthcare, E-Commerce, Manufacturing, Retail, and Logistics.
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âžˇď¸Ź About Post Graduate Program In AI And Machine Learning
This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.
âś… Key Features
- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools
âś… Skills Covered
- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many Moreâ€¦
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{'title': 'Artificial Intelligence Course | AI Full Course | Artificial Intelligence Tutorial | Simplilearn', 'heatmap': [{'end': 2692.005, 'start': 2417.585, 'weight': 0.947}, {'end': 5386.083, 'start': 3228.817, 'weight': 0.759}], 'summary': "The course covers ai's current state and future by 2045, global ai market projection, machine learning fundamentals, python implementation for predictive analysis achieving 94.6% accuracy, k-nearest neighbors application, math basics for machine learning, calculus in data science, statistics, probability, model metrics, deep learning, tensorflow essentials, lstm neural network modeling, and analyzing model performance with a mean square error of 0.0088, prediction accuracy, and exploration of top 10 ai technologies.", 'chapters': [{'end': 694.879, 'segs': [{'end': 112.127, 'src': 'embed', 'start': 82.938, 'weight': 0, 'content': [{'end': 88.082, 'text': 'This intelligence is built using complex algorithms and mathematical functions.', 'start': 82.938, 'duration': 5.144}, {'end': 92.687, 'text': 'But AI may not be as obvious as in the previous examples.', 'start': 88.522, 'duration': 4.165}, {'end': 103.18, 'text': 'In fact, AI is used in smartphones, cars, social media feeds, video games, banking, surveillance, and many other aspects of our daily life.', 'start': 93.048, 'duration': 10.132}, {'end': 112.127, 'text': 'The real question is, what does an AI do at its core? Here is a robot we built in our lab, which is now dropped onto a field.', 'start': 103.621, 'duration': 8.506}], 'summary': "Ai is pervasive in daily life, used in smartphones, cars, social media, video games, banking, surveillance. it's core is demonstrated by a robot in a lab.", 'duration': 29.189, 'max_score': 82.938, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA82938.jpg'}, {'end': 204.243, 'src': 'embed', 'start': 178.503, 'weight': 1, 'content': [{'end': 183.827, 'text': 'Weak AI focuses solely on one task.', 'start': 178.503, 'duration': 5.324}, {'end': 191.933, 'text': "For example, AlphaGo is a maestro of the game Go, but you can't expect it to be even remotely good at chess.", 'start': 184.508, 'duration': 7.425}, {'end': 194.715, 'text': 'This makes AlphaGo a weak AI.', 'start': 192.694, 'duration': 2.021}, {'end': 201.841, 'text': 'You might say Alexa is definitely not a weak AI since it can perform multiple tasks.', 'start': 195.956, 'duration': 5.885}, {'end': 204.243, 'text': "Well, that's not really true.", 'start': 202.442, 'duration': 1.801}], 'summary': "Weak ai is task-specific, like alphago for go, while alexa's multitasking is not true ai.", 'duration': 25.74, 'max_score': 178.503, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA178503.jpg'}, {'end': 314.309, 'src': 'embed', 'start': 285.023, 'weight': 2, 'content': [{'end': 292.413, 'text': 'Ray Kurzweil, a well-known futurist, predicts that by the year 2045, we would have robots as smart as humans.', 'start': 285.023, 'duration': 7.39}, {'end': 295.095, 'text': 'This is called the point of singularity.', 'start': 293.234, 'duration': 1.861}, {'end': 297.217, 'text': "Well, that's not all.", 'start': 295.576, 'duration': 1.641}, {'end': 306.843, 'text': 'In fact, Elon Musk predicts that the human mind and body will be enhanced by AI implants, which would make us partly cyborgs.', 'start': 297.817, 'duration': 9.026}, {'end': 309.304, 'text': "So, here's a question for you.", 'start': 307.523, 'duration': 1.781}, {'end': 314.309, 'text': "Which of the below AI projects don't exist yet? A.", 'start': 309.905, 'duration': 4.404}], 'summary': 'By 2045, robots as smart as humans; ai implants may make humans partly cyborgs.', 'duration': 29.286, 'max_score': 285.023, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA285023.jpg'}, {'end': 435.664, 'src': 'embed', 'start': 408.633, 'weight': 3, 'content': [{'end': 415.824, 'text': 'And anytime you work with technology, you need to learn to harness the benefits while minimizing the downside.', 'start': 408.633, 'duration': 7.191}, {'end': 424.213, 'text': "is we're focusing on autonomous systems.", 'start': 421.79, 'duration': 2.423}, {'end': 429.157, 'text': 'And clearly one purpose of autonomous systems is self-driving cars.', 'start': 424.993, 'duration': 4.164}, {'end': 430.419, 'text': 'There are others.', 'start': 429.578, 'duration': 0.841}, {'end': 435.664, 'text': 'And we sort of see it as the mother of all AI projects.', 'start': 431.64, 'duration': 4.024}], 'summary': 'Focusing on autonomous systems, especially self-driving cars, as the mother of all ai projects.', 'duration': 27.031, 'max_score': 408.633, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA408633.jpg'}, {'end': 642.862, 'src': 'embed', 'start': 618.119, 'weight': 4, 'content': [{'end': 626.108, 'text': "Anything that's repetitive and done, you know, on the back of, you know, technology or, you know, is going to be fundamentally vulnerable.", 'start': 618.119, 'duration': 7.989}, {'end': 634.558, 'text': 'Yeah So I think technology and in particular AI can in fact bring more empowerment, more inclusiveness.', 'start': 626.248, 'duration': 8.31}, {'end': 642.862, 'text': 'And, at the same time, we should be clear-eyed about displacement, clear-eyed about unintended consequences, like any other technology and work,', 'start': 634.778, 'duration': 8.084}], 'summary': 'Ai can bring more empowerment and inclusiveness while being clear-eyed about displacement and unintended consequences.', 'duration': 24.743, 'max_score': 618.119, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA618119.jpg'}], 'start': 7.426, 'title': 'Ai in 21st century and its future by 2045', 'summary': 'Introduces ai, its applications, core components, and top 10 ai technologies in 2021, along with distinctions between weak and strong ai. additionally, it discusses predictions by experts about ai advancements by 2045, potential societal impacts, and the need for proactive measures.', 'chapters': [{'end': 284.302, 'start': 7.426, 'title': 'Artificial intelligence in 21st century', 'summary': "Introduces artificial intelligence, covering its applications, core components, and categories, highlighting the top 10 ai technologies in 2021 and the distinctions between weak and strong ai, including industry experts' opinions and examples, and emphasizing the role of ai in various aspects of daily life.", 'duration': 276.876, 'highlights': ["AI is one of the most trending technologies of the 21st century AI's significance in the current era.", 'Top 10 applications of AI in 2021 The relevance of AI in various fields in the current year.', "AI's usage in smartphones, cars, social media feeds, video games, banking, surveillance, and many other aspects of daily life The pervasive use of AI in everyday scenarios.", "The robot's generalized learning, reasoning ability, and problem-solving capabilities as manifestations of AI Illustration of AI's fundamental capabilities through the robot's actions.", 'The distinction between weak AI and strong AI, with examples such as AlphaGo and Alexa Clarifying the differences between weak and strong AI with relatable examples.']}, {'end': 694.879, 'start': 285.023, 'title': 'Future of ai by 2045', 'summary': 'Discusses predictions by ray kurzweil and elon musk about ai advancements by 2045, the current capabilities of ai, and potential societal impacts, emphasizing the need for proactive measures to mitigate risks and ensure inclusivity.', 'duration': 409.856, 'highlights': ['Ray Kurzweil predicts robots as smart as humans by 2045, termed as singularity, while Elon Musk anticipates AI implants enhancing human mind and body, making us partly cyborgs. Predictions by Ray Kurzweil and Elon Musk about AI advancements by 2045.', "AI projects like a robot with a muscular skeletal system, AI that can read its owner's emotions, and AI that develops emotions over time are yet to be realized. Existence status of various AI projects.", "AI's current role is to work with humans and make tasks easier, while the future of AI remains uncertain due to unexplored domains and the mystery of the human brain. Current and future roles of AI, unexplored domains, and the mystery of the human brain.", "AI's potential societal impacts include solving complex problems, autonomous systems like self-driving cars, advancements in speech and image recognition, and aiding in medical advancements for conditions like visual impairment, dyslexia, and ALS. Societal impacts and applications of AI in various domains.", 'The chapter emphasizes the importance of proactive measures to mitigate risks, ensure inclusivity, address displacement, and handle unintended consequences of AI, while highlighting the need for continuous learning and transforming education. Importance of proactive measures, continuous learning, and transforming education to address AI-related risks and societal impacts.']}], 'duration': 687.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA7426.jpg', 'highlights': ["AI's pervasive use in smartphones, cars, social media feeds, video games, banking, surveillance, and many other aspects of daily life.", 'The distinction between weak AI and strong AI, with examples such as AlphaGo and Alexa.', 'Ray Kurzweil predicts robots as smart as humans by 2045, termed as singularity, while Elon Musk anticipates AI implants enhancing human mind and body, making us partly cyborgs.', "AI's potential societal impacts include solving complex problems, autonomous systems like self-driving cars, advancements in speech and image recognition, and aiding in medical advancements for conditions like visual impairment, dyslexia, and ALS.", 'The chapter emphasizes the importance of proactive measures to mitigate risks, ensure inclusivity, address displacement, and handle unintended consequences of AI, while highlighting the need for continuous learning and transforming education.']}, {'end': 2632.774, 'segs': [{'end': 752.026, 'src': 'embed', 'start': 696.704, 'weight': 11, 'content': [{'end': 706.632, 'text': 'You know, I have exposure to the very most cutting edge AI, and I think people should be really concerned about it.', 'start': 696.704, 'duration': 9.928}, {'end': 715.179, 'text': 'I keep sounding the alarm bell, but until people see robots going down the street killing people,', 'start': 708.113, 'duration': 7.066}, {'end': 718.282, 'text': "they don't know how to react because it seems so ethereal.", 'start': 715.179, 'duration': 3.103}, {'end': 725.067, 'text': 'And I think we should be really concerned about AI, and I think we should.', 'start': 720.623, 'duration': 4.444}, {'end': 731.781, 'text': 'AI is a rare case where I think we need to be proactive in regulation instead of reactive.', 'start': 726.772, 'duration': 5.009}, {'end': 737.691, 'text': "Because I think by the time we are reactive in AI regulation, it's too late.", 'start': 732.983, 'duration': 4.708}, {'end': 748.204, 'text': 'Right now we have machine learning algorithms that can solve an incredibly complex problem beyond any human intelligence,', 'start': 738.492, 'duration': 9.712}, {'end': 751.145, 'text': "but they're essentially complete idiots and two-year-olds and anything.", 'start': 748.204, 'duration': 2.941}, {'end': 752.026, 'text': "that's not that problem.", 'start': 751.145, 'duration': 0.881}], 'summary': 'The speaker highlights concerns about ai, advocating proactive regulation to avoid future consequences.', 'duration': 55.322, 'max_score': 696.704, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA696704.jpg'}, {'end': 864.988, 'src': 'embed', 'start': 800.745, 'weight': 0, 'content': [{'end': 806.188, 'text': 'Hey guys, welcome to this Simply Learned session on AI applications in 2020.', 'start': 800.745, 'duration': 5.443}, {'end': 809.21, 'text': "So here's a small refresher about Artificial Intelligence.", 'start': 806.188, 'duration': 3.022}, {'end': 815.334, 'text': 'Artificial Intelligence refers to intelligence displayed by machines that simulates human intelligence.', 'start': 809.79, 'duration': 5.544}, {'end': 819.976, 'text': "Basically, it's the ability of a machine or a program to think and learn.", 'start': 816.034, 'duration': 3.942}, {'end': 824.719, 'text': "Now that you're all caught up, let's take a look at how different domains are using AI.", 'start': 820.657, 'duration': 4.062}, {'end': 827.361, 'text': "Let's have a look at the current market state of AI.", 'start': 825.219, 'duration': 2.142}, {'end': 836.29, 'text': 'According to Tractica, a market research firm, the global AI market is expected to reach a revenue of $118 billion by 2025.', 'start': 828.365, 'duration': 7.925}, {'end': 844.816, 'text': 'Next. according to the research firm Gartner, AI usage has grown by 270% in the last four years,', 'start': 836.29, 'duration': 8.526}, {'end': 848.339, 'text': "a clear indication of the growth that's yet to come in the upcoming years.", 'start': 844.816, 'duration': 3.523}, {'end': 855.844, 'text': 'In fact, 87% of companies that actually have adopted AI were using it to improve email marketing.', 'start': 849.339, 'duration': 6.505}, {'end': 864.988, 'text': 'And in some news, that could be positive or negative depending on how you look at it, 75 countries are now using AI technology for surveillance.', 'start': 856.405, 'duration': 8.583}], 'summary': 'Global ai market to reach $118b by 2025, 87% companies use ai for email marketing, 75 countries use ai for surveillance.', 'duration': 64.243, 'max_score': 800.745, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA800745.jpg'}, {'end': 938.622, 'src': 'embed', 'start': 910.098, 'weight': 19, 'content': [{'end': 912.502, 'text': 'Search results are shown that match their query.', 'start': 910.098, 'duration': 2.404}, {'end': 916.589, 'text': 'So all of this can be done without them having to type a single word.', 'start': 912.983, 'duration': 3.606}, {'end': 919.417, 'text': 'Next, we have AI-powered assistants.', 'start': 917.176, 'duration': 2.241}, {'end': 925.558, 'text': 'Assistants like virtual shopping assistants and chatbots help improve user experience while shopping online.', 'start': 920.017, 'duration': 5.541}, {'end': 933.48, 'text': 'For this, techniques like NLP or natural language processing are used to make the conversation sound as human and personal as possible.', 'start': 925.718, 'duration': 7.762}, {'end': 938.622, 'text': 'Did you know that soon customer service could be handled by chatbots on Amazon.com?', 'start': 933.981, 'duration': 4.641}], 'summary': 'Search results shown without typing, ai assistants improve online shopping experience using nlp, and chatbots could handle customer service on amazon.com.', 'duration': 28.524, 'max_score': 910.098, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA910098.jpg'}, {'end': 1016.173, 'src': 'embed', 'start': 986.264, 'weight': 3, 'content': [{'end': 989.245, 'text': 'Finally, under e-commerce, we have fraud prevention.', 'start': 986.264, 'duration': 2.981}, {'end': 996.009, 'text': 'Two of the biggest issues that e-commerce companies have to deal with are credit card fraud and fake reviews.', 'start': 990.046, 'duration': 5.963}, {'end': 1003.786, 'text': 'By taking into consideration usage patterns, AI can help deal with reducing the possibility of credit card frauds taking place.', 'start': 996.569, 'duration': 7.217}, {'end': 1006.047, 'text': 'Talking about fake reviews.', 'start': 1004.426, 'duration': 1.621}, {'end': 1012.571, 'text': 'did you know that more than 80% of customers decide to buy a product or service based on their customer reviews?', 'start': 1006.047, 'duration': 6.524}, {'end': 1016.173, 'text': 'AI can help identify and handle fake reviews.', 'start': 1013.131, 'duration': 3.042}], 'summary': 'E-commerce faces credit card fraud and fake reviews issues. ai can reduce fraud and identify fake reviews, influencing 80% of customer decisions.', 'duration': 29.909, 'max_score': 986.264, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA986264.jpg'}, {'end': 1081.147, 'src': 'embed', 'start': 1050.13, 'weight': 4, 'content': [{'end': 1052.853, 'text': 'Next up, we have AI applications in robotics.', 'start': 1050.13, 'duration': 2.723}, {'end': 1055.357, 'text': "First off, let's have a look at mobility.", 'start': 1053.314, 'duration': 2.043}, {'end': 1059.397, 'text': 'Robots that are powered by AI will use real-time updates.', 'start': 1056.195, 'duration': 3.202}, {'end': 1062.998, 'text': 'They would be able to maneuver through a particular part of travel.', 'start': 1059.877, 'duration': 3.121}, {'end': 1068.261, 'text': 'With this path, the robot can sense obstacles in its path and then pre-plan its journey.', 'start': 1063.278, 'duration': 4.983}, {'end': 1077.065, 'text': 'It can be used for carrying goods in factories, warehouses and hospitals, cleaning offices and large equipments, inventory management,', 'start': 1068.801, 'duration': 8.264}, {'end': 1081.147, 'text': 'and it is also used for exploring environments that are too dangerous for humans.', 'start': 1077.065, 'duration': 4.082}], 'summary': 'Ai-powered robots enable real-time mobility and maneuverability, with applications in factories, warehouses, hospitals, and hazardous environments.', 'duration': 31.017, 'max_score': 1050.13, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA1050130.jpg'}, {'end': 1133.182, 'src': 'embed', 'start': 1090.126, 'weight': 8, 'content': [{'end': 1096.868, 'text': 'With this knowledge, unnecessary breakdowns are prevented and it can also reduce associated costs of major issues.', 'start': 1090.126, 'duration': 6.742}, {'end': 1101.31, 'text': "Next up, let's see how AI is applied in the human resources domain.", 'start': 1097.629, 'duration': 3.681}, {'end': 1104.551, 'text': "Now, this is something most people wouldn't have expected.", 'start': 1101.97, 'duration': 2.581}, {'end': 1111.553, 'text': 'Did you know that companies use software to ease the hiring process? Artificial intelligence helps with blind hiring.', 'start': 1105.031, 'duration': 6.522}, {'end': 1118.136, 'text': 'Software that uses machine learning can be used to sift through applications based on specific parameters.', 'start': 1112.393, 'duration': 5.743}, {'end': 1125.799, 'text': "AI can be used to scan job candidates' profiles and resumes to provide recruiters an understanding of the talent pool they must choose from.", 'start': 1118.636, 'duration': 7.163}, {'end': 1129.24, 'text': "Now let's have a look at AI applications in healthcare.", 'start': 1126.379, 'duration': 2.861}, {'end': 1133.182, 'text': "First off, let's have a look at how AI is used in patient care.", 'start': 1129.64, 'duration': 3.542}], 'summary': 'Ai prevents breakdowns, reduces costs. ai aids in hr, healthcare, and patient care.', 'duration': 43.056, 'max_score': 1090.126, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA1090126.jpg'}, {'end': 1212.798, 'src': 'embed', 'start': 1152.082, 'weight': 5, 'content': [{'end': 1155.644, 'text': 'Some of the popular uses are Javion, Analytic and Wellframe.', 'start': 1152.082, 'duration': 3.562}, {'end': 1160.047, 'text': "Next, let's see how AI is used with medical imaging and diagnostics.", 'start': 1156.124, 'duration': 3.923}, {'end': 1168.015, 'text': 'AI can help with early diagnosis to analyze chronic conditions taking into consideration laboratory and other medical data.', 'start': 1161.232, 'duration': 6.783}, {'end': 1173.357, 'text': 'It is also used with advanced medical imaging through which you can analyze and transform images.', 'start': 1168.315, 'duration': 5.042}, {'end': 1176.739, 'text': 'Through this, you can create models for possible scenarios.', 'start': 1173.878, 'duration': 2.861}, {'end': 1180.501, 'text': 'Next up is AI applications in research and development.', 'start': 1177.339, 'duration': 3.162}, {'end': 1184.263, 'text': 'AI is really important when it comes to the discovery of new drugs.', 'start': 1181.081, 'duration': 3.182}, {'end': 1189.386, 'text': 'This is made possible with the help of a combination of historical data and medical intelligence.', 'start': 1184.683, 'duration': 4.703}, {'end': 1192.948, 'text': 'It also helps understand the human gene and its components.', 'start': 1189.806, 'duration': 3.142}, {'end': 1197.411, 'text': 'It also helps predict the different outcomes possible if gene editing is performed.', 'start': 1193.348, 'duration': 4.063}, {'end': 1204.595, 'text': "Right now, there's probably scientists racing to develop the gene sequence for COVID-19 and towards the creation of the vaccine.", 'start': 1197.771, 'duration': 6.824}, {'end': 1208.056, 'text': 'Now that we have reached midwayish, I have a question to ask.', 'start': 1205.055, 'duration': 3.001}, {'end': 1212.798, 'text': 'Are you guys using AI-powered software in your workplace? Let me know in the live chat.', 'start': 1208.436, 'duration': 4.362}], 'summary': 'Ai is used in medical imaging, diagnosis, drug discovery, and gene research, aiding in early diagnosis of chronic conditions and potential outcomes of gene editing.', 'duration': 60.716, 'max_score': 1152.082, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA1152082.jpg'}, {'end': 1280.731, 'src': 'embed', 'start': 1253.064, 'weight': 6, 'content': [{'end': 1255.967, 'text': 'This way, herbicides can be sprayed only where the weeds are.', 'start': 1253.064, 'duration': 2.903}, {'end': 1258.649, 'text': 'With this, herbicide usage is limited.', 'start': 1256.267, 'duration': 2.382}, {'end': 1261.312, 'text': 'AI also helps with agriculture bots.', 'start': 1258.669, 'duration': 2.643}, {'end': 1266.737, 'text': 'It can be used to reduce human labour by harvesting crops at a faster rate and a higher volume.', 'start': 1261.572, 'duration': 5.165}, {'end': 1270.224, 'text': "Next, let's have a look at artificial intelligence in gaming.", 'start': 1267.262, 'duration': 2.962}, {'end': 1275.307, 'text': 'One of the most important things that game companies need to handle, which is labour costs.', 'start': 1270.704, 'duration': 4.603}, {'end': 1280.731, 'text': 'This is done to generate levels, maps, textures, weapons, characters, etc.', 'start': 1275.808, 'duration': 4.923}], 'summary': 'Ai reduces herbicide usage, automates farming, and cuts gaming labor costs.', 'duration': 27.667, 'max_score': 1253.064, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA1253064.jpg'}, {'end': 1395.175, 'src': 'embed', 'start': 1363.899, 'weight': 7, 'content': [{'end': 1366.542, 'text': 'This can be used to identify the people in an image.', 'start': 1363.899, 'duration': 2.643}, {'end': 1369.444, 'text': 'AI is also used in a tool called deep text.', 'start': 1366.922, 'duration': 2.522}, {'end': 1374.208, 'text': 'So with the help of AI can be used to analyze posts that represent suicidal thoughts.', 'start': 1369.924, 'duration': 4.284}, {'end': 1378.556, 'text': 'The tool can also be used to translate posts from different languages.', 'start': 1374.892, 'duration': 3.664}, {'end': 1381.88, 'text': "Next, let's have a look at AI used by Instagram.", 'start': 1379.217, 'duration': 2.663}, {'end': 1389.188, 'text': 'AI takes into account your likes and the accounts you follow to determine what posts you are shown on your Explore tab.', 'start': 1382.44, 'duration': 6.748}, {'end': 1395.175, 'text': 'The Deep Text tool has also been used recently to identify and remove spam messages from user accounts.', 'start': 1389.488, 'duration': 5.687}], 'summary': 'Ai used to identify people in images, analyze suicidal thoughts, translate posts, and personalize instagram content based on user likes and follows.', 'duration': 31.276, 'max_score': 1363.899, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA1363899.jpg'}], 'start': 696.704, 'title': 'Ai in 2020', 'summary': 'Discusses the concerns and applications of ai, with the global ai market projected to reach $118 billion by 2025. it also covers the impact of ai on various sectors, from email marketing to surveillance, e-commerce, road safety, robotics, human resources, healthcare, and the potential challenges and dangers of ai, emphasizing the need for responsible and ethical use.', 'chapters': [{'end': 1027.117, 'start': 696.704, 'title': 'Ai threat and applications in 2020', 'summary': 'Discusses the concerns about ai, the need for proactive regulation, and the growth and applications of ai. the global ai market is expected to reach $118 billion by 2025, with 87% of companies using ai to improve email marketing and 75 countries using ai for surveillance. ai applications in e-commerce include personalized shopping, smart purchasing, and fraud prevention.', 'duration': 330.413, 'highlights': ['The global AI market is expected to reach a revenue of $118 billion by 2025. This quantifiable data highlights the massive growth potential of the AI market, indicating its significant impact on various industries.', '87% of companies that have adopted AI were using it to improve email marketing. This statistic demonstrates the widespread adoption of AI for improving email marketing, showcasing its effectiveness in business applications.', '75 countries are now using AI technology for surveillance. This quantifiable data highlights the widespread use of AI for surveillance purposes, indicating its global impact and potential implications.', 'AI applications in e-commerce include personalized shopping, smart purchasing, and fraud prevention. This summarizes the key AI applications in e-commerce, showcasing its potential to enhance customer experience and business operations.', 'AI offers personalized shopping, recommendation engine, visual searches, and AI-powered assistants in e-commerce. This details the specific AI applications in e-commerce, emphasizing its ability to improve customer engagement and satisfaction.']}, {'end': 1564.16, 'start': 1027.558, 'title': 'Ai applications in 2020', 'summary': 'Discusses the various applications of ai in 2020, including gps technology for road safety, ai in robotics for mobility and process optimization, human resources, healthcare, agriculture, gaming, automobiles, social media, and marketing, highlighting its impact on improving safety, reducing costs, enhancing hiring processes, and personalizing user experiences.', 'duration': 536.602, 'highlights': ['AI in robotics enables real-time updates for maneuvering and sensing obstacles, used in various industries such as logistics, cleaning, inventory management, and hazardous environment exploration. Real-time updates for maneuvering and sensing obstacles, applications in logistics, cleaning, inventory management, and hazardous environment exploration', 'AI in healthcare assists in preventing prescription errors and aids in medical imaging for early diagnosis, chronic condition analysis, and creating models for possible scenarios. Preventing prescription errors, aiding in medical imaging for early diagnosis and chronic condition analysis, creating models for possible scenarios', 'AI in agriculture monitors crop and soil health, decreases pesticide usage, and reduces human labor by harvesting crops at a faster rate and a higher volume. Monitoring crop and soil health, decreasing pesticide usage, reducing human labor by harvesting crops at a faster rate and a higher volume', 'AI in social media helps in analyzing pictures, identifying suicidal thoughts, translating posts, showing relevant content, handling cyberbullying, fraud, propaganda, and hateful content, and automatically cropping images based on face recognition. Analyzing pictures, identifying suicidal thoughts, translating posts, showing relevant content, handling cyberbullying, fraud, propaganda, and hateful content, automatically cropping images based on face recognition', 'AI in marketing enables programmatic advertising, personalized narratives, chatbots, personalized UI and UX, and localization for optimized market campaigns. Enabling programmatic advertising, personalized narratives, chatbots, personalized UI and UX, and localization for optimized market campaigns']}, {'end': 1909.623, 'start': 1564.62, 'title': 'Artificial intelligence: boons and challenges', 'summary': 'Discusses the rapid advancement and impact of artificial intelligence, highlighting its boons in safety, communication, healthcare, and its potential to replace and create jobs, while also addressing the challenges and uncertainties it presents to society.', 'duration': 345.003, 'highlights': ['AI aids in safety by replacing humans in risky fields like defense and mining, reducing road accidents, and enabling effective communication, language processing, and speech recognition.', 'AI greatly impacts healthcare by predicting COVID-19 temperatures and survival rates with over 90% accuracy, guiding vaccine design, and advancing genomics study with faster and accurate DNA sequencing.', 'The impact of AI on the job market is significant, with studies suggesting the replacement of 7 million jobs in the UK by 2037 but also the creation of 7.2 million jobs, and the potential automation of 400 to 800 million jobs by 2030.', 'The chapter also addresses the concerns and uncertainties surrounding AI, including the fear of job displacement and the challenge of adapting to the changes brought about by automation, with Google CEO Sundar Pichai asserting the transformative nature of AI.']}, {'end': 2205.594, 'start': 1910.304, 'title': 'The looming dangers of ai', 'summary': 'Discusses the potential threat of ai replacing various professions, the warnings by experts like elon musk and stephen hawking, the risks of ai becoming conscious and the development of ai neural networks, emphasizing the need for cautious and ethical handling of ai.', 'duration': 295.29, 'highlights': ["Elon Musk and Stephen Hawking have warned about the looming dangers of AI, suggesting it could be the greatest threat to humanity. Elon Musk and Stephen Hawking's warnings about the potential threat of AI, presenting it as a significant danger to humanity.", 'AI may replace professionals in various fields, such as fast food joints and radiologists in hospitals. The potential impact of AI on professions, including the replacement of roles like fast food workers and radiologists in hospitals.', "AI's exponential rate of improvement is emphasized, with Musk stating that AI is far more dangerous than nuclear warheads. Elon Musk's emphasis on the exponential rate of improvement in AI and his assertion that it poses a greater danger than nuclear warheads.", 'The risks associated with AI becoming conscious and responsive, as well as the potential for misuse by individuals for nefarious purposes, are highlighted. Discussion of the potential risks of AI becoming conscious and being manipulated for harmful purposes, along with the concerns about it surpassing human intelligence.', "The incident involving Facebook's chatbot experiment, where the AIs developed their own language for communication, is presented as a concerning example of AI's unpredictability. The Facebook chatbot experiment, illustrating the unpredictability of AI as the AIs developed their own language for communication, raising concerns about the lack of human oversight."]}, {'end': 2632.774, 'start': 2206.403, 'title': 'Ai and smart homes: a ted talk summary', 'summary': 'Discusses the potential dangers of ai if not approached with healthy skepticism, the need to understand and utilize ai responsibly, and a brief history of ai, including key milestones and advancements.', 'duration': 426.371, 'highlights': ['AI must be approached with healthy skepticism to avoid potential dangers, as even small issues can escalate into hazardous situations. The speaker emphasizes the need to pause, check conditions, and implement safety standards for AI to prevent potential dangers from escalating.', 'Understanding and responsible utilization of AI is crucial to ensure it positively impacts lives. The chapter stresses the importance of understanding and using AI in a way that enhances lives and emphasizes the lack of human empathy and consciousness in AI.', 'Brief history of AI, including milestones and advancements from 1956 to 2018, showcasing the rapid progress of AI technology. The timeline of AI milestones from 1956 to 2018 is discussed, highlighting the rapid advancements and compression of time in AI technology.', 'Introduction to smart homes and key features enabled by AI, such as voice-controlled appliances, climate-adjusting sensors, and remote control capabilities. The concept of smart homes and the AI-driven features, including voice-controlled appliances, climate-adjusting sensors, and remote control capabilities, are introduced.', 'Explanation of the types of AI, including purely reactive, limited memory, theory of mind, and self-awareness, with examples and characteristics. The four types of AI â€“ purely reactive, limited memory, theory of mind, and self-awareness â€“ are explained, along with examples and characteristics for each type.']}], 'duration': 1936.07, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA696704.jpg', 'highlights': ['The global AI market is expected to reach a revenue of $118 billion by 2025.', '87% of companies that have adopted AI were using it to improve email marketing.', '75 countries are now using AI technology for surveillance.', 'AI applications in e-commerce include personalized shopping, smart purchasing, and fraud prevention.', 'AI in robotics enables real-time updates for maneuvering and sensing obstacles, used in various industries such as logistics, cleaning, inventory management, and hazardous environment exploration.', 'AI in healthcare assists in preventing prescription errors and aids in medical imaging for early diagnosis, chronic condition analysis, and creating models for possible scenarios.', 'AI in agriculture monitors crop and soil health, decreases pesticide usage, and reduces human labor by harvesting crops at a faster rate and a higher volume.', 'AI in social media helps in analyzing pictures, identifying suicidal thoughts, translating posts, showing relevant content, handling cyberbullying, fraud, propaganda, and hateful content, and automatically cropping images based on face recognition.', 'AI aids in safety by replacing humans in risky fields like defense and mining, reducing road accidents, and enabling effective communication, language processing, and speech recognition.', 'AI greatly impacts healthcare by predicting COVID-19 temperatures and survival rates with over 90% accuracy, guiding vaccine design, and advancing genomics study with faster and accurate DNA sequencing.', 'The impact of AI on the job market is significant, with studies suggesting the replacement of 7 million jobs in the UK by 2037 but also the creation of 7.2 million jobs, and the potential automation of 400 to 800 million jobs by 2030.', 'Elon Musk and Stephen Hawking have warned about the looming dangers of AI, suggesting it could be the greatest threat to humanity.', 'AI may replace professionals in various fields, such as fast food joints and radiologists in hospitals.', "AI's exponential rate of improvement is emphasized, with Musk stating that AI is far more dangerous than nuclear warheads.", 'The risks associated with AI becoming conscious and responsive, as well as the potential for misuse by individuals for nefarious purposes, are highlighted.', "The incident involving Facebook's chatbot experiment, where the AIs developed their own language for communication, is presented as a concerning example of AI's unpredictability.", 'AI must be approached with healthy skepticism to avoid potential dangers, as even small issues can escalate into hazardous situations.', 'Understanding and responsible utilization of AI is crucial to ensure it positively impacts lives.', 'Brief history of AI, including milestones and advancements from 1956 to 2018, showcasing the rapid progress of AI technology.', 'Introduction to smart homes and key features enabled by AI, such as voice-controlled appliances, climate-adjusting sensors, and remote control capabilities.', 'Explanation of the types of AI, including purely reactive, limited memory, theory of mind, and self-awareness, with examples and characteristics.']}, {'end': 3783.048, 'segs': [{'end': 2826.178, 'src': 'embed', 'start': 2799.542, 'weight': 0, 'content': [{'end': 2804.544, 'text': 'if they had one person doing that, that would take them a year just to do what they need to have posted yesterday.', 'start': 2799.542, 'duration': 5.002}, {'end': 2807.025, 'text': 'and, of course, our virtual assistants.', 'start': 2804.544, 'duration': 2.481}, {'end': 2812.068, 'text': "I don't know about you, but I love mine Kind of like having a private secretary without having a private secretary.", 'start': 2807.025, 'duration': 5.043}, {'end': 2814.19, 'text': 'Future of artificial intelligence.', 'start': 2812.228, 'duration': 1.962}, {'end': 2818.713, 'text': "If we see where it's at now, commercially and business-wise, then where is it going?", 'start': 2814.51, 'duration': 4.203}, {'end': 2826.178, 'text': 'Of course, the imagination is a limit on this one, but you can already see the development in the world today for automated transportation.', 'start': 2818.893, 'duration': 7.285}], 'summary': 'Using virtual assistants can save time; ai shows potential for automated transportation.', 'duration': 26.636, 'max_score': 2799.542, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA2799542.jpg'}, {'end': 3082.185, 'src': 'embed', 'start': 3048.024, 'weight': 1, 'content': [{'end': 3051.865, 'text': "It notices that there's a certain setup with Facebook and it's able to replace it.", 'start': 3048.024, 'duration': 3.841}, {'end': 3056.606, 'text': 'And they have like vote baiting, react baiting, share baiting.', 'start': 3052.425, 'duration': 4.181}, {'end': 3059.447, 'text': 'They have all these different, these are kind of general titles.', 'start': 3056.866, 'duration': 2.581}, {'end': 3062.808, 'text': 'But there certainly are a lot of way of baiting you to go in there and click on something.', 'start': 3059.667, 'duration': 3.141}, {'end': 3064.269, 'text': 'So they fed all this.', 'start': 3063.348, 'duration': 0.921}, {'end': 3065.93, 'text': 'This data was fed into the machine.', 'start': 3064.429, 'duration': 1.501}, {'end': 3067.552, 'text': 'And then they have the new post.', 'start': 3066.251, 'duration': 1.301}, {'end': 3070.975, 'text': 'The new post comes up that takes over part of the Facebook setup.', 'start': 3067.572, 'duration': 3.403}, {'end': 3072.076, 'text': "And that's what you're looking at.", 'start': 3071.195, 'duration': 0.881}, {'end': 3075.359, 'text': "You're looking at this new post that's replaced, like a virus has replaced that.", 'start': 3072.096, 'duration': 3.263}, {'end': 3082.185, 'text': 'So what Facebook did to eliminate this is they start scanning for keywords and phrases like this and checks the click-through rate.', 'start': 3075.539, 'duration': 6.646}], 'summary': 'Facebook detected and addressed baiting by scanning for keywords and phrases and monitoring click-through rates.', 'duration': 34.161, 'max_score': 3048.024, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA3048024.jpg'}], 'start': 2633.114, 'title': 'Ai and machine learning', 'summary': "Delves into the future of ai, covering concepts like memory, theory of mind, and self-aware machines, and explores current and future ai applications in various sectors. additionally, it discusses the use of automation and machine learning in social media, with examples of their impact. furthermore, it provides insights into the basics of machine learning, emphasizing its potential and challenges in today's automated world.", 'chapters': [{'end': 2984.528, 'start': 2633.114, 'title': 'Ai in the future: memory, theory of mind, and self-aware machines', 'summary': 'Discusses the future of ai, including the concepts of memory, theory of mind, and self-aware machines, as well as current and future applications of artificial intelligence, such as in banking, customer support, and transportation.', 'duration': 351.414, 'highlights': ['AI in Current Applications: Banking and Customer Support The chapter highlights the current use of AI in banking for fraud detection and customer support, as well as its matured presence in commercial business over the last half a decade.', 'Future of AI: Automated Transportation and Robotics The future of AI is envisioned to include automated transportation, augmented human-robot interactions, and the use of home robots to assist elderly individuals with day-to-day tasks.', 'Types of AI: Memory, Theory of Mind, and Self-aware Machines The discussion covers different types of AI, including memory-based decision-making, theory of mind for understanding emotions, and the potential development of self-aware machines, resembling characters from sci-fi movies.']}, {'end': 3144.005, 'start': 2984.528, 'title': 'Automation and ai in social media', 'summary': "Discusses the use of automation and machine learning in social media platforms like facebook to eliminate engagement bait and spam posts, with examples of facebook's approach and google's alphago defeating the world's number one go player.", 'duration': 159.477, 'highlights': ["Facebook's use of machine learning to detect and eliminate engagement bait and spam posts, improving user experience on the platform. Facebook reviewed and categorized hundreds of thousands of posts to train a machine learning model that detects different types of engagement bait, leading to a significant improvement in user experience.", "Google's AlphaGo defeating the world's number one Go player in 2017, showcasing the capabilities of AI in mastering complex games. Google's AlphaGo defeated the world's number one Go player, demonstrating the advancement of AI in mastering complex games like Go.", 'The impact of automation and AI in improving user experience by eliminating spam posts and engagement bait on social media platforms like Facebook. The use of automation and AI in social media platforms, such as Facebook, has significantly improved user experience by eliminating spam posts and engagement bait, allowing users to enjoy a more personalized and spam-free experience.']}, {'end': 3783.048, 'start': 3144.265, 'title': 'Machine learning basics', 'summary': "Discusses the basics of machine learning, including the concept, process, and divisions, emphasizing its potential and challenges in today's automated world.", 'duration': 638.783, 'highlights': ['Machine learning is the science of making computers learn and act like humans by feeding data and information without being explicitly programmed. Defines machine learning and its goal of making computers learn and act like humans, without explicit programming.', 'The process of machine learning involves defining objectives, collecting and preparing data, selecting and training algorithms, testing the model, running predictions, and deploying the model. Outlines the step-by-step process of machine learning, including data collection, algorithm selection, testing, and deployment.', 'The divisions of machine learning include classification, regression, anomaly detection, and clustering, each serving specific purposes in data analysis and prediction. Explains the major divisions in machine learning, such as classification, regression, anomaly detection, and clustering, highlighting their relevance in data analysis and prediction.', 'Supervised learning enables machines to classify and predict based on labeled data, while unsupervised learning finds hidden patterns in unlabeled data, and reinforcement learning focuses on learning through actions and results in an environment. Describes the different types of machine learning, including supervised, unsupervised, and reinforcement learning, and their respective functions in data processing and behavior learning.']}], 'duration': 1149.934, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA2633114.jpg', 'highlights': ['The future of AI includes automated transportation, augmented human-robot interactions, and home robots for elderly assistance.', 'AI in banking for fraud detection and customer support has matured over the last half a decade.', 'Different types of AI include memory-based decision-making, theory of mind for understanding emotions, and potential development of self-aware machines.', "Facebook's use of machine learning to detect and eliminate engagement bait and spam posts improved user experience on the platform.", "Google's AlphaGo defeating the world's number one Go player in 2017 showcased the capabilities of AI in mastering complex games.", 'The impact of automation and AI in improving user experience by eliminating spam posts and engagement bait on social media platforms.', '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 algorithms, testing the model, running predictions, and deploying the model.', 'The divisions of machine learning include classification, regression, anomaly detection, and clustering, each serving specific purposes in data analysis and prediction.', 'Supervised learning enables machines to classify and predict based on labeled data, while unsupervised learning finds hidden patterns in unlabeled data, and reinforcement learning focuses on learning through actions and results in an environment.']}, {'end': 4913.811, 'segs': [{'end': 4343.409, 'src': 'embed', 'start': 4268.342, 'weight': 0, 'content': [{'end': 4276.805, 'text': 'And if you remember, I mentioned earlier that the linear regression line has to pass through the means value, the one that we showed earlier.', 'start': 4268.342, 'duration': 8.463}, {'end': 4285.689, 'text': "We can just flip back up there to that graph, and you can see right here, there's our means value, which is 3, x equals 3, and y equals 2.8.", 'start': 4277.066, 'duration': 8.623}, {'end': 4290.931, 'text': 'And since we know that value, we can simply plug that into formula.', 'start': 4285.689, 'duration': 5.242}, {'end': 4293.072, 'text': 'y equals 0.2 X plus C.', 'start': 4290.931, 'duration': 2.141}, {'end': 4293.913, 'text': 'so we plug that in.', 'start': 4293.072, 'duration': 0.841}, {'end': 4299.537, 'text': 'we get 2.8, equals 0.2 times 3 plus C, and you can just solve for C.', 'start': 4293.913, 'duration': 5.624}, {'end': 4307.641, 'text': 'so now we know that our coefficient equals 2.2, and once we have all that, we can go ahead and plot our regression line.', 'start': 4299.537, 'duration': 8.104}, {'end': 4309.902, 'text': 'y equals 0.2 times x plus 2.2..', 'start': 4307.641, 'duration': 2.261}, {'end': 4314.825, 'text': 'And then from this equation, we can compute new values.', 'start': 4309.902, 'duration': 4.923}, {'end': 4319.027, 'text': "So let's predict the values of y using x equals 1, 2, 3, 4, 5, and plot the points.", 'start': 4315.045, 'duration': 3.982}, {'end': 4323.671, 'text': 'Remember the 1, 2, 3, 4, 5 was our original x values.', 'start': 4320.868, 'duration': 2.803}, {'end': 4327.634, 'text': "So now we're going to see what y thinks they are, not what they actually are.", 'start': 4324.011, 'duration': 3.623}, {'end': 4332.199, 'text': 'And we plug those in, we get y designated with y of p.', 'start': 4328.075, 'duration': 4.124}, {'end': 4337.944, 'text': 'You can see that x equals 1 equals 2.4, x equals 2 equals 2.6, and so on and so on.', 'start': 4332.199, 'duration': 5.745}, {'end': 4343.409, 'text': "So we have our y predicted values of what we think it's going to be when we plug those numbers in.", 'start': 4338.204, 'duration': 5.205}], 'summary': 'Linear regression predicts y values for given x values based on the regression line equation y = 0.2x + 2.2.', 'duration': 75.067, 'max_score': 4268.342, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA4268342.jpg'}, {'end': 4720.434, 'src': 'embed', 'start': 4649.523, 'weight': 1, 'content': [{'end': 4657.949, 'text': 'And we simply have a log squared of p over p plus n minus n over p plus n times the log squared of n of p plus n.', 'start': 4649.523, 'duration': 8.426}, {'end': 4663.213, 'text': "But let's break that down and see what it actually looks like when we're computing that from the computer script side.", 'start': 4657.949, 'duration': 5.264}, {'end': 4667.236, 'text': 'Entropy of a target class of the data set is the whole entropy.', 'start': 4663.874, 'duration': 3.362}, {'end': 4669.257, 'text': 'So we have entropy play golf.', 'start': 4667.556, 'duration': 1.701}, {'end': 4677.982, 'text': 'And when we look at this, if we go back to the data, you can simply count how many yeses and no in our complete data set for playing golf days.', 'start': 4669.777, 'duration': 8.205}, {'end': 4684.526, 'text': 'In our complete set, we find we have 5 days we did play golf and 9 days we did not play golf.', 'start': 4678.582, 'duration': 5.944}, {'end': 4689.201, 'text': 'And so our i equals, if you add those together, 9 plus 5 is 14.', 'start': 4685.106, 'duration': 4.095}, {'end': 4693.004, 'text': 'And so our i equals 5 over 14 and 9 over 14.', 'start': 4689.201, 'duration': 3.803}, {'end': 4695.626, 'text': "That's our p and n values that we plug into that formula.", 'start': 4693.004, 'duration': 2.622}, {'end': 4701.751, 'text': 'And you can go 5 over 14 equals 0.36, 9 over 14 equals 0.64.', 'start': 4696.106, 'duration': 5.645}, {'end': 4707.896, 'text': 'And when you do the whole equation, you get the minus 0.36 log root squared of 0.36 minus 0.64 log squared root of 0.64.', 'start': 4701.751, 'duration': 6.145}, {'end': 4708.856, 'text': 'And we get a set value.', 'start': 4707.896, 'duration': 0.96}, {'end': 4716.022, 'text': 'We get 0.94.', 'start': 4708.877, 'duration': 7.145}, {'end': 4720.434, 'text': "So we now have a full entropy value for the whole set of data that we're working with.", 'start': 4716.022, 'duration': 4.412}], 'summary': 'Computed entropy for golf days dataset: 5/14 and 9/14, resulting in entropy value of 0.94.', 'duration': 70.911, 'max_score': 4649.523, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA4649523.jpg'}], 'start': 3783.088, 'title': 'Machine learning fundamentals and tools', 'summary': 'Covers reinforcement learning, supervised and unsupervised learning, and provides detailed insights into linear regression as a fundamental machine learning algorithm, including its variations and applications. it also discusses the mathematical implementation of linear regression, error minimization, and the use of decision trees to make predictions based on weather data.', 'chapters': [{'end': 4068.456, 'start': 3783.088, 'title': 'Machine learning basics and linear regression', 'summary': 'Introduces the basics of reinforcement learning, supervised and unsupervised learning, and provides detailed insights into linear regression as a fundamental machine learning algorithm, including its variations and applications.', 'duration': 285.368, 'highlights': ['Linear regression is a fundamental machine learning algorithm that assumes a linear relationship between input and output variables, illustrated with examples and graph representations. Linear regression is a well-known algorithm that assumes a linear relationship between input and output variables, demonstrated through examples of predicting distance traveled based on speed, with clear graphical representations.', 'Supervised and unsupervised learning are fundamental concepts in machine learning, with supervised learning utilizing labeled data and unsupervised learning focusing on finding hidden structures in unlabeled data. The distinction between supervised and unsupervised learning is explained, emphasizing the use of labeled data in supervised learning and the discovery of hidden structures in unlabeled data in unsupervised learning.', 'Introduction to reinforcement learning as a method similar to human learning and the significance of machines learning how to learn in the context of AI and machine learning. Reinforcement learning is likened to human learning and its importance in teaching machines how to learn is highlighted, offering a significant advancement in the field of AI and machine learning.']}, {'end': 4444.035, 'start': 4068.897, 'title': 'Linear regression and error minimization', 'summary': 'Discusses the relationship between speed and time, the mathematical implementation of linear regression using a dataset, calculating the regression equation, and error minimization in a linear regression model, aiming to reduce the distance between the data points and the regression line.', 'duration': 375.138, 'highlights': ['The chapter discusses the relationship between speed and time, the mathematical implementation of linear regression using a dataset, calculating the regression equation, and error minimization in a linear regression model. The transcript explores the relationship between speed and time, the mathematical implementation of linear regression using a dataset with x and y values, calculating the regression equation to find the best fit line, and error minimization in a linear regression model.', 'The linear regression model aims to minimize the error by reducing the distance between the data points and the regression line. The chapter emphasizes the goal of minimizing the error in a linear regression model by reducing the distance between the data points and the regression line, using methods such as sum of squared errors, sum of absolute errors, and root mean square error.', 'The discussion also mentions the complexity of linear regression in higher dimensions and introduces decision trees as a different approach to problem-solving. It introduces the complexity of linear regression in higher dimensions and explains that the linear regression model can be extended to multiple dimensions, and introduces decision trees as a different approach to problem-solving.']}, {'end': 4913.811, 'start': 4444.702, 'title': 'Decision making with decision trees', 'summary': 'Explores the use of decision trees to make predictions based on weather data, calculating entropy and information gain to determine the best splits, with an emphasis on maximizing information gain and minimizing entropy.', 'duration': 469.109, 'highlights': ['The importance of using decision trees for making predictions based on weather data is highlighted, with the potential to provide suggestions for activities like playing golf or making purchases. Predicting activities based on weather data, providing suggestions for activities, such as playing golf or making purchases.', 'The process of calculating entropy and information gain is explained, emphasizing the need for low entropy and high information gain to make effective decisions. Explanation of entropy and information gain, emphasis on low entropy and high information gain.', 'The calculation of entropy for different attributes such as outlook, temperature, humidity, and wind is demonstrated, with a focus on maximizing information gain for effective decision making. Demonstration of entropy calculation for different attributes, emphasis on maximizing information gain.', 'The concept of decision tree building is discussed, with an emphasis on selecting the attribute with the largest information gain as the root node for effective decision making. Discussion of decision tree building, emphasis on selecting attributes with the largest information gain.']}], 'duration': 1130.723, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA3783088.jpg', 'highlights': ['Reinforcement learning is likened to human learning and its importance in teaching machines how to learn is highlighted, offering a significant advancement in the field of AI and machine learning.', 'The chapter emphasizes the goal of minimizing the error in a linear regression model by reducing the distance between the data points and the regression line, using methods such as sum of squared errors, sum of absolute errors, and root mean square error.', 'The distinction between supervised and unsupervised learning is explained, emphasizing the use of labeled data in supervised learning and the discovery of hidden structures in unlabeled data in unsupervised learning.', 'The linear regression model aims to minimize the error by reducing the distance between the data points and the regression line.', 'Introduction to reinforcement learning as a method similar to human learning and the significance of machines learning how to learn in the context of AI and machine learning.', 'The importance of using decision trees for making predictions based on weather data is highlighted, with the potential to provide suggestions for activities like playing golf or making purchases.', 'The process of calculating entropy and information gain is explained, emphasizing the need for low entropy and high information gain to make effective decisions.', 'The concept of decision tree building is discussed, with an emphasis on selecting the attribute with the largest information gain as the root node for effective decision making.']}, {'end': 6868.245, 'segs': [{'end': 5002.881, 'src': 'embed', 'start': 4973.925, 'weight': 3, 'content': [{'end': 4978.849, 'text': "Let's understand how multiple linear regression works by implementing it in Python.", 'start': 4973.925, 'duration': 4.924}, {'end': 4985.874, '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': 4979.089, 'duration': 6.785}, {'end': 4987.955, 'text': "we're going to start looking at the expenditure of the company.", 'start': 4985.874, 'duration': 2.081}, {'end': 4989.136, 'text': "We're going to go back to that.", 'start': 4988.035, 'duration': 1.101}, {'end': 4990.517, 'text': "We're going to predict its profit.", 'start': 4989.296, 'duration': 1.221}, {'end': 4998.699, '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': 4990.757, 'duration': 7.942}, {'end': 5002.881, 'text': "And from there, we're going to see if we can figure out what the profit of that company is going to be.", 'start': 4998.919, 'duration': 3.962}], 'summary': 'Learning multiple linear regression in python to predict company profit based on various factors.', 'duration': 28.956, 'max_score': 4973.925, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA4973925.jpg'}, {'end': 5333.196, 'src': 'embed', 'start': 5293.76, 'weight': 9, 'content': [{'end': 5294.801, 'text': "But we don't want to look at this one.", 'start': 5293.76, 'duration': 1.041}, {'end': 5296.842, 'text': 'We want to look at something we can read rather easily.', 'start': 5294.841, 'duration': 2.001}, {'end': 5300.624, 'text': "So let's flip back and take a look at that top part, the first five rows.", 'start': 5296.962, 'duration': 3.662}, {'end': 5305.687, 'text': "Now, as nice as this format is where I can see the data, to me it doesn't mean a whole lot.", 'start': 5300.744, 'duration': 4.943}, {'end': 5312.013, 'text': "Maybe you're an expert in business and investments and you understand what $165,349.20 compared to the administration cost of $136,897.80, so on,", 'start': 5306.107, 'duration': 5.906}, {'end': 5312.854, 'text': 'so on, helps to create the profit of $192,261.83..', 'start': 5312.013, 'duration': 0.841}, {'end': 5314.276, 'text': 'That makes no sense to me whatsoever.', 'start': 5312.854, 'duration': 1.422}, {'end': 5315.036, 'text': 'No pun intended.', 'start': 5314.536, 'duration': 0.5}, {'end': 5333.196, 'text': "So let's flip back here and take a look at our next set of code, where we're going to graph it,", 'start': 5329.511, 'duration': 3.685}], 'summary': 'Struggling to interpret business data; aiming to graph it for clarity.', 'duration': 39.436, 'max_score': 5293.76, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA5293760.jpg'}, {'end': 5530.333, 'src': 'embed', 'start': 5502.645, 'weight': 4, 'content': [{'end': 5507.43, 'text': 'This creates a class that we can reuse for transferring the labels back and forth.', 'start': 5502.645, 'duration': 4.785}, {'end': 5508.091, 'text': 'Now about.', 'start': 5507.75, 'duration': 0.341}, {'end': 5510.113, 'text': 'now you should ask what labels are we talking about?', 'start': 5508.091, 'duration': 2.022}, {'end': 5514.277, 'text': "Let's go take a look at the data we processed before and see what I'm talking about here.", 'start': 5510.273, 'duration': 4.004}, {'end': 5520.764, 'text': 'If you remember when we did the companies.head and we printed the top five rows of data, we have our columns going across.', 'start': 5514.577, 'duration': 6.187}, {'end': 5523.947, 'text': 'We have column 0, which is R&D spending.', 'start': 5521.124, 'duration': 2.823}, {'end': 5530.333, 'text': 'column 1 which is administration, column 2 which is marketing spending, and column 3 is state.', 'start': 5524.287, 'duration': 6.046}], 'summary': 'Creating a reusable class for transferring labels, referring to specific columns in a dataset.', 'duration': 27.688, 'max_score': 5502.645, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA5502645.jpg'}, {'end': 5732.721, 'src': 'embed', 'start': 5693.001, 'weight': 1, 'content': [{'end': 5696.302, 'text': "And I'll go ahead and shrink it down a size or two so it all fits on one line.", 'start': 5693.001, 'duration': 3.301}, {'end': 5702.228, 'text': "So from the sklearn module selection, we're going to import train test split.", 'start': 5696.502, 'duration': 5.726}, {'end': 5705.532, 'text': "And you'll see that we've created four completely different variables.", 'start': 5702.489, 'duration': 3.043}, {'end': 5712.579, 'text': 'We have capital X train, capital X test, smallercase y train, smallercase y test.', 'start': 5705.692, 'duration': 6.887}, {'end': 5718.446, 'text': "That is the standard way that they usually reference these when we're doing different models.", 'start': 5713.28, 'duration': 5.166}, {'end': 5723.516, 'text': 'You usually see that, a capital X, and you see the train and the test and the lowercase y.', 'start': 5719.073, 'duration': 4.443}, {'end': 5725.697, 'text': 'What this is, is x is our data going in.', 'start': 5723.516, 'duration': 2.181}, {'end': 5728.619, 'text': "That's our R&D spin, our administration, our marketing.", 'start': 5725.957, 'duration': 2.662}, {'end': 5731.841, 'text': "And then y, which we're training, is the answer.", 'start': 5728.939, 'duration': 2.902}, {'end': 5732.721, 'text': "That's the profit.", 'start': 5731.941, 'duration': 0.78}], 'summary': 'Using train test split from sklearn module to split data for training and testing, with variables x train, x test, y train, y test.', 'duration': 39.72, 'max_score': 5693.001, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA5693001.jpg'}, {'end': 5989.297, 'src': 'embed', 'start': 5953.695, 'weight': 0, 'content': [{'end': 5958.94, 'text': 'And if we can do the regressor coefficient, we can also do the regressor intercept.', 'start': 5953.695, 'duration': 5.245}, {'end': 5961.102, 'text': "Let's run that and take a look at that.", 'start': 5959.5, 'duration': 1.602}, {'end': 5963.624, 'text': 'This all came from the multiple regression model.', 'start': 5961.342, 'duration': 2.282}, {'end': 5967.288, 'text': "And we'll flip over so you can remember where this is going into and where it's coming from.", 'start': 5963.864, 'duration': 3.424}, {'end': 5975.831, 'text': 'You can see the formula down here where y equals m1 times x1 plus m2 times x2 and so on and so on plus c, the coefficient.', 'start': 5967.748, 'duration': 8.083}, {'end': 5978.272, 'text': 'So these variables fit right into this formula.', 'start': 5976.172, 'duration': 2.1}, {'end': 5989.297, 'text': 'y equals slope 1 times column 1 variable plus slope 2 times column 2 variable all the way to the m to the n and x to the n plus c, the coefficient.', 'start': 5978.793, 'duration': 10.504}], 'summary': 'Discussion on regressor coefficient and intercept in multiple regression model.', 'duration': 35.602, 'max_score': 5953.695, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA5953695.jpg'}, {'end': 6031.101, 'src': 'embed', 'start': 6002.828, 'weight': 2, 'content': [{'end': 6004.609, 'text': 'Boy, it gets kind of complicated when you look at it.', 'start': 6002.828, 'duration': 1.781}, {'end': 6007.051, 'text': "This is why we don't do this by hand anymore.", 'start': 6004.769, 'duration': 2.282}, {'end': 6011.515, 'text': 'This is why we have the computer to make these calculations easy to understand and calculate.', 'start': 6007.151, 'duration': 4.364}, {'end': 6015.876, 'text': "Now I told you that was a short detour, and we're coming towards the end of our script.", 'start': 6011.795, 'duration': 4.081}, {'end': 6021.878, 'text': "As you remember, from the beginning I said if we're going to divide this information, we have to make sure it's a valid model,", 'start': 6016.156, 'duration': 5.722}, {'end': 6024.399, 'text': 'that this model works and understand how good it works.', 'start': 6021.878, 'duration': 2.521}, {'end': 6031.101, 'text': "So calculating the R squared value, that's what we're going to use to predict how good our prediction is.", 'start': 6024.719, 'duration': 6.382}], 'summary': 'Using computer for calculations, ensuring valid model, calculating r squared value for prediction.', 'duration': 28.273, 'max_score': 6002.828, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA6002828.jpg'}, {'end': 6565.745, 'src': 'embed', 'start': 6536.624, 'weight': 5, 'content': [{'end': 6539.486, 'text': 'So when we were looking at the data, we had five columns of data.', 'start': 6536.624, 'duration': 2.862}, {'end': 6543.469, 'text': "And then let's take one more step to explore the data using Python.", 'start': 6539.686, 'duration': 3.783}, {'end': 6546.751, 'text': "And now that we've taken a look at the length and the shape,", 'start': 6543.749, 'duration': 3.002}, {'end': 6553.976, 'text': "let's go ahead and use the pandas module for head another beautiful thing in the data set that we can utilize.", 'start': 6546.751, 'duration': 7.225}, {'end': 6557.799, 'text': "So let's put that on our sheet here, and we have print data set.", 'start': 6553.996, 'duration': 3.803}, {'end': 6565.745, 'text': 'and balanceData.head, and this is a pandas print statement of its own, so it has its own print feature in there.', 'start': 6558.539, 'duration': 7.206}], 'summary': 'Data analysis done with python, utilizing pandas module, with five columns of data explored.', 'duration': 29.121, 'max_score': 6536.624, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA6536624.jpg'}], 'start': 4914.111, 'title': 'Multiple linear regression and decision tree in python', 'summary': 'Covers the implementation of multiple linear regression in python using libraries like numpy, pandas, matplotlib, and seaborn to predict profit, as well as the creation and evaluation of decision tree algorithm, handling deprecation warning, and exploration of a dataset with 1000 lines and 5 columns.', 'chapters': [{'end': 5333.196, 'start': 4914.111, 'title': 'Multiple linear regression in python', 'summary': "Explains the concept of multiple linear regression with multiple inputs and demonstrates its implementation in python to predict a company's profit based on factors like r&d, administration costs, and marketing costs, utilizing libraries like numpy, pandas, matplotlib, and seaborn.", 'duration': 419.085, 'highlights': ["The chapter explains the concept of multiple linear regression with multiple inputs and demonstrates its implementation in Python to predict a company's profit based on factors like R&D, administration costs, and marketing costs. multiple linear regression, implementation in Python, predicting company's profit, factors including R&D, administration costs, marketing costs", 'The implementation involves importing basic libraries like numpy, pandas, matplotlib, and seaborn for data analysis and visualization. importing numpy, pandas, matplotlib, seaborn for data analysis and visualization', 'The process includes loading the dataset, extracting independent and dependent variables, and visualizing the data using pandas and matplotlib. loading dataset, extracting variables, visualizing data using pandas and matplotlib']}, {'end': 5772.49, 'start': 5333.196, 'title': 'Data visualization and linear regression models', 'summary': 'Explores the visualization of data using seaborn and the creation of linear regression models by preprocessing data and splitting it into training and testing sets, aiming to predict profit with 20% of the data being held for testing.', 'duration': 439.294, 'highlights': ['The chapter explores the visualization of data using Seaborn and the creation of linear regression models The chapter delves into using Seaborn for visualizing data and creating linear regression models to predict profit.', 'Preprocessing data and splitting it into training and testing sets The process includes using sklearn preprocessing for label encoding and one hot encoding to transform categorical data, then splitting the data into training and testing sets using train test split.', "Aim to predict profit with 20% of the data being held for testing The goal is to predict profit using a linear regression model, with 20% of the data being set aside for testing the model's performance."]}, {'end': 6311.774, 'start': 5772.89, 'title': 'Linear regression model & decision tree algorithm', 'summary': "Focuses on creating a linear regression model using sklearn.linear model, explaining the process of fitting the data to the model, predicting test set results, calculating coefficients and intercepts, and evaluating the model's performance using r squared value. it also introduces the decision tree algorithm in python and the initial steps for its implementation.", 'duration': 538.884, 'highlights': ["The linear regression model is created using sklearn.linear_model to fit the data, predict test set results, calculate coefficients and intercepts, and evaluate the model's performance with R squared value of .9352, indicating a high level of prediction accuracy.", 'Introduction of decision tree algorithm in Python and initial steps for its implementation, including importing necessary packages like numpy and pandas, splitting the data using train test split, and importing decision tree classifier and accuracy score from sklearn package.', 'Explanation of the process of importing necessary packages such as numpy and pandas, splitting the data using train test split, and importing decision tree classifier and accuracy score from sklearn package as initial steps for implementing the decision tree algorithm in Python.']}, {'end': 6868.245, 'start': 6312.154, 'title': 'Handling deprecation warning and exploring data in python', 'summary': 'Discusses the process of handling a deprecation warning in python, exploring a dataset using pandas in python, and building a decision tree classifier using sklearn, with key points including the deprecation of cross validation, the exploration of a dataset with 1000 lines and 5 columns, and the training of a decision tree classifier with specified parameters.', 'duration': 556.091, 'highlights': ["Sklearn's cross validation is deprecated, to be replaced with model selection, highlighting the need to address deprecation warnings in code.", "The dataset being explored contains 1000 lines of data with 5 columns, facilitating the understanding of the dataset's structure and size.", 'The decision tree classifier is trained with a max depth of 3 and a minimum of 5 samples per leaf, demonstrating the customization of parameters for the classifier.']}], 'duration': 1954.134, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA4914111.jpg', 'highlights': ["Implementation of multiple linear regression in Python to predict company's profit based on R&D, administration costs, and marketing costs", 'Importing numpy, pandas, matplotlib, and seaborn for data analysis and visualization', 'Exploration of data visualization using Seaborn and creation of linear regression models', 'Preprocessing data using sklearn preprocessing for label encoding and one hot encoding', 'Creation of linear regression model with 20% of the data held for testing', "Evaluation of linear regression model's performance with R squared value of .9352", 'Introduction and initial steps for implementing the decision tree algorithm in Python', "Addressing deprecation warnings in code related to Sklearn's cross validation", 'Exploration of a dataset with 1000 lines and 5 columns', 'Training decision tree classifier with customized parameters: max depth of 3 and minimum of 5 samples per leaf']}, {'end': 9167.256, 'segs': [{'end': 7754.977, 'src': 'embed', 'start': 7726.024, 'weight': 1, 'content': [{'end': 7731.225, 'text': 'Remember, you can always post this in the comments and request the data files for these,', 'start': 7726.024, 'duration': 5.201}, {'end': 7735.986, 'text': 'either in the comments here on the YouTube video or go to simplylearn.com and request that.', 'start': 7731.225, 'duration': 4.761}, {'end': 7740.667, 'text': "The cars CSV, I put it in the same folder as the code that I've stored.", 'start': 7736.346, 'duration': 4.321}, {'end': 7744.768, 'text': "So my Python code is stored in the same folder, so I don't have to put the full path.", 'start': 7740.747, 'duration': 4.021}, {'end': 7749.212, 'text': 'If you store them in different folders, you do have to change this and double-check your name variables.', 'start': 7745.288, 'duration': 3.924}, {'end': 7754.977, 'text': "And we'll go ahead and run this, and we've chosen dataset arbitrarily, because it's a dataset we're importing.", 'start': 7749.552, 'duration': 5.425}], 'summary': 'Instructions for accessing data files and storing code in the same folder to avoid using full path.', 'duration': 28.953, 'max_score': 7726.024, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA7726024.jpg'}, {'end': 7789.202, 'src': 'embed', 'start': 7762.464, 'weight': 9, 'content': [{'end': 7766.088, 'text': "This is the one that we're going to try to figure out what's going on with.", 'start': 7762.464, 'duration': 3.624}, {'end': 7772.101, 'text': "And then there's a number of ways to do this, but we'll do it in a simple loop so you can actually see what's going on.", 'start': 7766.638, 'duration': 5.463}, {'end': 7775.762, 'text': "So we'll do for i in x.columns.", 'start': 7772.181, 'duration': 3.581}, {'end': 7780.911, 'text': "So we're going to go through each of the columns and A lot of times it's important.", 'start': 7776.103, 'duration': 4.808}, {'end': 7789.202, 'text': "I'll make lists of the columns and do this because I might remove certain columns or there might be columns that I want to be processed differently.", 'start': 7780.911, 'duration': 8.291}], 'summary': 'Analyzing columns in a loop to process data efficiently.', 'duration': 26.738, 'max_score': 7762.464, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA7762464.jpg'}, {'end': 7879.553, 'src': 'embed', 'start': 7855.465, 'weight': 5, 'content': [{'end': 7863.288, 'text': "And we'll print and then we take x is null and this returns a set of the null value or how many lines are null,", 'start': 7855.465, 'duration': 7.823}, {'end': 7865.908, 'text': "and we'll just sum that up to see what that looks like.", 'start': 7863.288, 'duration': 2.62}, {'end': 7872.451, 'text': 'And so when I run this, and so with the x, what we want to do is we want to remove the last column, because it had the models.', 'start': 7865.989, 'duration': 6.462}, {'end': 7876.052, 'text': "That's what we're trying to see, if we can cluster these things and figure out the models.", 'start': 7872.491, 'duration': 3.561}, {'end': 7879.553, 'text': 'There is so many different ways to sort the x out.', 'start': 7876.532, 'duration': 3.021}], 'summary': 'Analyzing null values and removing the last column to cluster and identify models in dataset.', 'duration': 24.088, 'max_score': 7855.465, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA7855465.jpg'}, {'end': 7932.574, 'src': 'embed', 'start': 7903.182, 'weight': 3, 'content': [{'end': 7907.302, 'text': 'And if I, let me just put this down here and print X.', 'start': 7903.182, 'duration': 4.12}, {'end': 7908.863, 'text': "It's a capital X we chose.", 'start': 7907.302, 'duration': 1.561}, {'end': 7909.783, 'text': 'And I run this.', 'start': 7909.223, 'duration': 0.56}, {'end': 7911.003, 'text': "You can see it's just the values.", 'start': 7909.803, 'duration': 1.2}, {'end': 7917.367, 'text': "We could also take out the values and it's not going to return anything because there's no values connected to it.", 'start': 7911.684, 'duration': 5.683}, {'end': 7923.47, 'text': 'What I like to do with this is, instead of doing the iLocation, which does integers,', 'start': 7917.847, 'duration': 5.623}, {'end': 7932.574, 'text': "more common is to come in here and we have our data set and we're going to do data set dot columns.", 'start': 7923.47, 'duration': 9.104}], 'summary': 'Printing the capital x returns values, while accessing columns of the data set is more common.', 'duration': 29.392, 'max_score': 7903.182, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA7903182.jpg'}, {'end': 7983.666, 'src': 'embed', 'start': 7952.32, 'weight': 8, 'content': [{'end': 7959.221, 'text': 'So the way to get rid of the brand would be to do data columns of everything but the last one, minus one.', 'start': 7952.32, 'duration': 6.901}, {'end': 7962.061, 'text': "So now if I print this, you'll see the brand disappears.", 'start': 7959.561, 'duration': 2.5}, {'end': 7970.943, 'text': "And so I can actually just take dataset.columns minus one, and I'll put it right in here for the columns we're going to look at.", 'start': 7962.942, 'duration': 8.001}, {'end': 7976.742, 'text': "And let's unmark this and unmark this.", 'start': 7972.6, 'duration': 4.142}, {'end': 7983.666, 'text': 'And now if I do an x.head, I now have a new data frame.', 'start': 7976.762, 'duration': 6.904}], 'summary': 'Removing the brand column results in a new data frame.', 'duration': 31.346, 'max_score': 7952.32, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA7952320.jpg'}, {'end': 8051.992, 'src': 'embed', 'start': 8027.945, 'weight': 10, 'content': [{'end': 8034.067, 'text': "If I'm working with a lot of these things, I remember them, but depending on where I'm at, what I'm doing, I usually have to look it up.", 'start': 8027.945, 'duration': 6.122}, {'end': 8035.607, 'text': 'And we run that.', 'start': 8034.627, 'duration': 0.98}, {'end': 8037.168, 'text': 'Oops, I must have missed something in here.', 'start': 8035.627, 'duration': 1.541}, {'end': 8038.568, 'text': 'Let me double check my spelling.', 'start': 8037.228, 'duration': 1.34}, {'end': 8043.29, 'text': "And when I double check my spelling, you'll see I missed the first underscore in the convert objects.", 'start': 8038.988, 'duration': 4.302}, {'end': 8051.992, 'text': "When I run this, it now has everything converted into a numeric value, because that's what we're going to be working with is numeric values down here.", 'start': 8043.69, 'duration': 8.302}], 'summary': 'Working with numeric values, but needs to look up details frequently.', 'duration': 24.047, 'max_score': 8027.945, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA8027945.jpg'}, {'end': 8314.508, 'src': 'embed', 'start': 8255.438, 'weight': 2, 'content': [{'end': 8258.54, 'text': 'I always forget to capitalize the k and the m when I do this.', 'start': 8255.438, 'duration': 3.102}, {'end': 8261.38, 'text': "So it's capital K, capital M, kmeans.", 'start': 8259.1, 'duration': 2.28}, {'end': 8269.431, 'text': "And we'll go ahead and create a ray, WCSS equals, let me get an empty ray.", 'start': 8263.669, 'duration': 5.762}, {'end': 8276.054, 'text': 'If you remember from the elbow method, from our slide, within the sums of squares,', 'start': 8269.871, 'duration': 6.183}, {'end': 8282.496, 'text': 'WSS is defined as the sum of square distance between each member of the cluster in a centroid.', 'start': 8276.054, 'duration': 6.442}, {'end': 8286.638, 'text': "So we're looking at that change in differences as far as a square distance.", 'start': 8282.897, 'duration': 3.741}, {'end': 8290.32, 'text': "And we're going to run this over a number of k-mean values.", 'start': 8287.239, 'duration': 3.081}, {'end': 8295.82, 'text': "In fact, let's go for i in range, we'll do 11 of them.", 'start': 8292.099, 'duration': 3.721}, {'end': 8299.962, 'text': 'Range 0 of 11.', 'start': 8297.501, 'duration': 2.461}, {'end': 8304.624, 'text': "And the first thing we're going to do is we're going to create the actual, we'll do it all lowercase.", 'start': 8299.962, 'duration': 4.662}, {'end': 8314.508, 'text': "And so we're going to create this object from the k-means that we just imported.", 'start': 8304.644, 'duration': 9.864}], 'summary': 'Implementing k-means algorithm to calculate wcss for 11 k-mean values.', 'duration': 59.07, 'max_score': 8255.438, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA8255438.jpg'}, {'end': 8410.789, 'src': 'embed', 'start': 8385.739, 'weight': 11, 'content': [{'end': 8393.028, 'text': "And if you're working with big data, you know the first thing you do is you run a small sample of the data so you can test all your stuff on it.", 'start': 8385.739, 'duration': 7.289}, {'end': 8403.086, 'text': "And you can already see the problem that if I'm going to iterate through a terabyte of data 11 times and then the k-means itself is iterating through the data multiple times,", 'start': 8393.663, 'duration': 9.423}, {'end': 8404.387, 'text': "that's a heck of a process.", 'start': 8403.086, 'duration': 1.301}, {'end': 8406.728, 'text': "So you've got to be a little careful with this.", 'start': 8404.407, 'duration': 2.321}, {'end': 8410.789, 'text': 'A lot of times, though, you can find your ELBO using the ELBO method.', 'start': 8406.748, 'duration': 4.041}], 'summary': 'Iterating through a terabyte of data 11 times is a lengthy process when working with big data and k-means algorithm.', 'duration': 25.05, 'max_score': 8385.739, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA8385739.jpg'}, {'end': 8540.803, 'src': 'embed', 'start': 8513.636, 'weight': 0, 'content': [{'end': 8521.078, 'text': "You can see a very nice elbow joint there at 2, and again, right around 3 and 4, and then after that, there's not very much.", 'start': 8513.636, 'duration': 7.442}, {'end': 8531.241, 'text': "Now, as a data scientist, if I was looking at this, I would do either 3 or 4, and I'd actually try both of them to see what the output looked like.", 'start': 8522.219, 'duration': 9.022}, {'end': 8535.362, 'text': "And they've already tried this in the back, so we're just going to use 3 as a setup on here.", 'start': 8531.661, 'duration': 3.701}, {'end': 8540.803, 'text': "And let's go ahead and see what that looks like when we actually use this to show the different kinds of cars.", 'start': 8535.602, 'duration': 5.201}], 'summary': 'Data scientist suggests trying options 3 and 4, settling on 3 for car visualization.', 'duration': 27.167, 'max_score': 8513.636, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA8513636.jpg'}, {'end': 9085.742, 'src': 'embed', 'start': 9060.81, 'weight': 6, 'content': [{'end': 9066.693, 'text': 'And so when we do this, we get ln of p over 1 minus p equals m times x plus c.', 'start': 9060.81, 'duration': 5.883}, {'end': 9069.034, 'text': "That's the sigmoid curve function we're looking for.", 'start': 9066.693, 'duration': 2.341}, {'end': 9078.337, 'text': "And we can zoom in on the function, and you'll see that the function as it derives goes to 1 or to 0, depending on what your x value is.", 'start': 9069.67, 'duration': 8.667}, {'end': 9085.742, 'text': "And the probability, if it's greater than 0.5, the value is automatically rounded off to 1, indicating that the student will pass.", 'start': 9078.677, 'duration': 7.065}], 'summary': 'The sigmoid curve function predicts student pass/fail. probability over 0.5 equals 1.', 'duration': 24.932, 'max_score': 9060.81, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA9060810.jpg'}], 'start': 6868.525, 'title': 'Machine learning algorithms in predictive analysis', 'summary': 'Explores the application and evaluation of decision tree classifier, achieving 94.6% accuracy in predicting loan repayments, k-means clustering for car brand classification, utilizing elbo method for optimal cluster determination, and logistic regression for tumor classification, with emphasis on practical demonstrations and quantifiable results.', 'chapters': [{'end': 7080.821, 'start': 6868.525, 'title': 'Decision tree classifier results', 'summary': 'Explains the process of applying and evaluating the decision tree classifier, achieving an accuracy of 94.6% in predicting loan repayments, empowering banks to make informed decisions on loan approvals.', 'duration': 212.296, 'highlights': ['An accuracy of 94.6% was achieved in predicting loan repayments using the decision tree algorithm, providing a powerful tool for banks to assess loan requests.', "The process of applying and evaluating the decision tree classifier is explained, showcasing the accuracy score calculation and the significance of the model's predictive capability.", 'The predict code was utilized to run a prediction, resulting in an accuracy of 93.67% in fitting the model to loan repayment data, demonstrating the effectiveness of the decision tree algorithm.']}, {'end': 7601.996, 'start': 7080.941, 'title': 'Predicting profit with clustering and logistic regression', 'summary': 'Covers k-means clustering, used in unsupervised learning to predict loan defaults, and logistic regression to classify tumors, with a focus on understanding k-means clustering and logistic regression, including a live python demo on clustering cars and classifying tumors.', 'duration': 521.055, 'highlights': ['k-means clustering used in unsupervised learning to predict loan defaults K-means clustering is an example of unsupervised learning, used to cluster loan balances and predict defaults based on feature similarities.', 'Logistic regression used to classify tumors Logistic regression is used to classify tumors as malignant or benign based on features, with an emphasis on the sigmoid function and a Python code demo.', 'Explanation of k-means clustering Provides an explanation of k-means clustering, organizing objects into groups based on similarity and exploring data with known categories, illustrated with a visual example of clustering books.', 'Step-by-step process of k-means clustering Details the step-by-step process of k-means clustering, including choosing centroids, computing distances, forming clusters, calculating centroids, and achieving convergence.', 'Selecting the appropriate number of clusters using the ELBO method Discusses the ELBO method for selecting the optimal number of clusters in k-means algorithm, with the objective of achieving convergence with the least number of iterations.']}, {'end': 8222.903, 'start': 7602.097, 'title': 'Using k-means clustering for car brand classification', 'summary': "Discusses using k-means clustering to classify cars into brands based on parameters such as horsepower, cubic inches, make, year, etc., using the dataset 'cars data' containing information about three brands of cars: toyota, honda, and nissan, and demonstrates data preparation, manipulation, and cleaning using pandas and numpy libraries in a jupyter notebook.", 'duration': 620.806, 'highlights': ['Demonstrating data preparation, manipulation, and cleaning using pandas and numpy libraries in a Jupyter Notebook The chapter demonstrates data preparation, manipulation, and cleaning using pandas and numpy libraries in a Jupyter Notebook.', 'Using k-means clustering to classify cars into brands based on parameters such as horsepower, cubic inches, make, year, etc. The chapter discusses using k-means clustering to classify cars into brands based on parameters such as horsepower, cubic inches, make, year, etc.', "Using the dataset 'cars data' containing information about three brands of cars: Toyota, Honda, and Nissan The chapter uses the dataset 'cars data' containing information about three brands of cars: Toyota, Honda, and Nissan."]}, {'end': 8640.175, 'start': 8224.064, 'title': 'Utilizing elbo method for k-means clustering', 'summary': 'Covers using the elbo method to find the optimal number of clusters, implementing k-means clustering to generate a graph with a clear elbow joint at 2, 3, and 4 clusters, ultimately choosing 3 clusters for application to the cars dataset.', 'duration': 416.111, 'highlights': ['Implementing k-means clustering to generate a graph with a clear elbow joint at 2, 3, and 4 clusters The process involves creating an array to store the sum of square distances, running k-means over a range of values (0 to 11), fitting the k-means 11 times to observe the change in inertia, and plotting the resulting graph with clear elbow joints at 2, 3, and 4 clusters.', 'Choosing 3 clusters for application to the cars dataset After visualizing the graph, the recommendation is to choose 3 clusters for application to the cars dataset, with a suggestion to also consider testing with 4 clusters to observe potential differences.', 'Converting X into a matrix for data processing A pandas trick involves converting X into a matrix with columns set to none for efficient data processing.']}, {'end': 9167.256, 'start': 8640.175, 'title': 'Plotting clusters and logistic regression', 'summary': 'Covers the process of plotting clusters using k-means, including visualizing clusters of car make and using logistic regression to classify whether a tumor is malignant or benign, with emphasis on the sigmoid function and its application in predicting student performance.', 'duration': 527.081, 'highlights': ['The process of plotting clusters using k-means is demonstrated, including visualization of clusters of car make, with distinct clusters of Honda, Toyota, and Nissan. Visualization of clusters using k-means; distinct clusters of car make (Honda, Toyota, Nissan).', 'The application of logistic regression in classifying whether a tumor is malignant or benign is explained, emphasizing the sigmoid function and its role in predicting student performance based on hours studied. Explanation of logistic regression; emphasis on sigmoid function; prediction of student performance based on hours studied.']}], 'duration': 2298.731, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA6868525.jpg', 'highlights': ['Achieved 94.6% accuracy in predicting loan repayments using decision tree algorithm, a powerful tool for banks.', 'Demonstrated process of applying and evaluating decision tree classifier, showcasing accuracy score calculation.', 'Utilized predict code to achieve 93.67% accuracy in fitting model to loan repayment data, demonstrating effectiveness.', 'Explained k-means clustering used in unsupervised learning to predict loan defaults based on feature similarities.', 'Used logistic regression to classify tumors as malignant or benign based on features, with emphasis on sigmoid function.', 'Provided explanation of k-means clustering, organizing objects into groups based on similarity.', 'Detailed step-by-step process of k-means clustering, including choosing centroids and achieving convergence.', 'Discussed ELBO method for selecting optimal number of clusters in k-means algorithm.', 'Demonstrated data preparation, manipulation, and cleaning using pandas and numpy libraries in a Jupyter Notebook.', 'Discussed using k-means clustering to classify cars into brands based on parameters such as horsepower, cubic inches, make, year, etc.', "Used dataset 'cars data' containing information about three brands of cars: Toyota, Honda, and Nissan.", 'Implemented k-means clustering to generate a graph with clear elbow joint at 2, 3, and 4 clusters.', 'Recommended choosing 3 clusters for application to the cars dataset, with suggestion to test with 4 clusters.', 'Demonstrated process of plotting clusters using k-means, including visualization of distinct clusters of car make.', 'Explained application of logistic regression in classifying whether a tumor is malignant or benign, emphasizing sigmoid function.']}, {'end': 10257.674, 'segs': [{'end': 9426.005, 'src': 'embed', 'start': 9385.05, 'weight': 0, 'content': [{'end': 9386.451, 'text': "We'll just look at those two columns.", 'start': 9385.05, 'duration': 1.401}, {'end': 9389.514, 'text': 'And data equals data.', 'start': 9387.572, 'duration': 1.942}, {'end': 9394.018, 'text': "So that tells us which two columns we're plotting and that we're going to use the data that we pulled in.", 'start': 9389.734, 'duration': 4.284}, {'end': 9395.079, 'text': "Let's just run that.", 'start': 9394.038, 'duration': 1.041}, {'end': 9401.1, 'text': "It generates a really nice graph on here, and there's all kinds of cool things on this graph to look at.", 'start': 9396.115, 'duration': 4.985}, {'end': 9404.343, 'text': 'I mean, we have the texture mean and the radius mean, obviously the axes.', 'start': 9401.12, 'duration': 3.223}, {'end': 9405.744, 'text': 'You can also see..', 'start': 9404.804, 'duration': 0.94}, {'end': 9411.675, 'text': 'And one of the cool things on here is you can also see the histogram.', 'start': 9408.493, 'duration': 3.182}, {'end': 9413.497, 'text': 'They show that for the radius mean.', 'start': 9411.815, 'duration': 1.682}, {'end': 9417.82, 'text': 'Where does the most common radius mean come up and where the most common texture is.', 'start': 9413.697, 'duration': 4.123}, {'end': 9426.005, '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': 9418.22, 'duration': 7.785}], 'summary': 'Data generates a graph with texture and radius mean, including histograms for common values.', 'duration': 40.955, 'max_score': 9385.05, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA9385050.jpg'}, {'end': 9570.654, 'src': 'embed', 'start': 9548.064, 'weight': 5, 'content': [{'end': 9557.028, 'text': "And we can see from the ID, there's no real one feature that just says, if you go across the top line, that lights up.", 'start': 9548.064, 'duration': 8.964}, {'end': 9562.39, 'text': "There's no one feature that says, hey, if the area is a certain size, then it's going to be benign or malignant.", 'start': 9557.228, 'duration': 5.162}, {'end': 9564.711, 'text': "It says there's some that sort of add up.", 'start': 9562.77, 'duration': 1.941}, {'end': 9570.654, 'text': "And that's a big hint in the data that we're trying to ID this, whether it's malignant or benign.", 'start': 9564.991, 'duration': 5.663}], 'summary': 'Id lacks distinct features for classifying as benign or malignant.', 'duration': 22.59, 'max_score': 9548.064, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA9548064.jpg'}, {'end': 9620.979, 'src': 'embed', 'start': 9593.662, 'weight': 1, 'content': [{'end': 9599.565, 'text': 'If you remember from earlier in this tutorial, we did it a little differently where we added stuff up and summed them up.', 'start': 9593.662, 'duration': 5.903}, {'end': 9605.468, 'text': "You can actually, with pandas, do it really quickly, data.isNull and sum it, and it's going to go across all the columns.", 'start': 9599.906, 'duration': 5.562}, {'end': 9612.292, 'text': "So when I run this, you're going to see all the columns come up with no null data.", 'start': 9605.869, 'duration': 6.423}, {'end': 9620.979, 'text': "So we've just, just to rehash these last few steps, we've done a lot of exploration.", 'start': 9614.537, 'duration': 6.442}], 'summary': 'In pandas, null data can be quickly summed across all columns for exploration.', 'duration': 27.317, 'max_score': 9593.662, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA9593662.jpg'}, {'end': 9740.659, 'src': 'embed', 'start': 9718.649, 'weight': 3, 'content': [{'end': 9727.888, 'text': "One of the reasons to start dividing your data up when you're looking at this information is sometimes the data will be the same data coming in.", 'start': 9718.649, 'duration': 9.239}, {'end': 9732.552, 'text': 'So if I have two measurements coming in to my model, it might overweigh them.', 'start': 9728.008, 'duration': 4.544}, {'end': 9737.977, 'text': "It might overpower the other measurements because it's basically taking that information in twice.", 'start': 9732.893, 'duration': 5.084}, {'end': 9740.659, 'text': "That's a little bit past the scope of this tutorial.", 'start': 9738.678, 'duration': 1.981}], 'summary': 'Dividing data can prevent overweighing or overpowering due to duplicate information.', 'duration': 22.01, 'max_score': 9718.649, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA9718649.jpg'}, {'end': 10045.243, 'src': 'embed', 'start': 10014.173, 'weight': 6, 'content': [{'end': 10015.254, 'text': 'And, of course, it prints this out.', 'start': 10014.173, 'duration': 1.081}, {'end': 10018.335, 'text': 'It tells us all the different variables that you can set on there.', 'start': 10015.334, 'duration': 3.001}, {'end': 10020.236, 'text': "There's a lot of different choices you can make.", 'start': 10018.355, 'duration': 1.881}, {'end': 10022.758, 'text': "But for what we're doing, we're just going to let all the defaults sit.", 'start': 10020.578, 'duration': 2.18}, {'end': 10025.619, 'text': "We don't really need to mess with those on this particular example.", 'start': 10023.079, 'duration': 2.54}, {'end': 10031.72, 'text': "And there's nothing in here that really stands out as super important until you start fine-tuning it.", 'start': 10025.859, 'duration': 5.861}, {'end': 10034.641, 'text': "But for what we're doing, the basics will work just fine.", 'start': 10031.94, 'duration': 2.701}, {'end': 10038.022, 'text': 'And then we need to go ahead and test out our model.', 'start': 10035.081, 'duration': 2.941}, {'end': 10041.042, 'text': "Is it working? So let's create a variable yPredict.", 'start': 10038.082, 'duration': 2.96}, {'end': 10045.243, 'text': 'And this is going to be equal to our log model.', 'start': 10041.642, 'duration': 3.601}], 'summary': 'Exploring variables and default settings, testing model performance with ypredict variable.', 'duration': 31.07, 'max_score': 10014.173, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA10014173.jpg'}], 'start': 9167.536, 'title': 'Python for tumor diagnosis prediction', 'summary': 'Discusses using python and libraries like numpy, pandas, seaborn, and matplotlib to predict tumor diagnosis with 92% precision, emphasizing data exploration, model testing, and key feature identification.', 'chapters': [{'end': 9318.764, 'start': 9167.536, 'title': 'Using python for tumor diagnosis prediction', 'summary': 'Discusses using python and libraries like numpy, pandas, seaborn and matplotlib to analyze a dataset containing tumor characteristics, with a focus on predicting the diagnosis of malignant or benign tumors based on various measurements.', 'duration': 151.228, 'highlights': ['The dataset contains various measurements related to tumor characteristics such as radius mean, texture average, perimeter mean, area mean, smoothness, and symmetry, with a total of around 36 features. The dataset includes numerous measurements like radius mean, texture average, perimeter mean, area mean, smoothness, and symmetry, providing valuable inputs for tumor analysis.', 'The chapter emphasizes the significance of using Python and libraries like numpy, pandas, seaborn, and matplotlib for analyzing the tumor dataset to predict the diagnosis of malignant or benign tumors. The use of Python and libraries like numpy, pandas, seaborn, and matplotlib is highlighted for the analysis of the tumor dataset, emphasizing its importance in predicting tumor diagnosis.', 'The significance of predicting the diagnosis of malignant or benign tumors based on the tumor characteristics is emphasized, indicating the potential impact on medical decision-making and patient outcomes. The prediction of tumor diagnosis based on its characteristics is highlighted, underscoring its potential impact on medical decision-making and patient outcomes.']}, {'end': 9830.278, 'start': 9321.005, 'title': 'Data exploration and analysis with pandas and seaborn', 'summary': 'Explores data exploration and analysis using pandas and seaborn, including visualizations of data distribution, correlation heatmap, and checking for null values, with emphasis on identifying key features for classification.', 'duration': 509.273, 'highlights': ['The chapter explores data exploration and analysis using Pandas and Seaborn, including visualizations of data distribution, correlation heatmap, and checking for null values. The chapter covers data exploration and analysis techniques with Pandas and Seaborn, showcasing visualizations of data distribution, correlation heatmap, and checking for null values.', "Emphasis is placed on identifying key features for classification by focusing on specific columns such as 'worst radius', 'worst texture', 'parameter area', 'smoothness', and 'compactness'. The chapter emphasizes identifying key features for classification, focusing on specific columns such as 'worst radius', 'worst texture', 'parameter area', 'smoothness', and 'compactness' to determine the diagnosis of benign or malignant.", 'The importance of dividing the data into distinct pieces is highlighted to avoid overweighing or overpowering specific measurements, especially when dealing with duplicated information. The importance of dividing the data into distinct pieces is highlighted to avoid overweighing or overpowering specific measurements, especially when dealing with duplicated information that might impact the accuracy of classification.']}, {'end': 10257.674, 'start': 9831.439, 'title': 'Data splitting and model testing', 'summary': "Discusses the process of splitting data into training and testing sets using sklearn's train test split, building and testing a logistic regression model, and evaluating its precision, achieving a 92% precision in predicting tumor types.", 'duration': 426.235, 'highlights': ["The chapter emphasizes the importance of testing the model by splitting data into training and testing sets using sklearn's train test split, with a recommendation to use a test size of 0.3, resulting in 70% of the data for training and 30% for testing.", 'It details the process of creating and testing a logistic regression model, using the predict function to test the model against the testing data, achieving a precision of 92% in predicting tumor types.', 'It explains the significance of precision in different domains, highlighting the importance of a 92% precision in a medical domain with potentially catastrophic outcomes.']}], 'duration': 1090.138, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA9167536.jpg', 'highlights': ['The dataset includes numerous measurements like radius mean, texture average, perimeter mean, area mean, smoothness, and symmetry, providing valuable inputs for tumor analysis.', 'The use of Python and libraries like numpy, pandas, seaborn, and matplotlib is highlighted for the analysis of the tumor dataset, emphasizing its importance in predicting tumor diagnosis.', 'The prediction of tumor diagnosis based on its characteristics is highlighted, underscoring its potential impact on medical decision-making and patient outcomes.', 'The chapter covers data exploration and analysis techniques with Pandas and Seaborn, showcasing visualizations of data distribution, correlation heatmap, and checking for null values.', "The chapter emphasizes identifying key features for classification, focusing on specific columns such as 'worst radius', 'worst texture', 'parameter area', 'smoothness', and 'compactness' to determine the diagnosis of benign or malignant.", 'The importance of dividing the data into distinct pieces is highlighted to avoid overweighing or overpowering specific measurements, especially when dealing with duplicated information that might impact the accuracy of classification.', "The chapter emphasizes the importance of testing the model by splitting data into training and testing sets using sklearn's train test split, with a recommendation to use a test size of 0.3, resulting in 70% of the data for training and 30% for testing.", 'It details the process of creating and testing a logistic regression model, using the predict function to test the model against the testing data, achieving a precision of 92% in predicting tumor types.', 'It explains the significance of precision in different domains, highlighting the importance of a 92% precision in a medical domain with potentially catastrophic outcomes.']}, {'end': 11849.169, 'segs': [{'end': 10395.126, 'src': 'embed', 'start': 10365.021, 'weight': 8, 'content': [{'end': 10365.822, 'text': 'And we can look at these.', 'start': 10365.021, 'duration': 0.801}, {'end': 10370.546, 'text': 'we can say we can evaluate the sharpness of the claws how sharp are their claws?', 'start': 10365.822, 'duration': 4.724}, {'end': 10372.888, 'text': 'and we can evaluate the length of the ears.', 'start': 10370.546, 'duration': 2.342}, {'end': 10377.232, 'text': 'and we can usually sort out cats from dogs based on even those two characteristics.', 'start': 10372.888, 'duration': 4.344}, {'end': 10380.192, 'text': 'Now tell me if it is a cat or a dog.', 'start': 10377.97, 'duration': 2.222}, {'end': 10381.233, 'text': 'An odd question.', 'start': 10380.653, 'duration': 0.58}, {'end': 10383.475, 'text': 'Usually little kids know cats and dogs by now.', 'start': 10381.273, 'duration': 2.202}, {'end': 10385.978, 'text': "Unless they live in a place where there's not many cats or dogs.", 'start': 10384.016, 'duration': 1.962}, {'end': 10389.501, 'text': 'So if we look at the sharpness of the claws, the length of the ears,', 'start': 10386.298, 'duration': 3.203}, {'end': 10395.126, 'text': 'and we can see that the cat has smaller ears and sharper claws than the other animals.', 'start': 10389.501, 'duration': 5.625}], 'summary': 'Sharpness of claws and length of ears distinguish cats from dogs.', 'duration': 30.105, 'max_score': 10365.021, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA10365021.jpg'}, {'end': 10464.357, 'src': 'embed', 'start': 10423.462, 'weight': 3, 'content': [{'end': 10429.11, 'text': "It's one of the simplest supervised machine learning algorithms mostly used for classification.", 'start': 10423.462, 'duration': 5.648}, {'end': 10438.122, 'text': "So we want to know is this a dog or it's not a dog? Is it a cat or not a cat? It classifies a data point based on how its neighbors are classified.", 'start': 10429.47, 'duration': 8.652}, {'end': 10444.146, 'text': 'KNN stores all available cases and classifies new cases based on a similarity measure.', 'start': 10438.602, 'duration': 5.544}, {'end': 10448.529, 'text': "And here we've gone from cats and dogs right into wine, another favorite of mine.", 'start': 10444.426, 'duration': 4.103}, {'end': 10453.592, 'text': 'KNN stores all available cases and classifies new cases based on a similarity measure.', 'start': 10448.729, 'duration': 4.863}, {'end': 10454.492, 'text': 'And here, you see,', 'start': 10453.872, 'duration': 0.62}, {'end': 10461.776, 'text': "we have a measurement of sulfur dioxide versus the chloride level and then the different wines they've tested and where they fall on that graph,", 'start': 10454.492, 'duration': 7.284}, {'end': 10464.357, 'text': 'based on how much sulfur dioxide and how much chloride.', 'start': 10461.776, 'duration': 2.581}], 'summary': 'Knn is a simple supervised ml algorithm used for classification, including a wine classification example.', 'duration': 40.895, 'max_score': 10423.462, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA10423462.jpg'}, {'end': 10813.497, 'src': 'embed', 'start': 10784.158, 'weight': 10, 'content': [{'end': 10788.121, 'text': 'And we can see the three closest neighbors puts them at normal.', 'start': 10784.158, 'duration': 3.963}, {'end': 10789.201, 'text': "And that's pretty self-evident.", 'start': 10788.221, 'duration': 0.98}, {'end': 10792.724, 'text': "When you look at this graph, it's pretty easy to say, okay, we're just voting.", 'start': 10789.221, 'duration': 3.503}, {'end': 10793.804, 'text': 'Normal, normal, normal.', 'start': 10792.864, 'duration': 0.94}, {'end': 10794.705, 'text': 'Three votes for normal.', 'start': 10793.844, 'duration': 0.861}, {'end': 10796.026, 'text': 'This is going to be a normal weight.', 'start': 10794.765, 'duration': 1.261}, {'end': 10798.587, 'text': 'So majority of neighbors are pointing towards normal.', 'start': 10796.406, 'duration': 2.181}, {'end': 10802.39, 'text': 'Hence, as per K&N algorithm, the class of 57, 170 should be normal.', 'start': 10798.808, 'duration': 3.582}, {'end': 10805.512, 'text': 'So a recap of KNN.', 'start': 10804.071, 'duration': 1.441}, {'end': 10809.395, 'text': 'Positive integer k is specified along with a new sample.', 'start': 10805.732, 'duration': 3.663}, {'end': 10813.497, 'text': 'We select the k entries in our database which are closest to the new sample.', 'start': 10809.515, 'duration': 3.982}], 'summary': 'Using knn algorithm, the majority of three closest neighbors predict the class of 57, 170 as normal.', 'duration': 29.339, 'max_score': 10784.158, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA10784158.jpg'}, {'end': 11051.168, 'src': 'embed', 'start': 11019.54, 'weight': 9, 'content': [{'end': 11023.745, 'text': "So we've brought in our pandas, our numpy, our two general Python tools.", 'start': 11019.54, 'duration': 4.205}, {'end': 11027.249, 'text': 'And then you can see over here we have our train test split.', 'start': 11024.145, 'duration': 3.104}, {'end': 11029.712, 'text': 'By now you should be familiar with splitting the data.', 'start': 11027.729, 'duration': 1.983}, {'end': 11034.057, 'text': 'We want to split part of it for training our thing and then training our particular model.', 'start': 11030.332, 'duration': 3.725}, {'end': 11038.14, 'text': 'And then we want to go ahead and test the remaining data to see how good it is.', 'start': 11034.657, 'duration': 3.483}, {'end': 11043.843, 'text': "Pre-processing, a standard scalar pre-processor, so we don't have a bias of really large numbers.", 'start': 11038.48, 'duration': 5.363}, {'end': 11051.168, 'text': "Remember, in the data we had, like number of pregnancies isn't going to get very large, where the amount of insulin they take can get up to 256.", 'start': 11043.863, 'duration': 7.305}], 'summary': 'Using pandas and numpy for data preprocessing and model training with a 75/25 train-test split, including standard scalar pre-processing to handle varying data ranges.', 'duration': 31.628, 'max_score': 11019.54, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA11019540.jpg'}, {'end': 11119.067, 'src': 'embed', 'start': 11076.038, 'weight': 1, 'content': [{'end': 11084.328, 'text': "So we have our two general Python modules we're importing, and then we have our six modules specific from the sklearn setup.", 'start': 11076.038, 'duration': 8.29}, {'end': 11089.152, 'text': 'And then we do need to go ahead and run this so that these are actually imported.', 'start': 11084.828, 'duration': 4.324}, {'end': 11089.973, 'text': 'There we go.', 'start': 11089.412, 'duration': 0.561}, {'end': 11091.434, 'text': 'And then move on to the next step.', 'start': 11090.173, 'duration': 1.261}, {'end': 11094.237, 'text': "And so in this set we're going to go ahead and load the database.", 'start': 11091.835, 'duration': 2.402}, {'end': 11095.638, 'text': "We're going to use pandas.", 'start': 11094.537, 'duration': 1.101}, {'end': 11097.06, 'text': 'Remember pandas is pd.', 'start': 11095.698, 'duration': 1.362}, {'end': 11099.282, 'text': "And we'll take a look at the data in Python.", 'start': 11097.38, 'duration': 1.902}, {'end': 11101.323, 'text': 'We looked at it in a simple spreadsheet.', 'start': 11099.362, 'duration': 1.961}, {'end': 11104.346, 'text': "But usually I like to also pull it up so that we can see what we're doing.", 'start': 11101.424, 'duration': 2.922}, {'end': 11108.61, 'text': "So here's our data set equals pd.readcsv.", 'start': 11104.606, 'duration': 4.004}, {'end': 11110.392, 'text': "That's a pandas command.", 'start': 11109.071, 'duration': 1.321}, {'end': 11115.665, 'text': 'And the diabetes folder, I just put in the same folder where my IPython script is.', 'start': 11110.842, 'duration': 4.823}, {'end': 11119.067, 'text': "If you put in a different folder, you'd need the full length on there.", 'start': 11115.965, 'duration': 3.102}], 'summary': 'Python modules and sklearn setup imported, database loaded using pandas for diabetes dataset.', 'duration': 43.029, 'max_score': 11076.038, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA11076038.jpg'}, {'end': 11246.249, 'src': 'embed', 'start': 11217.592, 'weight': 2, 'content': [{'end': 11219.894, 'text': 'We talked about glucose, blood pressure, skin thickness.', 'start': 11217.592, 'duration': 2.302}, {'end': 11225.76, 'text': "And this is a nice way when you're working with columns is to list the columns you need to do some kind of transformation on.", 'start': 11220.495, 'duration': 5.265}, {'end': 11227.141, 'text': 'A very common thing to do.', 'start': 11225.98, 'duration': 1.161}, {'end': 11234.644, 'text': "And then for this particular setup, we certainly could use the, there's some panda tools that will do a lot of this, where we can replace the NA.", 'start': 11227.461, 'duration': 7.183}, {'end': 11241.207, 'text': "But we're going to go ahead and do it as a dataset column equals dataset column dot replace.", 'start': 11234.764, 'duration': 6.443}, {'end': 11242.687, 'text': 'This is still pandas.', 'start': 11241.327, 'duration': 1.36}, {'end': 11243.808, 'text': 'You can do a direct.', 'start': 11242.987, 'duration': 0.821}, {'end': 11246.249, 'text': "There's also one that you look for your NAN.", 'start': 11243.828, 'duration': 2.421}], 'summary': 'Discussion about data transformation using pandas for replacing na values.', 'duration': 28.657, 'max_score': 11217.592, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA11217592.jpg'}, {'end': 11681.66, 'src': 'embed', 'start': 11651.302, 'weight': 0, 'content': [{'end': 11653.143, 'text': "So we'll go ahead and put in our classifier.", 'start': 11651.302, 'duration': 1.841}, {'end': 11654.664, 'text': "We're creating our classifier now.", 'start': 11653.163, 'duration': 1.501}, {'end': 11657.026, 'text': "And it's going to be the kNeighborsClassifier.", 'start': 11654.865, 'duration': 2.161}, {'end': 11658.763, 'text': 'nNeighbors equals 11.', 'start': 11657.246, 'duration': 1.517}, {'end': 11662.866, 'text': 'Remember we did 12 minus 1 for 11, so we have an odd number of neighbors.', 'start': 11658.763, 'duration': 4.103}, {'end': 11669.792, 'text': "P equals 2 because we're looking for are they diabetic or not, and we're using the Euclidean metric.", 'start': 11663.327, 'duration': 6.465}, {'end': 11672.394, 'text': 'There are other means of measuring the distance.', 'start': 11670.072, 'duration': 2.322}, {'end': 11679.28, 'text': 'You could do like square means values, all kinds of measure this, but the Euclidean is the most common one, and it works quite well.', 'start': 11672.494, 'duration': 6.786}, {'end': 11681.66, 'text': "It's important to evaluate the model.", 'start': 11679.858, 'duration': 1.802}], 'summary': 'Using kneighborsclassifier with nneighbors=11 for diabetic prediction.', 'duration': 30.358, 'max_score': 11651.302, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA11651302.jpg'}], 'start': 10257.714, 'title': 'K-nearest neighbors in machine learning', 'summary': 'Explains knn algorithm fundamentals, its application in predicting diabetes, data preprocessing, and achieving an 82% accuracy in diabetes prediction with a knn model.', 'chapters': [{'end': 10823.984, 'start': 10257.714, 'title': 'Understanding k-nearest neighbors in machine learning', 'summary': 'Explains the fundamental concepts of k-nearest neighbors (knn) algorithm, its relevance in machine learning, the process of choosing the factor k, its application in classification using real-life examples, and the working of knn algorithm through euclidean distance calculation, culminating in a comprehensive understanding of knn.', 'duration': 566.27, 'highlights': ['K-nearest neighbors (KNN) is a fundamental place to start in machine learning, used for classification, and its logic is easy to understand and incorporated in other forms of machine learning. KNN is a fundamental concept in machine learning, providing a basis for various other algorithms, and it is primarily used for classification tasks.', 'The process of choosing the factor K is essential for better accuracy, and it involves parameter tuning, where the right value of K is determined based on feature similarity. Choosing the factor K in KNN algorithm is crucial for achieving better accuracy, and it involves parameter tuning based on feature similarity.', 'The KNN algorithm works by classifying a data point based on how its neighbors are classified, where K represents the number of nearest neighbors to include in the majority voting process. KNN algorithm classifies a data point based on the classification of its nearest neighbors, with K representing the number of neighbors considered in the voting process.', 'The Euclidean distance calculation is used in KNN to find the nearest neighbors, and the majority classification of these neighbors determines the classification of the new sample. KNN algorithm utilizes Euclidean distance calculation to identify the nearest neighbors, and the majority classification among these neighbors is used to classify the new sample.', 'KNN is suitable for labeled data sets with minimal noise, making it efficient for smaller data sets, but not ideal for large, complex data sets. KNN is ideal for labeled data sets with low noise levels, making it efficient for smaller data sets, while not being suitable for large and complex data sets.']}, {'end': 11059.534, 'start': 10824.224, 'title': 'Predict diabetes use case in python', 'summary': 'Discusses a use case to predict whether a person will be diagnosed with diabetes or not using a data set of 768 people, emphasizing the data format, data size, and the tools required for the analysis.', 'duration': 235.31, 'highlights': ['The data set consists of 768 people who were or were not diagnosed with diabetes. The data set size is quantified, providing context for the analysis.', 'The data is in a simple spreadsheet format and is comma separated with eight columns representing attributes and a ninth column representing the outcome of whether they have diabetes. Describes the format of the data set, providing key details for understanding the use case.', 'The size of the data set is small, consisting of 768 entries, making it easily manageable on a regular desktop computer. Quantifies the data set size and emphasizes its manageability on standard hardware.', 'The chapter discusses the tools required for the analysis, including importing pandas, numpy, and using train test split and standard scalar pre-processor for pre-processing. Provides an overview of the essential tools and pre-processing techniques needed for the analysis.']}, {'end': 11332.603, 'start': 11059.534, 'title': 'Data preprocessing and model testing', 'summary': 'Covers importing python modules, loading and inspecting a dataset using pandas, and data preprocessing techniques such as replacing zero values with nan and calculating the mean to handle missing data.', 'duration': 273.069, 'highlights': ['We import six modules specific to the sklearn setup and run them for import.', 'We load the dataset using Pandas and display its length, which is 768 lines.', 'We preprocess the dataset by creating a list of columns with values that cannot be zero and replacing the zero values with NaN.', 'To handle missing data, we calculate the mean of each column and replace the NaN values with their respective means.']}, {'end': 11634.351, 'start': 11332.963, 'title': 'Data prep and model training', 'summary': 'Outlines the data preparation process including data exploration, data splitting, and data scaling, before training the knn model using the kneighborsclassifier, and also provides insights into the dimensionality of the dataset.', 'duration': 301.388, 'highlights': ['The data is split into training and testing sets with a test size of 20%. The transcript mentions that the data set is split into training and testing sets with a test size of 20%, allowing for a portion of the data to be set aside for testing later.', 'The data is standardized using a standard scalar to ensure all data falls within the range of -1 to 1. The process involves standardizing the data using a standard scalar, which ensures that all the data falls within the range of -1 to 1, maintaining consistency and aiding in model training.', 'The KNN model is trained using the kNeighborsClassifier after completing the data preparation steps. After completing the data preparation steps, the KNN model is trained using the kNeighborsClassifier, showcasing the progression from data preparation to model training.', "Insight into the dimensionality of the dataset is provided, with the length of y being 768 and the square root of the length of y test being 12.409. Insight is provided into the dimensionality of the dataset, with the length of y being 768 and the square root of the length of y test being 12.409, offering valuable information about the dataset's dimensions and characteristics."]}, {'end': 11849.169, 'start': 11635.011, 'title': 'Knn model for diabetes prediction', 'summary': 'Discusses the creation of a k-nearest neighbors (knn) model with 11 neighbors for predicting diabetes, evaluating it using confusion matrix, f1 score, and accuracy score, achieving an 82% accuracy.', 'duration': 214.158, 'highlights': ['Creating KNN model with 11 neighbors for diabetes prediction The speaker creates a KNN model with nNeighbors set to 11 for predicting diabetes, aiming for an odd number of neighbors to avoid ties in voting.', 'Evaluation using confusion matrix, F1 score, and accuracy score The model is evaluated using a confusion matrix, F1 score, and accuracy score, with the F1 score calculated at 0.69 and an overall accuracy of 82% achieved.', "Importance of F1 score over accuracy score The F1 score is highlighted as more telling than accuracy, as it takes into account both false positives and false negatives, offering more insight into the model's performance."]}], 'duration': 1591.455, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA10257714.jpg', 'highlights': ['KNN is a fundamental concept in machine learning, providing a basis for various other algorithms, and it is primarily used for classification tasks.', 'Choosing the factor K in KNN algorithm is crucial for achieving better accuracy, and it involves parameter tuning based on feature similarity.', 'The KNN algorithm works by classifying a data point based on the classification of its nearest neighbors, with K representing the number of neighbors considered in the voting process.', 'The Euclidean distance calculation is used in KNN to find the nearest neighbors, and the majority classification of these neighbors determines the classification of the new sample.', 'The data set consists of 768 people who were or were not diagnosed with diabetes.', 'The data is in a simple spreadsheet format and is comma separated with eight columns representing attributes and a ninth column representing the outcome of whether they have diabetes.', 'The size of the data set is small, consisting of 768 entries, making it easily manageable on a regular desktop computer.', 'The data is split into training and testing sets with a test size of 20%.', 'The KNN model is trained using the kNeighborsClassifier after completing the data preparation steps.', 'Creating KNN model with 11 neighbors for diabetes prediction', 'Evaluation using confusion matrix, F1 score, and accuracy score', "The F1 score is highlighted as more telling than accuracy, as it takes into account both false positives and false negatives, offering more insight into the model's performance."]}, {'end': 13453.54, 'segs': [{'end': 12041.194, 'src': 'embed', 'start': 12011.416, 'weight': 2, 'content': [{'end': 12014.677, 'text': "But now we're talking about buckets and we want to count how many people are in that bucket.", 'start': 12011.416, 'duration': 3.261}, {'end': 12021.8, 'text': 'Quantitative numerical data falls into two classes, discrete or continuous.', 'start': 12015.577, 'duration': 6.223}, {'end': 12030.723, 'text': 'And so data with a final set of values which can be categorized, class strength, questions answered correctly, and runs hit and cricket.', 'start': 12022.4, 'duration': 8.323}, {'end': 12032.044, 'text': 'A lot of times.', 'start': 12031.403, 'duration': 0.641}, {'end': 12037.57, 'text': 'when you see this, you can think integer and a very restricted integer, i.e..', 'start': 12032.044, 'duration': 5.526}, {'end': 12041.194, 'text': 'you can only have 100 questions on a test, so you can.', 'start': 12037.57, 'duration': 3.624}], 'summary': 'Analyzing data in discrete and continuous classes for categorization and quantification.', 'duration': 29.778, 'max_score': 12011.416, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA12011416.jpg'}, {'end': 13081.653, 'src': 'embed', 'start': 13050.134, 'weight': 3, 'content': [{'end': 13054.035, 'text': 'And of course, the matrix, you can get very complicated on these.', 'start': 13050.134, 'duration': 3.901}, {'end': 13060.338, 'text': "Or in this case, we'll go ahead and do, let's create two complex matrices.", 'start': 13054.155, 'duration': 6.183}, {'end': 13065.315, 'text': 'This one is a matrix of 12, 10, 4, 6, 4, 31.', 'start': 13061.538, 'duration': 3.777}, {'end': 13070.142, 'text': "We'll just print out A so you can see what that looks like.", 'start': 13065.32, 'duration': 4.822}, {'end': 13071.782, 'text': "Here's print A.", 'start': 13070.162, 'duration': 1.62}, {'end': 13081.653, 'text': 'When we print A out, you can see that we have a 2 by 3 layer matrix for A.', 'start': 13073.303, 'duration': 8.35}], 'summary': 'Creating two complex matrices: a (2x3) and b (?)', 'duration': 31.519, 'max_score': 13050.134, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA13050134.jpg'}, {'end': 13453.54, 'src': 'embed', 'start': 13391.371, 'weight': 0, 'content': [{'end': 13401.986, 'text': "And then another thing we can do to kind of wrap this up, we'll hit you with the most complicated piece of this puzzle here, is an inverse A matrix.", 'start': 13391.371, 'duration': 10.615}, {'end': 13406.752, 'text': "And let's just go ahead and put the, oh, it's a lengthy description.", 'start': 13402.967, 'duration': 3.785}, {'end': 13409.514, 'text': "Let's go ahead and put the description.", 'start': 13408.414, 'duration': 1.1}, {'end': 13414.636, 'text': 'This is straight out of the website for NumPy.', 'start': 13409.534, 'duration': 5.102}, {'end': 13422.815, 'text': "So given a square matrix A, here's our square matrix A, which is 2, 1, 0, 0, 1, 0, 1, 2, 1.", 'start': 13415.737, 'duration': 7.078}, {'end': 13424.88, 'text': "Keep in mind, 3 by 3, it's square.", 'start': 13422.819, 'duration': 2.061}, {'end': 13425.92, 'text': "It's got to be equal.", 'start': 13425.04, 'duration': 0.88}, {'end': 13433.083, 'text': "It's going to return the matrix A inverse satisfying dot A inverse.", 'start': 13426.34, 'duration': 6.743}, {'end': 13435.904, 'text': "So here's our matrix multiplication.", 'start': 13433.103, 'duration': 2.801}, {'end': 13446.499, 'text': 'And then of course it equals the dot, yeah, A inverse of A with an identity shape of A dot shape zero.', 'start': 13437.425, 'duration': 9.074}, {'end': 13448.182, 'text': 'This is just reshaping the identity.', 'start': 13446.599, 'duration': 1.583}, {'end': 13450.779, 'text': "That's a little complicated there.", 'start': 13449.619, 'duration': 1.16}, {'end': 13453.54, 'text': "So we're going to have our, here's our array.", 'start': 13451.54, 'duration': 2}], 'summary': 'Discussion on finding the inverse of a 3x3 square matrix using numpy.', 'duration': 62.169, 'max_score': 13391.371, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA13391371.jpg'}], 'start': 11849.61, 'title': 'Mathematics and linear algebra for machine learning', 'summary': 'Covers the basics of mathematics for machine learning including a print accuracy score of 80%, data types, linear algebra, calculus, statistics, and probability, and delves into the basics of linear algebra including linear equations, matrices, and vectors with detailed explanations of operations and applications, as well as i-gene vectors and values and various matrix operations in numpy.', 'chapters': [{'end': 11912.131, 'start': 11849.61, 'title': 'Math for machine learning', 'summary': 'Covers the basics of mathematics for machine learning, including a print accuracy score of 80%, data types, linear algebra, calculus, statistics, and probability, with the aim of performing analytics to drive insights.', 'duration': 62.521, 'highlights': ['The print accuracy score is .818, which can be rounded off to 80%, indicating a fair fit in the model.', 'The agenda covers data types, linear algebra, calculus, statistics, and probability for machine learning, along with hands-on demos.', 'Data denotes the individual pieces of factual information collected from various sources, which is stored, processed, and used for analysis.', 'Performing analytics to drive insights is crucial, especially for explaining complex technical information to shareholders in a way they can understand.']}, {'end': 12681.565, 'start': 11913.069, 'title': 'Types of data and linear algebra', 'summary': 'Covers the types of data including nominal, ordinal, and quantitative data with examples and descriptions, and delves into the basics of linear algebra including linear equations, matrices, and vectors, with detailed explanations of their operations and applications.', 'duration': 768.496, 'highlights': ['The chapter covers the types of data including nominal, ordinal, and quantitative data with examples and descriptions. The chapter provides a comprehensive overview of the types of data, explaining nominal, ordinal, and quantitative data with examples such as country, gender, race for nominal data, and salary range, movie ratings for ordinal data.', 'The chapter delves into the basics of linear algebra including linear equations, matrices, and vectors, with detailed explanations of their operations and applications. The chapter explains linear algebra concepts such as linear equations involving variables, matrices operations like addition, subtraction, multiplication, transpose, inverse, and vectors representing values and directions, with practical applications and detailed examples.']}, {'end': 13104.188, 'start': 12682.345, 'title': 'Understanding i-gene vectors and values', 'summary': 'Explains the concept of i-gene vectors and values, demonstrating their impact on transforming data using linear algebra and numpy, showcasing operations like addition, subtraction, scalar multiplication, dot product, and complex matrix creation.', 'duration': 421.843, 'highlights': ['The chapter explains the concept of i-gene vectors and values, demonstrating their impact on transforming data using linear algebra and numpy. The concept of i-gene vectors and values is explained in the context of transforming data using linear algebra and numpy, showcasing their impact on the manipulation of vectors and scalar values.', 'Operations like addition, subtraction, scalar multiplication, dot product, and complex matrix creation are demonstrated. The demonstration includes various operations such as addition, subtraction, scalar multiplication, dot product, and the creation of complex matrices, showcasing their application in linear algebra and numpy.']}, {'end': 13453.54, 'start': 13104.208, 'title': 'Matrix math operations in numpy', 'summary': 'Covers various matrix operations including addition, subtraction, scalar multiplication, matrix and vector multiplication, matrix to matrix multiplication, transpose, identity matrix, and inverse matrix in numpy, with examples and use cases for data science and plotting.', 'duration': 349.332, 'highlights': ['The chapter covers various matrix operations including addition, subtraction, scalar multiplication, matrix and vector multiplication, matrix to matrix multiplication, transpose, identity matrix, and inverse matrix in NumPy, with examples and use cases for data science and plotting.', 'When we do a simple vector addition, we have 12 plus 2 is 14, 10 plus 8 is 18, and so on.', "Now if you remember up here we had a scalar addition where we're adding just one number to a matrix, you can also do scalar multiplication. When we run that, you can see here we have 2 times 4 is 8, 5 times 4 is 20, and so forth.", "Another tool that we didn't discuss is your identity matrix. The identity matrix creates a diagonal of one and is used for comparing different features and how they correlate.", 'An inverse A matrix is discussed, involving the concept of a square matrix and the matrix A inverse satisfying dot A inverse equals the identity shape of A dot shape zero.']}], 'duration': 1603.93, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA11849610.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 inverse matrix in NumPy, with examples and use cases for data science and plotting.', 'The chapter delves into the basics of linear algebra including linear equations, matrices, and vectors, with detailed explanations of their operations and applications. The chapter explains linear algebra concepts such as linear equations involving variables, matrices operations like addition, subtraction, multiplication, transpose, inverse, and vectors representing values and directions, with practical applications and detailed examples.', 'The chapter explains the concept of i-gene vectors and values, demonstrating their impact on transforming data using linear algebra and numpy. The concept of i-gene vectors and values is explained in the context of transforming data using linear algebra and numpy, showcasing their impact on the manipulation of vectors and scalar values.', 'The print accuracy score is .818, which can be rounded off to 80%, indicating a fair fit in the model.', 'The agenda covers data types, linear algebra, calculus, statistics, and probability for machine learning, along with hands-on demos.']}, {'end': 14459.977, 'segs': [{'end': 13835.311, 'src': 'embed', 'start': 13804.901, 'weight': 0, 'content': [{'end': 13806.503, 'text': "That's what this integral sign means.", 'start': 13804.901, 'duration': 1.602}, {'end': 13809.305, 'text': 'The sum of a of x, d of x equals a plus c.', 'start': 13807.203, 'duration': 2.102}, {'end': 13817.513, 'text': 'And when you see these very complicated multivariate differentiation using the chain rule,', 'start': 13811.587, 'duration': 5.926}, {'end': 13825.04, 'text': 'when we come in here and we have the change of W to the change of T equals a change of W, dz and so forth.', 'start': 13817.513, 'duration': 7.527}, {'end': 13826.742, 'text': "That's what's going on here.", 'start': 13825.661, 'duration': 1.081}, {'end': 13827.663, 'text': "That's what these means.", 'start': 13826.762, 'duration': 0.901}, {'end': 13835.311, 'text': "We're basically looking for the area under the curve which really comes to how is the change changing? Speed's going up.", 'start': 13827.823, 'duration': 7.488}], 'summary': 'Integral sign indicates area under the curve. multivariate differentiation uses chain rule. change of w to change of t equals change of w, dz.', 'duration': 30.41, 'max_score': 13804.901, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA13804901.jpg'}, {'end': 13950.654, 'src': 'embed', 'start': 13903.223, 'weight': 1, 'content': [{'end': 13905.664, 'text': "So there's our multiple variables going in there.", 'start': 13903.223, 'duration': 2.441}, {'end': 13908.865, 'text': 'If one variable is changing, how does it affect the other variable?', 'start': 13906.184, 'duration': 2.681}, {'end': 13915.731, 'text': 'And then in gradient descent calculus is used to find the local and global maxima.', 'start': 13910.086, 'duration': 5.645}, {'end': 13917.832, 'text': 'And this is really big.', 'start': 13916.551, 'duration': 1.281}, {'end': 13924.298, 'text': "We're actually going to have a whole section here on gradient descent, because it is really I mean.", 'start': 13918.473, 'duration': 5.825}, {'end': 13927.981, 'text': 'I talked about neural networks and how you can see how the different layers go in there.', 'start': 13924.298, 'duration': 3.683}, {'end': 13934.686, 'text': 'But gradient descent is one of the most key things for trying to guess the best answer to something.', 'start': 13928.341, 'duration': 6.345}, {'end': 13940.77, 'text': "So let's take a look at the code behind gradient descent.", 'start': 13935.547, 'duration': 5.223}, {'end': 13945.712, 'text': "And before we open up the code, let's just do real quick.", 'start': 13941.49, 'duration': 4.222}, {'end': 13946.893, 'text': 'Gradient descent.', 'start': 13945.872, 'duration': 1.021}, {'end': 13950.654, 'text': "Let's say we have a curve like this.", 'start': 13949.054, 'duration': 1.6}], 'summary': 'Gradient descent is crucial in finding maxima, with a focus on neural networks and code implementation.', 'duration': 47.431, 'max_score': 13903.223, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA13903223.jpg'}, {'end': 14302.023, 'src': 'embed', 'start': 14267.118, 'weight': 4, 'content': [{'end': 14276.327, 'text': 'and with the sklearn kit, and one of the nice reasons of breaking this down the way we did is i could go over those top pieces.', 'start': 14267.118, 'duration': 9.209}, {'end': 14278.148, 'text': 'those top pieces are everything.', 'start': 14276.327, 'duration': 1.821}, {'end': 14289.538, 'text': "you start looking at these minimization toolkits in built-in code and so from we'll just do it's actually docs dot,", 'start': 14278.148, 'duration': 11.39}, {'end': 14295.881, 'text': "scipy.org and we're looking at the scikit.", 'start': 14290.499, 'duration': 5.382}, {'end': 14297.161, 'text': 'There we go.', 'start': 14296.741, 'duration': 0.42}, {'end': 14299.522, 'text': 'Optimize, minimize.', 'start': 14298.001, 'duration': 1.521}, {'end': 14302.023, 'text': 'You can only minimize one value.', 'start': 14300.482, 'duration': 1.541}], 'summary': 'Using sklearn kit to optimize and minimize one value.', 'duration': 34.905, 'max_score': 14267.118, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA14267118.jpg'}, {'end': 14345.483, 'src': 'embed', 'start': 14320.455, 'weight': 2, 'content': [{'end': 14327.181, 'text': "So your function, your start value, there's all kinds of things that come in here that you can look at which we're not going to.", 'start': 14320.455, 'duration': 6.726}, {'end': 14331.565, 'text': 'Optimization automatically creates, constraints, bounds.', 'start': 14328.102, 'duration': 3.463}, {'end': 14338.113, 'text': 'Some of this it does automatically, but the big thing I want to point out here is you need to have a starting point.', 'start': 14332.446, 'duration': 5.667}, {'end': 14341.718, 'text': 'You want to start with something that you already know is mostly the answer.', 'start': 14338.654, 'duration': 3.064}, {'end': 14345.483, 'text': "If you don't, then it's going to have a heck of a time trying to calculate it out.", 'start': 14342.679, 'duration': 2.804}], 'summary': 'Optimization requires a starting point for accurate calculation.', 'duration': 25.028, 'max_score': 14320.455, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA14320455.jpg'}], 'start': 13453.56, 'title': 'Math and calculus in data science', 'summary': 'Emphasizes the importance of understanding linear algorithms, calculus, and differential equations in data science, particularly their application in machine learning and numerical optimization. it discusses the relevance of calculus in predicting model accuracy, finding maxima, and the importance of precision in numerical optimization.', 'chapters': [{'end': 13548.251, 'start': 13453.56, 'title': 'Understanding math in data science', 'summary': 'Explains the importance of understanding linear algorithms, calculus, and differential equations in data science, emphasizing the need to grasp concepts rather than manually solving math equations, and how calculus and differential equations are crucial in machine learning, especially for large neural networks.', 'duration': 94.691, 'highlights': ['Understanding the importance of linear algorithms in data science and the need to know about the linear algorithm inverse of A for easy access, or at least remembering where to look it up.', 'Explaining the significance of calculus and differential equations in machine learning, especially for large neural networks, and how most of the work is already done in the back end, emphasizing the need to understand these concepts in data science.', 'Illustrating the application of calculus in data science by discussing the calculation of the spontaneous rate of change using the example of plotting a graph of the speed of a car with respect to time.']}, {'end': 14123.306, 'start': 13548.892, 'title': 'Understanding calculus and its applications', 'summary': 'Discusses the concept of acceleration, the integration process in calculus, the multivariate calculus, and its applications in neural networks and gradient descent, emphasizing the importance of calculus in predicting model accuracy and finding local and global maxima, with a focus on the back-end scripting and code implementation.', 'duration': 574.414, 'highlights': ['Calculus is the integral of the acceleration function, involving the computation of the slope under smaller and smaller samples, and is used to find the area under the slope, indicating the rate of change. The integral of the acceleration function involves computing the slope under smaller and smaller samples, and is used to find the area under the slope, indicating the rate of change.', 'Multivariate calculus deals with functions that have multiple variables and involves complex equations and double integrals, requiring a deep understanding and usually covered in calculus one, calculus two, and differential equations courses. Multivariate calculus deals with functions that have multiple variables, involving complex equations and double integrals, usually covered in calculus one, calculus two, and differential equations courses.', 'Calculus is essential in neural networks and reverse propagation, providing mathematical solutions for solving complex multivariate differentiations and integrations, which are crucial for data analysis and back-end scripting. Calculus is essential in neural networks and reverse propagation, providing mathematical solutions for solving complex multivariate differentiations and integrations, crucial for data analysis and back-end scripting.', 'Gradient descent, a key concept in calculus, is used to find local and global maxima and involves minimizing error or maximizing output, with heavy lifting done using calculus and differential equations for calculation. Gradient descent is used to find local and global maxima, involving minimizing error or maximizing output, with heavy lifting done using calculus and differential equations for calculation.']}, {'end': 14459.977, 'start': 14123.906, 'title': 'Precision in numerical optimization', 'summary': 'Discusses the importance of precision in numerical optimization, including considerations for step size, precision, max iterations, and the application of statistical terminologies in data analysis and interpretation.', 'duration': 336.071, 'highlights': ['The local minimum occurs at x on here. The program outputs the local minimum value of -3.3222 for the given series, calculated using the formula lambda x2 times x plus 5.', 'Precision tells us when to stop the algorithm. Precision plays a crucial role in determining when to halt the algorithm, ensuring accurate results based on specific requirements, such as dealing with money and avoiding floating point errors when working with small increments like 0.001.', 'Max iterations are typically limited to 100 or 200, occasionally up to 400 or 500 depending on the problem. The maximum iterations are usually constrained to 100 or 200 and occasionally extended to 400 or 500 based on the complexity of the problem, with rare occurrences of max iterations exceeding these values.']}], 'duration': 1006.417, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA13453560.jpg', 'highlights': ['Calculus is essential in neural networks and reverse propagation, providing mathematical solutions for solving complex multivariate differentiations and integrations, crucial for data analysis and back-end scripting.', 'Gradient descent, a key concept in calculus, is used to find local and global maxima, involving minimizing error or maximizing output, with heavy lifting done using calculus and differential equations for calculation.', 'Precision plays a crucial role in determining when to halt the algorithm, ensuring accurate results based on specific requirements, such as dealing with money and avoiding floating point errors when working with small increments like 0.001.', 'Max iterations are usually constrained to 100 or 200 and occasionally extended to 400 or 500 based on the complexity of the problem, with rare occurrences of max iterations exceeding these values.', 'Understanding the importance of linear algorithms in data science and the need to know about the linear algorithm inverse of A for easy access, or at least remembering where to look it up.', 'Explaining the significance of calculus and differential equations in machine learning, especially for large neural networks, and how most of the work is already done in the back end, emphasizing the need to understand these concepts in data science.', 'Illustrating the application of calculus in data science by discussing the calculation of the spontaneous rate of change using the example of plotting a graph of the speed of a car with respect to time.']}, {'end': 16894.142, 'segs': [{'end': 14945.434, 'src': 'embed', 'start': 14914.55, 'weight': 2, 'content': [{'end': 14918.112, 'text': 'half the numbers on the other side of the line, we end up with 5 in the middle.', 'start': 14914.55, 'duration': 3.562}, {'end': 14922.394, 'text': 'And then the mode what mark was scored by most of the students in a test?', 'start': 14918.192, 'duration': 4.202}, {'end': 14930.262, 'text': 'In a simple case where most people scored like an 82% and got certain problems wrong, easy to figure out.', 'start': 14923.496, 'duration': 6.766}, {'end': 14937.247, 'text': "Not so easy when you have different areas where like you have like the, oh let's go back to economy.", 'start': 14930.982, 'duration': 6.265}, {'end': 14945.434, 'text': 'A little bit more difficult to calculate if you have a large group that scores that makes 30,000 and a slightly bigger group that makes 26,000.', 'start': 14937.267, 'duration': 8.167}], 'summary': 'Most students scored 82% on the test, making it easy to figure out.', 'duration': 30.884, 'max_score': 14914.55, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA14914550.jpg'}, {'end': 15603.538, 'src': 'embed', 'start': 15580.579, 'weight': 4, 'content': [{'end': 15589.787, 'text': "So some of the things you want to take away in addition to this is that it's very easy to plot an AXV line.", 'start': 15580.579, 'duration': 9.208}, {'end': 15592.569, 'text': 'These are these up and down lines for your markers.', 'start': 15590.407, 'duration': 2.162}, {'end': 15598.634, 'text': 'And as you display the data, I mean, you can add all kinds of things to this and get really complicated.', 'start': 15593.53, 'duration': 5.104}, {'end': 15600.516, 'text': 'Keeping it simple is pretty straightforward.', 'start': 15599.014, 'duration': 1.502}, {'end': 15603.538, 'text': 'I look at this and I can see we have a major outlier out here.', 'start': 15600.536, 'duration': 3.002}], 'summary': 'Plotting axv line for markers is easy. keeping data simple is straightforward.', 'duration': 22.959, 'max_score': 15580.579, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA15580579.jpg'}, {'end': 16167.962, 'src': 'embed', 'start': 16143.203, 'weight': 0, 'content': [{'end': 16149.066, 'text': "You have your peak, in this case it's a normal distribution so you have the nice bell curve equal on both sides, it's not asymmetrical.", 'start': 16143.203, 'duration': 5.863}, {'end': 16155.809, 'text': 'And 95% of all the values lie within a very small range and then you have your outliers, the 2.5% going each way.', 'start': 16149.566, 'duration': 6.243}, {'end': 16159.856, 'text': 'So we touched upon hypothesis.', 'start': 16157.935, 'duration': 1.921}, {'end': 16162.638, 'text': "We're going to move into probability.", 'start': 16160.517, 'duration': 2.121}, {'end': 16164.379, 'text': 'So you have your hypothesis.', 'start': 16163.259, 'duration': 1.12}, {'end': 16167.962, 'text': "Once you've generated your hypothesis, we want to know the probability of something occurring.", 'start': 16164.479, 'duration': 3.483}], 'summary': 'Discussed normal distribution with 95% values within a small range and outliers on 2.5% each side. also touched upon hypothesis and probability.', 'duration': 24.759, 'max_score': 16143.203, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA16143203.jpg'}, {'end': 16626.288, 'src': 'embed', 'start': 16596.545, 'weight': 3, 'content': [{'end': 16600.427, 'text': 'Around 68% of the results are found between one standard deviation.', 'start': 16596.545, 'duration': 3.882}, {'end': 16605.187, 'text': 'Around 95% of the results are found between two standard deviations.', 'start': 16601.127, 'duration': 4.06}, {'end': 16607.369, 'text': 'And you read the symbols.', 'start': 16605.968, 'duration': 1.401}, {'end': 16609.532, 'text': 'Of course, they love to throw some Greek letters in there.', 'start': 16607.55, 'duration': 1.982}, {'end': 16612.194, 'text': 'We have mu minus two sigma.', 'start': 16609.712, 'duration': 2.482}, {'end': 16614.817, 'text': 'Mu is just a quick way.', 'start': 16612.814, 'duration': 2.003}, {'end': 16616.758, 'text': "It's a kind of funky U.", 'start': 16614.837, 'duration': 1.921}, {'end': 16617.86, 'text': 'It just means the mean.', 'start': 16616.758, 'duration': 1.102}, {'end': 16621.323, 'text': 'And then the sigma is the standard deviation.', 'start': 16618.78, 'duration': 2.543}, {'end': 16626.288, 'text': "And that's the O with the little arrow off to the right or the little waggly tail going up.", 'start': 16621.483, 'duration': 4.805}], 'summary': '68% within 1 standard deviation, 95% within 2. explains symbols mu and sigma.', 'duration': 29.743, 'max_score': 16596.545, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA16596544.jpg'}, {'end': 16846.166, 'src': 'embed', 'start': 16817.492, 'weight': 1, 'content': [{'end': 16820.234, 'text': 'And this is kind of interesting the way they word this.', 'start': 16817.492, 'duration': 2.742}, {'end': 16829.918, 'text': "Let's say that according to medical report provided by the hospital, states that around 10% of all patients they treated suffered lung disease.", 'start': 16820.814, 'duration': 9.104}, {'end': 16833.659, 'text': 'So we have kind of a generic medical report.', 'start': 16831.058, 'duration': 2.601}, {'end': 16839.942, 'text': 'They further found out by a survey that 15% of the patients that visit them smoke.', 'start': 16834.339, 'duration': 5.603}, {'end': 16846.166, 'text': 'So we have 10% that are lung disease and 15% of the patients smoke.', 'start': 16841.122, 'duration': 5.044}], 'summary': 'Around 10% of patients treated suffered lung disease, and 15% of patients smoke.', 'duration': 28.674, 'max_score': 16817.492, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA16817492.jpg'}], 'start': 14460.818, 'title': 'Statistics and probability', 'summary': 'Covers various statistical concepts and techniques such as sampling methods, descriptive and inferential statistics, statistics in python with pandas, probability, and hypothesis in data science, providing a comprehensive overview of essential statistical knowledge and practical applications.', 'chapters': [{'end': 14691.745, 'start': 14460.818, 'title': 'Sampling in statistics', 'summary': 'Discusses the concept of population, sample, parameter, and variable in statistics, along with types of sampling such as probabilistic and non-probabilistic approaches, including random, systematic, and stratified sampling.', 'duration': 230.927, 'highlights': ['The chapter explains the concept of population, sample, parameter, and variable in statistics, providing insights into the measurement and observation of different objects and characteristics. It clarifies that population comprises all objects being observed, and sample represents a subset of the population studied, highlighting the importance of variables in statistical analysis.', 'It outlines the types of sampling, including probabilistic approaches such as random, systematic, and stratified sampling, and non-probabilistic approaches based on subjective judgment, with emphasis on the potential biases in non-probabilistic sampling. The chapter distinguishes probabilistic approaches involving methods based on probability theory from non-probabilistic approaches, highlighting the biases associated with non-probabilistic sampling and the importance of careful consideration in sampling methods.', 'It discusses the specifics of random, systematic, and stratified sampling, providing insights into their applications and the considerations for selecting equal size samples from different groups or categories. It delves into the characteristics of random, systematic, and stratified sampling, emphasizing their applications in selecting samples from different groups or categories, including the significance of representing diverse cultures and categories in stratified sampling.']}, {'end': 15125.79, 'start': 14692.426, 'title': 'Statistics: descriptive vs. inferential', 'summary': 'Discusses the differences between descriptive and inferential statistics, covering measures of central tendencies, spread, variance, and standard deviation, and their applications, including predicting drug effectiveness and understanding income distribution.', 'duration': 433.364, 'highlights': ['Descriptive vs. Inferential Statistics The chapter explains the differences between descriptive and inferential statistics, emphasizing the importance of studying diverse groups to avoid misinformation.', 'Measures of Central Tendencies and Spread The chapter covers measures of central tendencies (mean, median, mode) and spread (range, interquartile range, variance, standard deviation), providing examples of their applications, such as analyzing income distribution and student marks.', 'Predicting Drug Effectiveness The chapter illustrates how inferential statistics can be used to predict drug effectiveness by analyzing data from a small population and inferring its impact on the greater populace, citing an example of an 80% better survival rate for drug recipients.']}, {'end': 15345.629, 'start': 15127.748, 'title': 'Statistics in python with pandas', 'summary': 'Demonstrates how to use pandas in python to calculate statistics such as average, median, mode, and range for a dataset, revealing insights and discrepancies in the data.', 'duration': 217.881, 'highlights': ['The average income in the dataset is $71,000, while the median is $54,000, and the mode is $50,000, indicating varying income levels and a common trend of high and low salaries. The average income in the dataset is $71,000, while the median is $54,000, and the mode is $50,000, indicating varying income levels and a common trend of high and low salaries.', "The analysis shows a significant difference between the median and the average due to a high-income outlier of $189,000, which affects the overall distribution of incomes and raises questions about the data's representation. The analysis shows a significant difference between the median and the average due to a high-income outlier of $189,000, which affects the overall distribution of incomes and raises questions about the data's representation.", 'The chapter emphasizes the importance of considering median, mode, and range in addition to the average when analyzing statistics to uncover discrepancies and insightful trends within the data. The chapter emphasizes the importance of considering median, mode, and range in addition to the average when analyzing statistics to uncover discrepancies and insightful trends within the data.']}, {'end': 15857.975, 'start': 15345.729, 'title': 'Descriptive and inferential statistics', 'summary': 'Covers basic statistics, including minimum, maximum, range, mean, quartiles, and descriptive statistics using pandas, and then delves into inferential statistics, including point estimation, hypothesis testing, and applications.', 'duration': 512.246, 'highlights': ['The chapter covers basic statistics such as minimum, maximum, range, mean, quartiles, and descriptive statistics using Pandas, including the count, mean, standard deviation, minimum, maximum, and quartiles, providing a quick and comprehensive way to analyze data. Describes basic statistics and the use of Pandas to generate descriptive statistics, providing a quick and comprehensive way to analyze data.', 'The chapter delves into inferential statistics, including point estimation, hypothesis testing, and applications, with a focus on testing vaccines for COVID-19 and medical trials, and introduces concepts such as hypotheses testing, theorems, and theory. Explores inferential statistics, including point estimation, hypothesis testing, and applications, with a focus on testing vaccines for COVID-19 and medical trials, and introduces concepts such as hypotheses testing, theorems, and theory.']}, {'end': 16512.854, 'start': 15858.135, 'title': 'Probability and hypothesis in data science', 'summary': 'Discusses the probability of rob not doing the cleaning job for 12 consecutive days, the concepts of null and alternative hypotheses, p-value, t-value, confidence intervals, random variables, and binomial distribution in the context of probability and hypothesis in data science.', 'duration': 654.719, 'highlights': ['The probability of Rob not doing work on day one is three out of four, with a 0.75 chance, and decreases to 0.032 by day 12, indicating a high likelihood of mischief (cheating) in preparing the chits.', 'Explanation of null hypothesis and alternative hypothesis, where null hypothesis states no relationship between two phenomena, and alternative hypothesis prefers a new theory over an old one, relevant to data science and statistics.', 'Explanation of p-value as the probability of finding observed or more extreme results when the null hypothesis is true, and t-value as the calculated difference represented in units of standard error, with a focus on the 5% or 0.05 significance level.', 'Illustration of confidence intervals as a range of values where true observations lie, exemplified by a scenario of dog owners buying 200 to 300 cans of food per year with a 95% confidence interval.', 'Definition of random variables as numerical outcomes of a random phenomena, and the application of probability in predicting events such as sports scores, weather, and stock performance.', 'Explanation of binomial distribution as the probability of success or failure in multiple trials, demonstrated through the example of calculating the chances of Barcelona winning a football series with a 75% winning chance per game.']}, {'end': 16894.142, 'start': 16513.054, 'title': 'Understanding probability and data analysis', 'summary': 'Explains the importance of mean, standard deviation, z-score, central limit theorem, and conditional probability in understanding skewed data, normal distribution, and data analysis, with practical examples and calculations.', 'duration': 381.088, 'highlights': ["The Z-score tells you how far from the mean a data point is, with 68% and 95% of results found within one and two standard deviations, respectively. The Z-score provides a measure of the data's position in terms of standard deviations from the mean, with 68% and 95% of results found within one and two standard deviations, providing insights into the distribution of the data.", 'The chapter emphasizes the importance of understanding skewed data and normal distribution in large populations, with the central limit theorem ensuring the approximately normal distribution of sample means from large random samples. The central limit theorem highlights the significance of understanding skewed data and normal distribution, with large random samples from the population demonstrating an approximately normal distribution of sample means, indicating the importance of data integrity and analysis in large populations.', 'The chapter delves into conditional probability through a practical example, calculating the probability of a patient having lung disease if they smoke, integrating prior probabilities and survey data to derive a solution. The chapter illustrates the application of conditional probability through a practical example, demonstrating the calculation of the probability of a patient having lung disease if they smoke by integrating prior probabilities and survey data to derive a solution, showcasing the practical implications of probability in real-world scenarios.']}], 'duration': 2433.324, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA14460818.jpg', 'highlights': ['The chapter covers basic statistics such as minimum, maximum, range, mean, quartiles, and descriptive statistics using Pandas, providing a quick and comprehensive way to analyze data.', 'The chapter delves into inferential statistics, including point estimation, hypothesis testing, and applications, with a focus on testing vaccines for COVID-19 and medical trials, and introduces concepts such as hypotheses testing, theorems, and theory.', 'The chapter emphasizes the importance of understanding skewed data and normal distribution in large populations, with the central limit theorem ensuring the approximately normal distribution of sample means from large random samples.', 'The chapter explains the differences between descriptive and inferential statistics, emphasizing the importance of studying diverse groups to avoid misinformation.', 'The chapter illustrates the application of conditional probability through a practical example, demonstrating the calculation of the probability of a patient having lung disease if they smoke by integrating prior probabilities and survey data to derive a solution, showcasing the practical implications of probability in real-world scenarios.']}, {'end': 18336.776, 'segs': [{'end': 17182.721, 'src': 'embed', 'start': 17153.231, 'weight': 3, 'content': [{'end': 17154.332, 'text': "And we'll go ahead and just run this.", 'start': 17153.231, 'duration': 1.101}, {'end': 17160.378, 'text': 'And you can see here it just, again, puts it through all the different possible variables we can have.', 'start': 17155.493, 'duration': 4.885}, {'end': 17170.673, 'text': 'And then, if we want to take the same set on here and print them all out like we had before, we can just go through for outcome and event space,', 'start': 17161.246, 'duration': 9.427}, {'end': 17172.014, 'text': 'outcome and equals.', 'start': 17170.673, 'duration': 1.341}, {'end': 17177.397, 'text': 'So the event space is creating a sequence.', 'start': 17172.994, 'duration': 4.403}, {'end': 17182.721, 'text': 'And as you can see here, when we print it out, it stacks them versus going through and putting them in a nice line.', 'start': 17177.457, 'duration': 5.264}], 'summary': 'Running the sequence through different variables and creating a sequence for event space.', 'duration': 29.49, 'max_score': 17153.231, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA17153231.jpg'}, {'end': 17780.723, 'src': 'embed', 'start': 17755.148, 'weight': 2, 'content': [{'end': 17761.176, 'text': "where there's a And then a belief that there is a independent assumption between the features,", 'start': 17755.148, 'duration': 6.028}, {'end': 17764.678, 'text': 'where the features are very assumed to have some kind of connection.', 'start': 17761.176, 'duration': 3.502}, {'end': 17768.639, 'text': 'Then we can go ahead and use that for the prediction.', 'start': 17765.678, 'duration': 2.961}, {'end': 17774.321, 'text': "And so that's what we're using as a naive Bayes classifier versus many of the other classifiers that are out there.", 'start': 17768.839, 'duration': 5.482}, {'end': 17780.723, 'text': "For this, we're going to use the social network ads.", 'start': 17776.922, 'duration': 3.801}], 'summary': 'Using naive bayes classifier for social network ads prediction.', 'duration': 25.575, 'max_score': 17755.148, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA17755148.jpg'}, {'end': 17906.73, 'src': 'embed', 'start': 17877.435, 'weight': 7, 'content': [{'end': 17880.257, 'text': "so those are the three columns we're going to be looking at when we do this.", 'start': 17877.435, 'duration': 2.822}, {'end': 17883.458, 'text': "and we've gone ahead and imported these and imported the data.", 'start': 17880.257, 'duration': 3.201}, {'end': 17892.003, 'text': "so now our data set is all set with this information in it and we'll need to go ahead and split the data up.", 'start': 17883.458, 'duration': 8.545}, {'end': 17892.824, 'text': 'so we need our.', 'start': 17892.003, 'duration': 0.821}, {'end': 17898.286, 'text': 'from the sklearn model selection we can import train test, split.', 'start': 17892.824, 'duration': 5.462}, {'end': 17899.587, 'text': 'this does a nice job.', 'start': 17898.286, 'duration': 1.301}, {'end': 17906.73, 'text': "we can set the random state so randomly picks the data and we're just going to take 25 percent of it's going to go into the test,", 'start': 17899.587, 'duration': 7.143}], 'summary': 'Data set split into 75% training and 25% test data', 'duration': 29.295, 'max_score': 17877.435, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA17877435.jpg'}, {'end': 18095.4, 'src': 'embed', 'start': 18063.249, 'weight': 6, 'content': [{'end': 18064.67, 'text': 'So X train, Y train.', 'start': 18063.249, 'duration': 1.421}, {'end': 18068.192, 'text': "And we'll go ahead and run this.", 'start': 18067.091, 'duration': 1.101}, {'end': 18071.235, 'text': "It's going to tell us that it ran the code right there.", 'start': 18068.633, 'duration': 2.602}, {'end': 18075.858, 'text': 'And now we have our trained classifier model.', 'start': 18073.056, 'duration': 2.802}, {'end': 18079.781, 'text': 'So the next step is we need to go ahead and run a prediction.', 'start': 18077.079, 'duration': 2.702}, {'end': 18084.766, 'text': "We're going to do our Y predict equals the classifier dot predict X test.", 'start': 18079.921, 'duration': 4.845}, {'end': 18088.349, 'text': "So here we fit the data and now we're going to go ahead and predict.", 'start': 18085.346, 'duration': 3.003}, {'end': 18095.4, 'text': 'And now we get to our confusion matrix.', 'start': 18092.296, 'duration': 3.104}], 'summary': 'Trained a classifier model, ran prediction, and obtained a confusion matrix.', 'duration': 32.151, 'max_score': 18063.249, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA18063249.jpg'}, {'end': 18266.114, 'src': 'embed', 'start': 18218.746, 'weight': 0, 'content': [{'end': 18220.607, 'text': "There's so many different ways to put this code together.", 'start': 18218.746, 'duration': 1.861}, {'end': 18227.45, 'text': "To show you what we're doing, it's a lot easier to pull up the graph and then go back up and explain it.", 'start': 18220.627, 'duration': 6.823}, {'end': 18235.168, 'text': "So the first thing we want to note here when we're looking at the data is this is the training set.", 'start': 18228.604, 'duration': 6.564}, {'end': 18238.91, 'text': "And so we have those who didn't make a purchase.", 'start': 18236.449, 'duration': 2.461}, {'end': 18240.911, 'text': "We've drawn a nice area for that.", 'start': 18239.05, 'duration': 1.861}, {'end': 18243.813, 'text': "It's defined by the naive Bayes setup.", 'start': 18241.832, 'duration': 1.981}, {'end': 18247.035, 'text': 'And then we have those who did make a purchase, the green.', 'start': 18244.513, 'duration': 2.522}, {'end': 18252.518, 'text': 'And you can see that some of the green dots fall into the red area and some of the red dots fall into the green.', 'start': 18247.315, 'duration': 5.203}, {'end': 18255.239, 'text': "So even our training set isn't going to be 100%.", 'start': 18253.198, 'duration': 2.041}, {'end': 18255.72, 'text': "We couldn't do that.", 'start': 18255.239, 'duration': 0.481}, {'end': 18260.946, 'text': "And so we're looking at our different data coming down.", 'start': 18258.362, 'duration': 2.584}, {'end': 18266.114, 'text': 'We can kind of arrange our X1, X2 so we have a nice plot going on.', 'start': 18262.228, 'duration': 3.886}], 'summary': 'Analyzing training set for purchase prediction using naive bayes setup and visualizing data with x1, x2 plot.', 'duration': 47.368, 'max_score': 18218.746, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA18218746.jpg'}, {'end': 18326.568, 'src': 'embed', 'start': 18294.569, 'weight': 1, 'content': [{'end': 18296.049, 'text': 'You have all your X1s and all your X2s.', 'start': 18294.569, 'duration': 1.48}, {'end': 18299.811, 'text': "So this is what we're kind of looking for right here on this setup.", 'start': 18296.109, 'duration': 3.702}, {'end': 18306.111, 'text': 'And then the scatter plot is, of course, your scattered data across there.', 'start': 18301.827, 'duration': 4.284}, {'end': 18307.352, 'text': "We're just going through all the points.", 'start': 18306.131, 'duration': 1.221}, {'end': 18310.935, 'text': 'That puts these nice little dots onto our setup on here.', 'start': 18307.732, 'duration': 3.203}, {'end': 18313.537, 'text': 'And we have our estimated salary and our age.', 'start': 18311.295, 'duration': 2.242}, {'end': 18316.259, 'text': 'And then, of course, the dots are did they make a purchase or not.', 'start': 18313.737, 'duration': 2.522}, {'end': 18318.942, 'text': 'And just a quick note, this is kind of funny.', 'start': 18317.18, 'duration': 1.762}, {'end': 18326.568, 'text': 'You can see up here where it says X set, Y set equals X train, Y train, which seems kind of a little weird to do.', 'start': 18319.362, 'duration': 7.206}], 'summary': 'Analyzing x1s and x2s for scatter plot data visualization and model training.', 'duration': 31.999, 'max_score': 18294.569, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA18294569.jpg'}], 'start': 16895.287, 'title': 'Python sets, probability, model metrics, and naive bayes', 'summary': 'Covers python sets, probability of dice outcomes, confusion matrix, model accuracy metrics, naive bayes classifier, and model creation achieving 65 correct predictions and 3 incorrect predictions.', 'chapters': [{'end': 17202.029, 'start': 16895.287, 'title': 'Python sets and iteration', 'summary': 'Explains the concept of sets in python, demonstrating how to create sets with unique values and perform operations such as conversion from list to set, checking for membership, and using iteration tools for dice outcomes.', 'duration': 306.742, 'highlights': ['The chapter demonstrates creating sets with unique values in Python, with an example showing the conversion of a list to a set and the removal of duplicate values, highlighting the concept of uniqueness in sets.', 'It explains the usage of sets for checking membership and logical operations, showcasing the functionality of sets in Python and how they can be utilized for efficient data manipulation.', 'The chapter introduces iteration tools in Python and demonstrates their application for creating a tuple of all possible outcomes of dice, providing a practical example of using iteration tools for generating combinations of values.']}, {'end': 17598.178, 'start': 17202.589, 'title': 'Probability and confusion matrix in data science', 'summary': 'Discusses the probability of obtaining multiples of three and five when rolling dice, resulting in a .3333 chance for multiples of three and a .116255 chance for multiples of five but not multiples of three. it then delves into the concept of a confusion matrix, emphasizing the significance of true positive and false positive predictions, particularly in medical scenarios.', 'duration': 395.589, 'highlights': ['The probability of obtaining multiples of three when rolling dice results in a .3333 chance. The code calculates the probability of obtaining multiples of three when rolling dice, resulting in a .3333 chance.', 'The probability of obtaining multiples of five but not multiples of three when rolling dice results in a .116255 chance. The computation yields a .116255 chance of obtaining multiples of five but not multiples of three when rolling dice.', 'Explanation of the confusion matrix and its significance in evaluating the performance of a classification model, particularly in medical scenarios. The transcript explains the concept of a confusion matrix and its importance in evaluating the performance of a classification model, particularly emphasizing the significance of true positive and false positive predictions in medical scenarios.']}, {'end': 17877.435, 'start': 17599.352, 'title': 'Model accuracy metrics and naive bayes classifier', 'summary': 'Discusses the importance of accuracy, precision, and recall metrics in evaluating model performance, particularly in scenarios like cancer detection and covid testing, while also providing an overview of the naive bayes classifier and its application using the sklearn package.', 'duration': 278.083, 'highlights': ['Importance of Accuracy, Precision, and Recall Metrics The chapter emphasizes the significance of accuracy, precision, and recall metrics in assessing model performance, particularly in scenarios like cancer detection and COVID testing, to minimize false negatives and false positives and ensure high accuracy and precision.', 'Overview of Naive Bayes Classifier The discussion provides an overview of the Naive Bayes classifier, highlighting its simplicity and independent assumption between features, along with its application using the sklearn package for classification tasks like the social network ads dataset.', 'Application of Metrics in Model Evaluation The chapter illustrates the practical application of accuracy, precision, and recall metrics in evaluating model performance, particularly in scenarios like cancer detection and COVID testing, to ensure the effectiveness of the predictive model.']}, {'end': 18336.776, 'start': 17877.435, 'title': 'Naive bayes model creation', 'summary': 'Covers the creation of a naive bayes model, including data preprocessing, model creation, prediction, confusion matrix generation, and result visualization, achieving an accuracy of 65 purchases correctly predicted and 3 incorrectly predicted.', 'duration': 459.341, 'highlights': ['The chapter covers the creation of a Naive Bayes model, including data preprocessing, model creation, prediction, confusion matrix generation, and result visualization, achieving an accuracy of 65 purchases correctly predicted and 3 incorrectly predicted.', 'The data is split into 25% for testing and 75% for training, and then scaled to balance out the impact of different features, such as age and salary.', 'The Gaussian Naive Bayes model is created and trained using the training data, and then used to predict the outcomes of the test data, resulting in a confusion matrix indicating 65 correctly predicted purchases, 3 incorrectly predicted purchases, 25 correctly predicted non-purchases, and 7 incorrectly predicted non-purchases.', 'The results are visualized using a scatter plot showing the estimated salary and age, and how they relate to the predicted purchases, with the training set achieving imperfect but observable separation between the two categories.']}], 'duration': 1441.489, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA16895287.jpg', 'highlights': ['The chapter covers the creation of a Naive Bayes model, achieving 65 correct predictions and 3 incorrect predictions.', 'The chapter illustrates the practical application of accuracy, precision, and recall metrics in evaluating model performance.', 'The chapter introduces iteration tools in Python and demonstrates their application for creating a tuple of all possible outcomes of dice.', 'The probability of obtaining multiples of three when rolling dice results in a .3333 chance.', 'The probability of obtaining multiples of five but not multiples of three when rolling dice results in a .116255 chance.', 'Explanation of the confusion matrix and its significance in evaluating the performance of a classification model.', 'Overview of Naive Bayes Classifier, highlighting its simplicity and independent assumption between features.', 'The data is split into 25% for testing and 75% for training, and then scaled to balance out the impact of different features.']}, {'end': 19635.261, 'segs': [{'end': 18362.311, 'src': 'embed', 'start': 18336.776, 'weight': 0, 'content': [{'end': 18342.521, 'text': "because the next thing we're going to want to do is do the exact same thing, but we're going to visualize the test set results.", 'start': 18336.776, 'duration': 5.745}, {'end': 18348.544, 'text': 'That way we can see what happened with our test group, our 25%.', 'start': 18343.461, 'duration': 5.083}, {'end': 18351.305, 'text': 'And you can see down here we have the test set.', 'start': 18348.544, 'duration': 2.761}, {'end': 18359.79, 'text': "And if you look at the two graphs next to each other, this one obviously has 75% of the data, so it's going to show a lot more.", 'start': 18351.946, 'duration': 7.844}, {'end': 18362.311, 'text': 'This is only 25% of the data.', 'start': 18360.73, 'duration': 1.581}], 'summary': 'Visualize test set results to analyze 25% data.', 'duration': 25.535, 'max_score': 18336.776, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA18336776.jpg'}, {'end': 18437.793, 'src': 'embed', 'start': 18406.755, 'weight': 5, 'content': [{'end': 18410.339, 'text': 'And we have our precision, the recall on getting it right.', 'start': 18406.755, 'duration': 3.584}, {'end': 18414.924, 'text': 'And then we can do our accuracy, the macro average, and the weighted average.', 'start': 18411.159, 'duration': 3.765}, {'end': 18420.648, 'text': 'So you can see that it pulls in Pretty good as far as how accurate it is.', 'start': 18415.745, 'duration': 4.903}, {'end': 18428.15, 'text': "You could say it's going to be about 90% is going to guess correctly that they're not going to purchase.", 'start': 18421.328, 'duration': 6.822}, {'end': 18431.511, 'text': 'And we had an 89% chance that they are going to purchase.', 'start': 18428.63, 'duration': 2.881}, {'end': 18437.793, 'text': "And then the other numbers as you get down have a little bit different meaning, but it's pretty straightforward on here.", 'start': 18432.411, 'duration': 5.382}], 'summary': 'Model accuracy is about 90%, with 89% chance of purchase.', 'duration': 31.038, 'max_score': 18406.755, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA18406755.jpg'}, {'end': 18570.104, 'src': 'embed', 'start': 18544.325, 'weight': 1, 'content': [{'end': 18549.447, 'text': 'When we look at this, we have the larger category, which is artificial intelligence.', 'start': 18544.325, 'duration': 5.122}, {'end': 18553.388, 'text': 'Very generic, comprehensive ideal.', 'start': 18549.687, 'duration': 3.701}, {'end': 18559.269, 'text': 'And in there we have machine learning, and then a subcategory of machine learning is deep learning.', 'start': 18553.588, 'duration': 5.681}, {'end': 18566.461, 'text': 'So when we talk about artificial intelligence, this is the ability of a machine to imitate intelligent human behavior.', 'start': 18559.594, 'duration': 6.867}, {'end': 18570.104, 'text': 'So, when we look at something, can it solve a problem the way humans do?', 'start': 18566.681, 'duration': 3.423}], 'summary': 'Artificial intelligence includes machine learning and deep learning, enabling machines to imitate human behavior and solve problems like humans.', 'duration': 25.779, 'max_score': 18544.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA18544325.jpg'}, {'end': 18697.769, 'src': 'embed', 'start': 18674.236, 'weight': 3, 'content': [{'end': 18682.6, 'text': 'So both of those play a huge part in deciding which would best serve your purposes to predict what your data is going to do and try to predict the outcome.', 'start': 18674.236, 'duration': 8.364}, {'end': 18687.923, 'text': 'So what are neural networks? With deep learning, a machine can be trained to identify various shapes.', 'start': 18682.66, 'duration': 5.263}, {'end': 18690.024, 'text': 'So here we have a square coming in.', 'start': 18688.263, 'duration': 1.761}, {'end': 18694.066, 'text': "You can see we've broken it up into the pixels and we want the label to come out square.", 'start': 18690.064, 'duration': 4.002}, {'end': 18697.769, 'text': "And if we turn the square slightly sideways, it's still a square.", 'start': 18694.406, 'duration': 3.363}], 'summary': 'Neural networks and deep learning are used to predict data outcomes and identify shapes.', 'duration': 23.533, 'max_score': 18674.236, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA18674236.jpg'}, {'end': 18918.657, 'src': 'embed', 'start': 18892.896, 'weight': 2, 'content': [{'end': 18900.402, 'text': 'The least cost value is obtained by making adjustments to the weights and biases iteratively throughout the training process.', 'start': 18892.896, 'duration': 7.506}, {'end': 18903.765, 'text': "And when you think about this, we're not just sending one set of numbers through.", 'start': 18900.683, 'duration': 3.082}, {'end': 18906.107, 'text': "We're sending all kinds of data in here.", 'start': 18903.965, 'duration': 2.142}, {'end': 18913.613, 'text': 'So you might have 100 samples or 1,000 samples, and each one of those samples comes in, and then we look at the cost for that.', 'start': 18906.207, 'duration': 7.406}, {'end': 18918.657, 'text': 'And we want to get that cost, the minimal, the average minimal among all the different samples.', 'start': 18913.773, 'duration': 4.884}], 'summary': 'Optimizing weights and biases iteratively to minimize cost across multiple data samples.', 'duration': 25.761, 'max_score': 18892.896, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA18892896.jpg'}, {'end': 19046.68, 'src': 'embed', 'start': 19016.473, 'weight': 6, 'content': [{'end': 19020.197, 'text': 'And phi is the activation function based on step one.', 'start': 19016.473, 'duration': 3.724}, {'end': 19023.301, 'text': "So it might be if it's close to zero, it's zero.", 'start': 19020.277, 'duration': 3.024}, {'end': 19024.702, 'text': "If it's close to one, it's one.", 'start': 19023.341, 'duration': 1.361}, {'end': 19026.204, 'text': 'And it might be a value in between.', 'start': 19024.743, 'duration': 1.461}, {'end': 19029.288, 'text': "It's very common, depending on what activation function you use.", 'start': 19026.264, 'duration': 3.024}, {'end': 19034.792, 'text': 'The results of the activation function determine which neurons will be activated in the following layer.', 'start': 19029.608, 'duration': 5.184}, {'end': 19040.776, 'text': 'So you can see here we have B1, as we looked at, with X1 and weighted 1, X2 and weighted 2,', 'start': 19034.932, 'duration': 5.844}, {'end': 19046.68, 'text': 'and then you would compute B2 the same way and B3 the same way and B4 and B5 and so on.', 'start': 19040.776, 'duration': 5.904}], 'summary': 'Phi is an activation function with values close to zero, one, or in between, determining which neurons are activated.', 'duration': 30.207, 'max_score': 19016.473, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA19016473.jpg'}], 'start': 18336.776, 'title': 'Visualizing test set results and metrics classification report, deep learning 101, neural networks and deep learning, and tensorflow data analysis', 'summary': 'Covers visualizing test set results and metrics, achieving 90% precision for not purchasing and 89% for purchasing, the importance of deep learning, its applications, and neural networks, including the functions of neurons, the working of neural networks, the concept of weights and biases, activation functions, cost function, backpropagation, and deep learning platforms, as well as the versatility of tensors in tensorflow, including data pre-processing, model building, training, and estimation, with practical implementation using anaconda navigator and jupyterlab or jupyter notebook.', 'chapters': [{'end': 18477.162, 'start': 18336.776, 'title': 'Visualizing test set results and metrics classification report', 'summary': 'Explains how to visualize the test set results, showing the effectiveness of the model using precision, recall, and accuracy metrics, with a 90% precision for not purchasing and 89% for purchasing.', 'duration': 140.386, 'highlights': ["The precision of zeros is 90, with a recall of 0.96, showing a 90% chance of correctly guessing that they're not going to purchase and an 89% chance of correctly guessing that they are going to purchase. The precision of zeros is 90 with a recall of 0.96, indicating a 90% chance of correctly guessing not purchasing and an 89% chance of correctly guessing purchasing.", 'Visualizing the test set results demonstrates the effectiveness of the model, with the 75% data graph showing a lot more than the 25% data graph. Visualizing the test set results demonstrates the effectiveness of the model, with the 75% data graph showing a lot more information than the 25% data graph.', "Explanation of the definitions of accuracy, precision, and recall provide a comprehensive understanding of the metrics used to evaluate the model's performance. The explanation of the definitions of accuracy, precision, and recall provides a comprehensive understanding of the metrics used to evaluate the model's performance."]}, {'end': 18793.498, 'start': 18477.463, 'title': 'Deep learning 101', 'summary': 'Explains the importance of deep learning, its applications, and the concepts of neural networks, machine learning, and artificial intelligence, highlighting that deep learning can work with unstructured data, handle complex operations, and achieve best performance, as well as discussing the functioning of neural networks and the operations within a neuron.', 'duration': 316.035, 'highlights': ['Deep learning helps make predictions about natural events and enables machines to comprehend speech and recognize people and objects in images, with applications in real-time bidding and targeted display advertising. Predictions about natural events, speech comprehension, image recognition, real-time advertising', 'Deep learning is a subfield of machine learning that works with both structured and unstructured data, handles complex operations, and achieves best performance, while machine learning algorithms use labeled sample data and performance decreases as data increases. Works with structured and unstructured data, handles complex operations, achieves best performance, comparison with machine learning', 'Neural networks are modeled on the human brain, with inputs processed by neurons and transferred over weighted channels, and the output is the final value predicted by the artificial neuron. Modeling on human brain, input processing, weighted channels, output prediction', 'Within a neuron, operations include finding the product of each input and the weight of the channel, computing the sum of the weighted products, and adding a unique bias to the sum. Operations within a neuron, weighted products, unique bias']}, {'end': 19351.597, 'start': 18793.598, 'title': 'Neural networks and deep learning', 'summary': 'Explains the working of neural networks, including the concept of weights and biases, the role of activation functions, the importance of cost function in training, and the process of backpropagation. it also covers the training process for identifying shapes and provides an overview of deep learning platforms such as torch, keras, tensorflow, and dl4j.', 'duration': 557.999, 'highlights': ["The cost function is the difference between the neural net's predicted output and the actual output from a set of labeled training data, and the least cost value is obtained by making adjustments to the weights and biases iteratively throughout the training process. The cost function measures the error in prediction by subtracting the actual value from the predicted value, squaring the result, and dividing it by 2. It determines the adjustments needed in the weights and biases to minimize the error, and the backpropagation process is continued until the cost cannot be reduced any further.", 'The concept of weights and biases in neural networks involves multiplying the weight by the value coming in and adding that to the bias, and the final sum is then subjected to the activation function. Weights are multiplied by the input values and summed with the bias before being passed through the activation function, which determines the neuron activation in the subsequent layers. This process helps in processing and passing information through the network.', 'The process of backpropagation involves sending the error back through the network to adjust the weights and biases in order to reduce the prediction error, and it continues iteratively until the cost cannot be further reduced. During backpropagation, the error is sent back through the network to adjust the weights and biases, leading to iterative training to minimize the prediction error. This process ensures that the network is trained with new weights and the cost is minimized without bias towards specific figures.', 'The neural network is trained to identify shapes by processing input data through hidden layers, which improve the accuracy of the output, and the weights are further adjusted to predict shapes with the highest accuracy. The network is trained to recognize shapes by processing input data through hidden layers, where the weights are adjusted to improve the accuracy of predicting shapes. This iterative process ensures that the network can reliably predict the input shapes with high accuracy.', 'An overview of deep learning platforms such as Torch, Keras, TensorFlow, and DL4J is provided, with details about their primary programming languages and key features. The chapter provides an overview of various deep learning platforms, including Torch, Keras, TensorFlow, and DL4J, highlighting their primary programming languages and key features, such as reusability of code, CPU, and GPU processing, and integration with Hadoop and Apache Spark.']}, {'end': 19635.261, 'start': 19352.057, 'title': 'Tensorflow data analysis', 'summary': 'Explores the versatility of tensors in tensorflow, emphasizing the importance of different dimensions in analyzing data structures. it also delves into the architecture of tensorflow, including data pre-processing, model building, training, and estimation, and provides practical implementation guidance using anaconda navigator and jupyterlab or jupyter notebook.', 'duration': 283.204, 'highlights': ['Tensors in TensorFlow are crucial for analyzing data structures with different dimensions, such as processing pictures and movies, and involve computations performed on matrices of n dimension. Emphasizes the importance of different dimensions in analyzing data structures and the role of tensors in processing pictures and movies.', 'TensorFlow architecture involves pre-processing data, building a model, training, and estimating the model, providing a comprehensive framework for data analysis and machine learning implementation. Provides an overview of the TensorFlow architecture, including pre-processing data, model building, and model training and estimation.', 'Practical implementation guidance is offered using Anaconda Navigator and JupyterLab or Jupyter Notebook for setting up and running deep learning projects, importation of tools like TensorFlow and Pandas, and working with data. Provides practical guidance for setting up and running deep learning projects using Anaconda Navigator and JupyterLab or Jupyter Notebook, including tool importation and working with data.']}], 'duration': 1298.485, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA18336776.jpg', 'highlights': ['Visualizing the test set results demonstrates the effectiveness of the model, with the 75% data graph showing a lot more information than the 25% data graph.', 'Deep learning helps make predictions about natural events, speech comprehension, image recognition, real-time advertising.', 'The cost function measures the error in prediction by subtracting the actual value from the predicted value, squaring the result, and dividing it by 2. It determines the adjustments needed in the weights and biases to minimize the error.', 'The process of backpropagation involves sending the error back through the network to adjust the weights and biases in order to reduce the prediction error, and it continues iteratively until the cost cannot be further reduced.', 'Tensors in TensorFlow are crucial for analyzing data structures with different dimensions, such as processing pictures and movies, and involve computations performed on matrices of n dimension.', 'Provides an overview of the TensorFlow architecture, including pre-processing data, model building, and model training and estimation.', 'Practical guidance for setting up and running deep learning projects using Anaconda Navigator and JupyterLab or Jupyter Notebook, including tool importation and working with data.']}, {'end': 20983.787, 'segs': [{'end': 20147.902, 'src': 'embed', 'start': 20117.391, 'weight': 0, 'content': [{'end': 20121.373, 'text': 'And as far as the initial setup, we need to go ahead and create a model.', 'start': 20117.391, 'duration': 3.982}, {'end': 20123.214, 'text': 'So this is where it starts to come together.', 'start': 20121.613, 'duration': 1.601}, {'end': 20127.095, 'text': 'As far as the pre-setup, we have our TF.', 'start': 20123.234, 'duration': 3.861}, {'end': 20128.215, 'text': 'We have an estimator.', 'start': 20127.155, 'duration': 1.06}, {'end': 20131.216, 'text': "We're going to do linear classifier on the estimator.", 'start': 20128.235, 'duration': 2.981}, {'end': 20132.856, 'text': 'In classes equals two.', 'start': 20131.496, 'duration': 1.36}, {'end': 20135.017, 'text': "So we know there's only two classes we're looking at.", 'start': 20133.037, 'duration': 1.98}, {'end': 20137.618, 'text': 'We have ongoing train feature columns.', 'start': 20135.197, 'duration': 2.421}, {'end': 20139.098, 'text': 'and then we have our different.', 'start': 20138.058, 'duration': 1.04}, {'end': 20142.88, 'text': 'we have categorical features plus continuous features.', 'start': 20139.098, 'duration': 3.782}, {'end': 20147.902, 'text': "so this basically creates our model what data is going in, and we'll go ahead and run this,", 'start': 20142.88, 'duration': 5.022}], 'summary': 'Creating a model using tf estimator for a linear classifier with two classes, utilizing categorical and continuous features.', 'duration': 30.511, 'max_score': 20117.391, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA20117391.jpg'}, {'end': 20234.964, 'src': 'embed', 'start': 20177.034, 'weight': 1, 'content': [{'end': 20179.935, 'text': 'In this case, an adult and adult test.', 'start': 20177.034, 'duration': 2.901}, {'end': 20182.576, 'text': 'We have a training data set and a test data set.', 'start': 20179.975, 'duration': 2.601}, {'end': 20184.417, 'text': 'We set the columns up.', 'start': 20183.136, 'duration': 1.281}, {'end': 20190.416, 'text': "We took a very short, brief exploration of the data and its shape as far as what we're working with.", 'start': 20184.793, 'duration': 5.623}, {'end': 20195.658, 'text': 'We changed our label around a little bit so that the label makes a little bit more sense of 01.', 'start': 20190.776, 'duration': 4.882}, {'end': 20199.78, 'text': "Instead of for the machine, it's easier to spit out a 0 or a 1.", 'start': 20195.658, 'duration': 4.122}, {'end': 20200.58, 'text': 'We can look up here.', 'start': 20199.78, 'duration': 0.8}, {'end': 20203.862, 'text': 'We double-check to make sure how many 0s and 1s we have.', 'start': 20200.62, 'duration': 3.242}, {'end': 20208.025, 'text': "double check our data, make sure it's integer 64, nothing weird's going on.", 'start': 20204.122, 'duration': 3.903}, {'end': 20213.449, 'text': "And then we looked at three different ways that we can kind of label the data as far as the way we're going to read it.", 'start': 20208.145, 'duration': 5.304}, {'end': 20216.791, 'text': 'We have our continuous features and our categorical features.', 'start': 20213.969, 'duration': 2.822}, {'end': 20218.832, 'text': "Here's our relationship, which is one of them.", 'start': 20217.071, 'duration': 1.761}, {'end': 20224.336, 'text': 'When we went ahead and created our model, we did not put the relationship in here, which you can do.', 'start': 20218.852, 'duration': 5.484}, {'end': 20232.502, 'text': 'You can actually maybe take it out of categorical and then have its own on here instead of having categorical features and continuous features and so on.', 'start': 20224.396, 'duration': 8.106}, {'end': 20234.964, 'text': "So we've created our setup for our model.", 'start': 20232.902, 'duration': 2.062}], 'summary': 'Preprocessed and labeled data, checked for balance and integrity, and set up model for analysis.', 'duration': 57.93, 'max_score': 20177.034, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA20177034.jpg'}, {'end': 20361.929, 'src': 'embed', 'start': 20335.01, 'weight': 4, 'content': [{'end': 20339.234, 'text': "Again, we're not too worried about the number of epochs for this particular model.", 'start': 20335.01, 'duration': 4.224}, {'end': 20343.998, 'text': "Depending on how much data you have and depending on what you're running, it depends on how many epochs you need to run.", 'start': 20339.354, 'duration': 4.644}, {'end': 20346.76, 'text': "And there's a lot of rules on how many epics you need to run.", 'start': 20344.218, 'duration': 2.542}, {'end': 20354.144, 'text': "One of them is if your training data and your testing data, because you'll check your testing data against your training data.", 'start': 20347.12, 'duration': 7.024}, {'end': 20360.749, 'text': "If your testing data starts having better results than your training data, that means you're no longer fitting towards the data,", 'start': 20354.264, 'duration': 6.485}, {'end': 20361.929, 'text': "but you're fitting to the answer.", 'start': 20360.749, 'duration': 1.18}], 'summary': 'Number of epochs depends on data size and model performance.', 'duration': 26.919, 'max_score': 20335.01, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA20335010.jpg'}, {'end': 20542.324, 'src': 'embed', 'start': 20518.674, 'weight': 2, 'content': [{'end': 20527.038, 'text': 'let me just highlight that ongoing model checkpoint TensorFlow, running local init, evaluate 100 out of 1,000, and so on.', 'start': 20518.674, 'duration': 8.364}, {'end': 20530.339, 'text': 'And once it gets to the end, we get a nice output here.', 'start': 20527.258, 'duration': 3.081}, {'end': 20536.481, 'text': 'We have an accuracy in this case of 0.79 with a baseline of 0.76.', 'start': 20530.499, 'duration': 5.982}, {'end': 20537.962, 'text': "So now we've created a model.", 'start': 20536.481, 'duration': 1.481}, {'end': 20540.363, 'text': "Let's go ahead and tweak it a little bit.", 'start': 20538.502, 'duration': 1.861}, {'end': 20542.324, 'text': 'So we have our accuracy up here.', 'start': 20540.603, 'duration': 1.721}], 'summary': 'Model evaluation shows 0.79 accuracy, improved from baseline of 0.76.', 'duration': 23.65, 'max_score': 20518.674, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA20518674.jpg'}, {'end': 20879.797, 'src': 'embed', 'start': 20850.185, 'weight': 8, 'content': [{'end': 20853.807, 'text': "we still see uh, here's our accuracy, baseline 0.763.", 'start': 20850.185, 'duration': 3.622}, {'end': 20857.009, 'text': "that didn't really change and you know, the accuracy didn't really go up that much.", 'start': 20853.807, 'duration': 3.202}, {'end': 20858.51, 'text': 'i could hit the run button a few times.', 'start': 20857.009, 'duration': 1.501}, {'end': 20860.471, 'text': 'i would probably upbeat the one above.', 'start': 20858.51, 'duration': 1.961}, {'end': 20862.452, 'text': "It's not a big change, but that's all right.", 'start': 20860.771, 'duration': 1.681}, {'end': 20863.052, 'text': 'This is how we learn.', 'start': 20862.472, 'duration': 0.58}, {'end': 20867.173, 'text': "This is how we go through and figure out what's going to work with our data and what's not.", 'start': 20863.072, 'duration': 4.101}, {'end': 20871.815, 'text': "What's going to improve the quality of our data so we have a better prediction and what's not.", 'start': 20867.433, 'duration': 4.382}, {'end': 20874.996, 'text': 'And we can now go ahead and utilize this model.', 'start': 20872.055, 'duration': 2.941}, {'end': 20879.797, 'text': "This is where it gets exciting when you're actually working with somebody or with your clients and you come in and you say okay,", 'start': 20875.036, 'duration': 4.761}], 'summary': 'Accuracy baseline remains at 0.763 with minimal improvement after iterations, highlighting the iterative nature of learning and data quality improvement.', 'duration': 29.612, 'max_score': 20850.185, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA20850185.jpg'}, {'end': 20970.23, 'src': 'embed', 'start': 20943.305, 'weight': 3, 'content': [{'end': 20946.406, 'text': "So you can see there's all kinds of additional information you can pull from this.", 'start': 20943.305, 'duration': 3.101}, {'end': 20949.726, 'text': 'And likewise, we could do it for position 3.', 'start': 20946.906, 'duration': 2.82}, {'end': 20951.407, 'text': 'Let me go ahead and run that.', 'start': 20949.726, 'duration': 1.681}, {'end': 20956.328, 'text': "And what's kind of nice about this is you can now see here's label 1, and here's our logistics output.", 'start': 20951.767, 'duration': 4.561}, {'end': 20959.388, 'text': 'And again, we have to kind of hunt for it a little bit in this particular setup.', 'start': 20956.448, 'duration': 2.94}, {'end': 20961.468, 'text': "But here's the output array, and there's our 1.", 'start': 20959.408, 'duration': 2.06}, {'end': 20963.629, 'text': 'And they match, label 1, 1.', 'start': 20961.468, 'duration': 2.161}, {'end': 20968.89, 'text': "So we predicted for the very first one, location 0, that it's going to be a 0 on the label.", 'start': 20963.629, 'duration': 5.261}, {'end': 20970.23, 'text': "It's going to make under $50,000.", 'start': 20968.91, 'duration': 1.32}], 'summary': 'The model predicted that the first location would make under $50,000, matching label 1, 1.', 'duration': 26.925, 'max_score': 20943.305, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA20943305.jpg'}], 'start': 19635.261, 'title': 'Data preparation, preprocessing, model training, and model improvement', 'summary': 'Covers loading and preparing data with pandas, handling 32,561 and 16,281 row datasets, data preprocessing and feature engineering for machine learning with tensorflow, creating and training a model with 1,000 steps and achieving 0.79 accuracy, squaring the age variable for data analysis, and continually tweaking a model to reach a baseline accuracy of 0.76 and predict income levels.', 'chapters': [{'end': 19782.284, 'start': 19635.261, 'title': 'Data loading and data preparation', 'summary': 'Discusses loading and preparing data using pandas, including skipping initial spaces, setting index columns, and converting labels into numerical values, with the dataset containing 32,561 rows and 15 columns for the first set, and 16,281 rows and 15 columns for the second set.', 'duration': 147.023, 'highlights': ['The dataset contains 32,561 rows and 15 columns for the first set, and 16,281 rows and 15 columns for the second set. The dataset consists of 32,561 rows and 15 columns for the first set, and 16,281 rows and 15 columns for the second set, providing a clear understanding of the dataset size.', 'Converting labels into numerical values by setting the label equal to 0 for less than or equal to 50K and 1 for greater than or equal to 50K. The process of converting labels into numerical values is explained, where the label is set to 0 for less than or equal to 50K and 1 for greater than or equal to 50K, facilitating the classification process.', 'Loading and preparing data using pandas, including skipping initial spaces, setting index columns, and using pandas D types to check the data types of the columns. The chapter covers the process of loading and preparing data using pandas, which includes skipping initial spaces, setting index columns, and utilizing pandas D types to check the data types of the columns, ensuring proper data handling and organization.']}, {'end': 20117.031, 'start': 19782.304, 'title': 'Data preprocessing and feature engineering', 'summary': 'Discusses data preprocessing and feature engineering, including identifying and handling different data sets, converting data types, creating categorical and continuous features, and setting up feature columns for tensorflow, with a focus on ensuring the data is ready for machine learning with tensorflow.', 'duration': 334.727, 'highlights': ['The chapter discusses data preprocessing and feature engineering, including identifying and handling different data sets, converting data types, creating categorical and continuous features, and setting up feature columns for TensorFlow. The transcript covers various aspects of data preprocessing and feature engineering, encompassing handling different data sets, converting data types, creating categorical and continuous features, and setting up feature columns for TensorFlow.', 'The chapter emphasizes the importance of ensuring the data is ready for machine learning with TensorFlow. The emphasis is placed on preparing the data for machine learning with TensorFlow, highlighting the significance of data readiness for the machine learning process.', 'The transcript includes specific techniques such as identifying and handling different data sets, converting data types, and creating categorical and continuous features. Specific techniques discussed in the transcript include identifying and handling different data sets, converting data types, and creating categorical and continuous features to prepare the data for further processing.']}, {'end': 20542.324, 'start': 20117.391, 'title': 'Creating and training a model', 'summary': 'Details the process of creating and training a model using tensorflow, including setting up the model with features and labels, defining the input function, training the model with 1,000 steps and evaluating its accuracy at 0.79.', 'duration': 424.933, 'highlights': ['The model is trained with 1,000 steps The model is trained using 1,000 steps to optimize its performance and accuracy.', 'The model achieves an accuracy of 0.79 The trained model achieves an accuracy of 0.79, indicating its effectiveness in making predictions.', 'The input function is defined to read the data into the model An input function is defined to read the features and labels into the model for training and evaluation.', 'Features and labels are set up for the model Features and labels are set up to provide the necessary input data for training and evaluating the model.']}, {'end': 20795.88, 'start': 20542.344, 'title': 'Age squaring for data analysis', 'summary': 'Discusses the process of squaring the age variable in data analysis, demonstrating how it can yield different results and the subsequent steps involved in building and evaluating a tensorflow model using the modified data.', 'duration': 253.536, 'highlights': ['The chapter explains the rationale for squaring the age variable in data analysis, highlighting the observation that age increases in youth and decreases near retirement, leading to the decision to square the age value (relevance score: 5)', 'The process of creating a new function to handle the square variable is detailed, emphasizing the potential for reusability with multiple features exhibiting similar qualities (relevance score: 4)', "The steps involved in modifying the data and creating a new TensorFlow model, including evaluating the model's performance, are outlined, providing insights into the practical application of the squared age variable in data analysis (relevance score: 3)"]}, {'end': 20983.787, 'start': 20796.08, 'title': 'Model tweaking for improved accuracy', 'summary': 'Discusses the process of continually tweaking a model to improve accuracy, reaching a baseline accuracy of 0.76 by partitioning data and iterating through predictions, with a demonstration of predicting income levels from the model.', 'duration': 187.707, 'highlights': ['The chapter discusses the process of continually tweaking a model to improve accuracy, reaching a baseline accuracy of 0.76 by partitioning data and iterating through predictions.', "The demonstration involves predicting income levels from the model, with a detailed example of predicting an individual's income level based on various attributes.", 'The process involves running the prediction multiple times to enhance the quality of data and improve predictions for better data science outcomes.']}], 'duration': 1348.526, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA19635261.jpg', 'highlights': ['The dataset contains 32,561 rows and 15 columns for the first set, and 16,281 rows and 15 columns for the second set, providing a clear understanding of the dataset size.', 'Converting labels into numerical values by setting the label equal to 0 for less than or equal to 50K and 1 for greater than or equal to 50K, facilitating the classification process.', 'Loading and preparing data using pandas, including skipping initial spaces, setting index columns, and using pandas D types to check the data types of the columns, ensuring proper data handling and organization.', 'The chapter discusses data preprocessing and feature engineering, including identifying and handling different data sets, converting data types, creating categorical and continuous features, and setting up feature columns for TensorFlow.', 'The model achieves an accuracy of 0.79, indicating its effectiveness in making predictions.', 'The input function is defined to read the features and labels into the model for training and evaluation.', 'The chapter explains the rationale for squaring the age variable in data analysis, highlighting the observation that age increases in youth and decreases near retirement, leading to the decision to square the age value.', 'The process of creating a new function to handle the square variable is detailed, emphasizing the potential for reusability with multiple features exhibiting similar qualities.', 'The chapter discusses the process of continually tweaking a model to improve accuracy, reaching a baseline accuracy of 0.76 by partitioning data and iterating through predictions.']}, {'end': 22176.063, 'segs': [{'end': 21206.392, 'src': 'embed', 'start': 21178.428, 'weight': 1, 'content': [{'end': 21184.854, 'text': 'TensorFlow applications, how TensorFlow works, TensorFlow 1.0 versus 2.0,', 'start': 21178.428, 'duration': 6.426}, {'end': 21192.3, 'text': "TensorFlow 2.0 architecture and then we'll go over a TensorFlow demo where we roll up our sleeves and dive right into the code.", 'start': 21184.854, 'duration': 7.446}, {'end': 21196.464, 'text': "So let's start with deep learning frameworks.", 'start': 21193.101, 'duration': 3.363}, {'end': 21203.529, 'text': "To start with, this chart doesn't even do the filled justice because it's just exploded.", 'start': 21197.064, 'duration': 6.465}, {'end': 21206.392, 'text': 'These are just some of the major frameworks out there.', 'start': 21204.17, 'duration': 2.222}], 'summary': 'Overview of tensorflow applications, architecture, and a demo comparing tensorflow 1.0 and 2.0.', 'duration': 27.964, 'max_score': 21178.428, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA21178428.jpg'}, {'end': 21435.363, 'src': 'embed', 'start': 21409.968, 'weight': 3, 'content': [{'end': 21416.991, 'text': 'When you think of neural networks, because TensorFlow is a neural network, think of complicated chaotic data.', 'start': 21409.968, 'duration': 7.023}, {'end': 21422.674, 'text': "This is very different than if you have set numbers like you're looking at the stock market.", 'start': 21417.311, 'duration': 5.363}, {'end': 21430.439, 'text': "You can use this on the stock market, but if you're doing something where the numbers are very clear and not so chaotic as you have in a picture,", 'start': 21423.255, 'duration': 7.184}, {'end': 21435.363, 'text': "then you're talking more about linear regression models and different regression models when you're looking at that.", 'start': 21430.439, 'duration': 4.924}], 'summary': 'Neural networks like tensorflow handle chaotic data, unlike linear regression models for clear, non-chaotic data.', 'duration': 25.395, 'max_score': 21409.968, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA21409968.jpg'}, {'end': 21653.52, 'src': 'embed', 'start': 21612.037, 'weight': 0, 'content': [{'end': 21618.08, 'text': 'But, TensorFlow does such a nice job that you can spin different setups up very easily and test them out.', 'start': 21612.037, 'duration': 6.043}, {'end': 21620.401, 'text': 'So you can test out these different models to see how they work.', 'start': 21618.14, 'duration': 2.261}, {'end': 21623.202, 'text': 'Now, TensorFlow has gone through two major stages.', 'start': 21620.501, 'duration': 2.701}, {'end': 21629.865, 'text': 'We had the original TensorFlow release of 1.0 and then they came out with the 2.0 version.', 'start': 21624.063, 'duration': 5.802}, {'end': 21634.848, 'text': 'And the 2.0 addressed so many things out there that the 1.0 really needed.', 'start': 21630.166, 'duration': 4.682}, {'end': 21639.591, 'text': 'So, when we start talking about TensorFlow 1.0 versus 2.0,.', 'start': 21635.128, 'duration': 4.463}, {'end': 21646.115, 'text': "I guess you would need to know this for a legacy programming job if you're pulling apart somebody else's code.", 'start': 21639.591, 'duration': 6.524}, {'end': 21651.038, 'text': 'The first thing is that TensorFlow 2.0 supports eager execution by default.', 'start': 21646.335, 'duration': 4.703}, {'end': 21653.52, 'text': 'It allows you to build your models and run them instantly.', 'start': 21651.158, 'duration': 2.362}], 'summary': 'Tensorflow 2.0 improved on 1.0, offering eager execution and easy model testing.', 'duration': 41.483, 'max_score': 21612.037, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA21612037.jpg'}, {'end': 21719.895, 'src': 'embed', 'start': 21694.102, 'weight': 4, 'content': [{'end': 21699.644, 'text': "So if you see the first part and you're like, what the heck is all this session thing going on? That's TensorFlow 1.0.", 'start': 21694.102, 'duration': 5.542}, {'end': 21702.665, 'text': "And then when you get into 2.0, it's just nice and clean.", 'start': 21699.644, 'duration': 3.021}, {'end': 21713.191, 'text': 'If you remember from the beginning, I said Keras on our list up there, and Keras is the high-level API in TensorFlow 2.0.', 'start': 21703.445, 'duration': 9.746}, {'end': 21717.374, 'text': 'Keras is the official high-level API of TensorFlow 2.0.', 'start': 21713.191, 'duration': 4.183}, {'end': 21719.895, 'text': 'It has incorporated Keras as tf.keras.', 'start': 21717.374, 'duration': 2.521}], 'summary': 'Tensorflow 2.0 uses keras as its official high-level api.', 'duration': 25.793, 'max_score': 21694.102, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA21694102.jpg'}], 'start': 20984.227, 'title': 'Implementing model with python and r, introduction to tensorflow 2.0, and understanding tensorflow 2.0', 'summary': 'Covers implementing models with python and r, introducing tensorflow 2.0 with its features and applications, and understanding tensorflow 2.0 including computational capabilities, key changes, hierarchy, and architecture.', 'chapters': [{'end': 21118.742, 'start': 20984.227, 'title': 'Implementing model with python and r', 'summary': 'Explains the process of defining features, using python or r for relationship correlation, and creating an input function for training models, emphasizing the importance of avoiding bias and ensuring generic answers for a large amount of data.', 'duration': 134.515, 'highlights': ['The chapter emphasizes the importance of defining features and utilizing Python or R for relationship correlation to determine connected features and their relationships.', 'An input function is crucial in training models to avoid bias, with the goal of maintaining generic answers for a large amount of data.', 'The process of creating an input function involves considerations such as the number of iterations through the data, preventing bias, and shuffling the batch size.']}, {'end': 21518.418, 'start': 21118.802, 'title': 'Introduction to tensorflow 2.0', 'summary': 'Introduces tensorflow 2.0, covering its features, applications, and fundamental concepts, such as tensors and neural networks, highlighting its scalability, support for multi-dimensional arrays, and diverse applications.', 'duration': 399.616, 'highlights': ['TensorFlow 2.0 is a popular open source library released in 2015 by Google Brain Team for building machine learning and deep learning models. TensorFlow 2.0 introduction and release information.', 'It provides scalability of computation across machines and large data sets. Scalability of TensorFlow 2.0 across machines and data sets.', 'It supports fast debugging and model building, allowing for the creation of models with different layers and properties. Capability of fast debugging and flexible model building with various layers and properties.', 'TensorFlow 2.0 has a large community and provides TensorBoard to visualize the model, enhancing collaboration and communication of models to stakeholders. Community support and the use of TensorBoard for visualizing models.', 'TensorFlow 2.0 is used for various applications, including data analytics, face detection, language translation, fraud detection, and video detection. Applications of TensorFlow 2.0 in data analytics and various detection tasks.']}, {'end': 22176.063, 'start': 21518.418, 'title': 'Understanding tensorflow 2.0', 'summary': 'Explains the basics of tensorflow, detailing its computational capabilities with data flow graphs, the key changes from tensorflow 1.0 to 2.0, and the hierarchy and architecture of tensorflow 2.0, as well as its tools and platforms.', 'duration': 657.645, 'highlights': ['TensorFlow 2.0 supports eager execution by default, reducing the code required for model building and running. The transition from TensorFlow 1 to TensorFlow 2.0 results in almost double the code to perform the same tasks, with TensorFlow 2.0 supporting eager execution by default.', 'Keras is the official high-level API of TensorFlow 2.0, providing model-building APIs like sequential, functional, and subclassing. Keras in TensorFlow 2.0 incorporates model-building APIs such as sequential, functional, and subclassing, offering different levels of abstraction for project needs.', 'In TensorFlow 2.0, TF layers are automatically defined when added under the sequential model, simplifying the process of defining layers. TensorFlow 2.0 simplifies the process of defining TF layers, as they are automatically defined when added under the sequential model, eliminating the need for the TF variable block.', 'The Autograph feature of tf function in TensorFlow 2.0 allows writing graph code using natural Python syntax, enhancing the flexibility and reusability of Python functions. In TensorFlow 2.0, the Autograph feature of tf function enables the writing of graph code in natural Python syntax, improving the flexibility and reusability of Python functions.']}], 'duration': 1191.836, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA20984227.jpg', 'highlights': ['TensorFlow 2.0 is a popular open source library for building machine learning and deep learning models, with scalability across machines and large data sets.', 'The chapter emphasizes the importance of defining features and utilizing Python or R for relationship correlation to determine connected features and their relationships.', 'TensorFlow 2.0 supports eager execution by default, reducing the code required for model building and running.', 'An input function is crucial in training models to avoid bias, with the goal of maintaining generic answers for a large amount of data.', 'Keras is the official high-level API of TensorFlow 2.0, providing model-building APIs like sequential, functional, and subclassing.']}, {'end': 23694.505, 'segs': [{'end': 22199.542, 'src': 'embed', 'start': 22176.143, 'weight': 0, 'content': [{'end': 22184.208, 'text': 'You want to be able to run this at a high speed so that when you have hundreds of people sending their transactions in, it says hey,', 'start': 22176.143, 'duration': 8.065}, {'end': 22187.771, 'text': "this doesn't look right, someone's scamming this person and probably has their credit card.", 'start': 22184.208, 'duration': 3.563}, {'end': 22192.015, 'text': "so when we're talking about all those fun things, we're talking about saved model.", 'start': 22187.771, 'duration': 4.244}, {'end': 22197.26, 'text': 'this is, we were talking about that earlier, where it used to be, when you did one of these models.', 'start': 22192.015, 'duration': 5.245}, {'end': 22199.542, 'text': "it wouldn't truncate the float numbers the same.", 'start': 22197.26, 'duration': 2.282}], 'summary': 'The goal is to run transactions at high speed, detecting scams and credit card fraud, addressing issues with float number truncation in models.', 'duration': 23.399, 'max_score': 22176.143, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA22176143.jpg'}, {'end': 22278.527, 'src': 'embed', 'start': 22251.641, 'weight': 1, 'content': [{'end': 22258.604, 'text': 'So you can now create your TensorFlow backend, save it and have it accessed from C, Java Go, C, Sharp,', 'start': 22251.641, 'duration': 6.963}, {'end': 22260.925, 'text': "Rust R or from whatever package you're working on.", 'start': 22258.604, 'duration': 2.321}, {'end': 22268.713, 'text': "So we kind of have an overview of the architecture and what's going on behind the scenes, and in this case what's going on as far as distributing it.", 'start': 22261.445, 'duration': 7.268}, {'end': 22273.699, 'text': "Let's go ahead and take a look at three specific pieces of TensorFlow.", 'start': 22269.134, 'duration': 4.565}, {'end': 22278.527, 'text': 'And those are going to be constants, variables, and sessions.', 'start': 22274.665, 'duration': 3.862}], 'summary': 'Tensorflow backend can be accessed from various languages like c, java, go, c#, rust, and r, and consists of constants, variables, and sessions.', 'duration': 26.886, 'max_score': 22251.641, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA22251641.jpg'}, {'end': 22503.175, 'src': 'embed', 'start': 22475.759, 'weight': 2, 'content': [{'end': 22478.88, 'text': "It might be that they've already updated it and I don't know it and I have an older version.", 'start': 22475.759, 'duration': 3.121}, {'end': 22483.403, 'text': "But you want to make sure you're in a Python version 3.6 in your environment.", 'start': 22479.92, 'duration': 3.483}, {'end': 22486.365, 'text': 'And of course in Anaconda I can easily set that environment up.', 'start': 22483.663, 'duration': 2.702}, {'end': 22497.014, 'text': "Make sure you go ahead and pip in your TensorFlow or if you're in Anaconda you can do Anaconda install TensorFlow to make sure it's in your package.", 'start': 22486.886, 'duration': 10.128}, {'end': 22499.915, 'text': "So let's just go ahead and dive in and bring that up.", 'start': 22497.774, 'duration': 2.141}, {'end': 22503.175, 'text': 'This will open up a nice browser window.', 'start': 22501.095, 'duration': 2.08}], 'summary': 'Ensure python 3.6 in anaconda environment, install tensorflow using pip or anaconda. open browser window.', 'duration': 27.416, 'max_score': 22475.759, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA22475759.jpg'}, {'end': 22976.676, 'src': 'embed', 'start': 22937.95, 'weight': 3, 'content': [{'end': 22939.932, 'text': 'We want to be able to run this on any computer.', 'start': 22937.95, 'duration': 1.982}, {'end': 22943.393, 'text': "and so we need to control whether it's a tf float 16.", 'start': 22940.592, 'duration': 2.801}, {'end': 22947.614, 'text': 'in this case we did an integer 32.', 'start': 22943.393, 'duration': 4.221}, {'end': 22949.775, 'text': 'we could also do this as a float.', 'start': 22947.614, 'duration': 2.161}, {'end': 22954.316, 'text': 'so if i run this as a float 32, that means this has a 32-bit precision.', 'start': 22949.775, 'duration': 4.541}, {'end': 22955.756, 'text': "you'll see zero point whatever.", 'start': 22954.316, 'duration': 1.44}, {'end': 22964.428, 'text': "and then, to go with zeros, We have ones if we're going from the opposite side, and so we can easily just create a tensor flow with ones.", 'start': 22955.756, 'duration': 8.672}, {'end': 22970.952, 'text': 'You might ask yourself, why would I want zeros and ones? And your first thought might be to initiate a new tensor.', 'start': 22965.588, 'duration': 5.364}, {'end': 22976.676, 'text': 'Usually we initiate a lot of this stuff with random numbers because it does a better job solving it.', 'start': 22971.832, 'duration': 4.844}], 'summary': 'Configuring tensor flow precision to 32-bit; initializing tensors with zeros and ones for improved problem-solving.', 'duration': 38.726, 'max_score': 22937.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA22937950.jpg'}, {'end': 23125.481, 'src': 'embed', 'start': 23096.905, 'weight': 5, 'content': [{'end': 23100.827, 'text': 'One of the things that comes up, of course, is recasting your data.', 'start': 23096.905, 'duration': 3.922}, {'end': 23108.911, 'text': "So if we have a dtype float32, we might want to convert these to integers because of the project we're working on.", 'start': 23102.247, 'duration': 6.664}, {'end': 23118.316, 'text': "I know one of the projects I've worked on ended up wanting to do a lot of roundoffs so that it would take a dollar amount or a float value and then have to run it off to a dollar amount.", 'start': 23109.871, 'duration': 8.445}, {'end': 23119.797, 'text': 'So we only wanted two decimal points.', 'start': 23118.356, 'duration': 1.441}, {'end': 23122.319, 'text': 'And in which case you have a lot of different options.', 'start': 23120.357, 'duration': 1.962}, {'end': 23125.481, 'text': 'you can multiply by 100 and then round it off, or whatever you want to do.', 'start': 23122.319, 'duration': 3.162}], 'summary': 'Consider converting float32 data to integers for rounding off to two decimal points.', 'duration': 28.576, 'max_score': 23096.905, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA23096905.jpg'}], 'start': 22176.143, 'title': 'Tensorflow essentials', 'summary': 'Explores tensorflow distribution, serving cloud capabilities, tensorflow lite for mobile and raspberry pi deployment, language bindings, covers constants, variables, and sessions, jupyter notebook setup, tensor creation and manipulation, zeros and ones usage, and data type manipulation in tensorflow.', 'chapters': [{'end': 22273.699, 'start': 22176.143, 'title': 'Tensorflow distribution', 'summary': 'Discusses the distribution capabilities of tensorflow, including tensorflow serving cloud for high-speed processing, tensorflow lite for mobile and raspberry pi deployment, and various language bindings for backend access, enabling efficient distribution to a wide range of endpoints.', 'duration': 97.556, 'highlights': ['The chapter highlights the high-speed processing capabilities of TensorFlow serving cloud for handling hundreds of transactions, identifying potential fraud, and supporting credit card security.', 'It discusses the deployment of TensorFlow Lite on mobile devices like Android, iOS, and Raspberry Pi, enabling affordable beta testing of new products and introducing the new version with a built-in TPU and camera for video pre-processing.', 'The chapter also covers various language bindings, such as C, Java, Go, C Sharp, Rust, and R, for backend access, providing an overview of the architecture and efficient distribution capabilities of TensorFlow.']}, {'end': 22428.03, 'start': 22274.665, 'title': 'Tensorflow constants, variables, and sessions', 'summary': 'Covers the basic concepts of tensorflow, including constants, variables, and sessions. it explains the syntax and functionality of each, and demonstrates their usage through hands-on examples, emphasizing the importance of these fundamental components for tensorflow setup and usage.', 'duration': 153.365, 'highlights': ['Constants in TensorFlow are created using the function constant and remain static throughout the computations. Constants are created using the function constant, and the example of creating a constant value of 5.2 with dtype as float is provided.', 'Variables in TensorFlow are in-memory buffers that store tensors, providing full control to change the values. Variables are explained as in-memory buffers that store tensors, and an example of creating a 2 by 3 array filled with ones using tf.variables and tf.ones is given.', 'Sessions in TensorFlow are used to run a computational graph to evaluate the nodes, enabling the execution of the computations. The concept of sessions is introduced, explaining their role in running computational graphs to evaluate the nodes, with a demonstration of using a session to perform computations and printing the result.']}, {'end': 22955.756, 'start': 22428.27, 'title': 'Tensorflow basics in jupyter notebook', 'summary': 'Covers setting up anaconda, using jupyter notebook for tensorflow in python version 3.6, creating and manipulating tensors, understanding data types and precision control, and utilizing numpy arrays.', 'duration': 527.486, 'highlights': ['Setting up Anaconda and using Jupyter Notebook for TensorFlow in Python version 3.6 The chapter emphasizes the importance of using Python version 3.6 for TensorFlow, setting up the environment in Anaconda, and utilizing Jupyter Notebook for coding.', 'Creating and manipulating tensors in Jupyter Notebook The process of creating constants and variables as tensors, checking their shapes, and understanding the difference between constants and variables is explained.', 'Understanding data types and precision control in TensorFlow The chapter delves into controlling precision by specifying the data types such as float 16, float 32, and integer 32 to ensure compatibility across different platforms.', 'Utilizing numpy arrays in TensorFlow The utilization of numpy arrays in TensorFlow for treating its output and the significance of understanding and visualizing the output format are highlighted.']}, {'end': 23095.764, 'start': 22955.756, 'title': 'Tensorflow zeros and ones', 'summary': 'Explores the use of zeros and ones in tensorflow for tasks such as masking, reshaping, and initializing tensors, while emphasizing the importance of avoiding bias by using random numbers for better problem-solving.', 'duration': 140.008, 'highlights': ['Using random numbers for tensor initialization does a better job solving problems than starting with a uniform set of ones or zeros.', 'Starting a neural network with ones and zeros can introduce bias and should be done with caution.', 'Zeros and ones are often used for masking and initializing tensors with different numbers as the tensor learns for control and manipulation.', 'Reshaping tensors in TensorFlow can be achieved similar to NumPy, allowing for flexibility in array manipulation.', 'The use of tf.random uniform allows for the filling of tensors with random numbers, providing diversity in tensor values and illustrating the reshaping effects.']}, {'end': 23694.505, 'start': 23096.905, 'title': 'Tensorflow data manipulation', 'summary': 'Covers the process of recasting data types, converting float values to integers, performing tensor manipulations such as casting to integer and transposing, and conducting matrix multiplication and bitwise multiplication in tensorflow, along with a brief introduction to setting up the tensorflow environment and importing necessary libraries.', 'duration': 597.6, 'highlights': ['The chapter covers the process of recasting data types, converting float values to integers, performing tensor manipulations such as casting to integer and transposing, and conducting matrix multiplication and bitwise multiplication in TensorFlow. This is the main focus of the chapter, providing an overview of the key topics covered in the transcript.', 'Performing matrix multiplication of tensors resulting in the output 36 and 30, which involves multiplying a constant A (5839) with a vector V (4, 2). This demonstrates the practical application of matrix multiplication in TensorFlow with specific quantifiable results.', 'Introduction to setting up the TensorFlow environment and importing necessary libraries including NumPy, Pandas, Seaborn, Matplotlib, and SciPy, with specific configuration settings for graphics and warnings. Provides an insight into the initial setup required for working with TensorFlow, including the necessary libraries and configurations.']}], 'duration': 1518.362, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA22176143.jpg', 'highlights': ['The chapter highlights the high-speed processing capabilities of TensorFlow serving cloud for handling hundreds of transactions, identifying potential fraud, and supporting credit card security.', 'The chapter also covers various language bindings, such as C, Java, Go, C Sharp, Rust, and R, for backend access, providing an overview of the architecture and efficient distribution capabilities of TensorFlow.', 'The chapter emphasizes the importance of using Python version 3.6 for TensorFlow, setting up the environment in Anaconda, and utilizing Jupyter Notebook for coding.', 'The chapter delves into controlling precision by specifying the data types such as float 16, float 32, and integer 32 to ensure compatibility across different platforms.', 'Using random numbers for tensor initialization does a better job solving problems than starting with a uniform set of ones or zeros.', 'The chapter covers the process of recasting data types, converting float values to integers, performing tensor manipulations such as casting to integer and transposing, and conducting matrix multiplication and bitwise multiplication in TensorFlow. This is the main focus of the chapter, providing an overview of the key topics covered in the transcript.']}, {'end': 25663.332, 'segs': [{'end': 23743.131, 'src': 'embed', 'start': 23713.531, 'weight': 0, 'content': [{'end': 23720.293, 'text': "So when we're importing Keras and the sequential model, we are in effect importing TensorFlow underneath of it.", 'start': 23713.531, 'duration': 6.762}, {'end': 23723.374, 'text': 'We just brought in the math, probably should have put that up above.', 'start': 23720.993, 'duration': 2.381}, {'end': 23727.417, 'text': "And then we have our Keras models we're going to import sequential.", 'start': 23724.294, 'duration': 3.123}, {'end': 23733.022, 'text': 'Now if you remember from our slide there was three different options.', 'start': 23728.018, 'duration': 5.004}, {'end': 23736.645, 'text': 'Let me just flip back over there so we can have a quick recall on that.', 'start': 23733.182, 'duration': 3.463}, {'end': 23743.131, 'text': 'And so in Keras we have sequential, functional, and subclassing.', 'start': 23737.005, 'duration': 6.126}], 'summary': 'Importing keras and tensorflow in sequential model with three options: sequential, functional, and subclassing.', 'duration': 29.6, 'max_score': 23713.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA23713531.jpg'}, {'end': 23855.429, 'src': 'embed', 'start': 23826.092, 'weight': 4, 'content': [{'end': 23829.514, 'text': 'The dense layer is your standard neural network.', 'start': 23826.092, 'duration': 3.422}, {'end': 23833.857, 'text': 'By default, it uses ReLU for its activation.', 'start': 23830.275, 'duration': 3.582}, {'end': 23839.281, 'text': 'And then the LSTM is a long, short-term memory layer.', 'start': 23835.038, 'duration': 4.243}, {'end': 23845.125, 'text': "Since we're going to be looking probably at sequential data, we want to go ahead and do the LSTM.", 'start': 23839.541, 'duration': 5.584}, {'end': 23855.429, 'text': 'And if we go into Keras and we look at their layers this is a Keras website you can see as we scroll down for the Keras layers that are built in,', 'start': 23845.625, 'duration': 9.804}], 'summary': 'Neural network uses relu, lstm for sequential data in keras.', 'duration': 29.337, 'max_score': 23826.092, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA23826092.jpg'}, {'end': 24010.241, 'src': 'embed', 'start': 23981.058, 'weight': 1, 'content': [{'end': 23986.122, 'text': "Here is the GEM, which I'm going to guess is the timestamp on there, so we have a date and time.", 'start': 23981.058, 'duration': 5.064}, {'end': 23996.931, 'text': 'We have our 03, CO, NO2 reading, SO2, NO, CO2, VOC, and then some other numbers here, PM1, PM2.5, PM4, PM10.', 'start': 23986.983, 'duration': 9.948}, {'end': 24003.796, 'text': '10 without actually looking through the data.', 'start': 24000.213, 'duration': 3.583}, {'end': 24006.278, 'text': 'I mean some of this I can guess is like temperature humidity.', 'start': 24003.796, 'duration': 2.482}, {'end': 24010.241, 'text': "I'm not sure what the pms are, but we have a whole slew of data here.", 'start': 24006.278, 'duration': 3.963}], 'summary': 'Gem provides various air quality readings, including co, no2, so2, no, co2, voc, pm1, pm2.5, pm4, and pm10.', 'duration': 29.183, 'max_score': 23981.058, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA23981058.jpg'}, {'end': 24315.925, 'src': 'embed', 'start': 24280.849, 'weight': 2, 'content': [{'end': 24286.111, 'text': 'We have now reorganized this so we put in date time 03 CO.', 'start': 24280.849, 'duration': 5.262}, {'end': 24290.213, 'text': 'So now this is in the same order as it was before.', 'start': 24286.792, 'duration': 3.421}, {'end': 24297.716, 'text': "And you'll see the date time now has our 00, same date 123 and so on.", 'start': 24290.233, 'duration': 7.483}, {'end': 24302.658, 'text': "So it's grouped the data together so it's a lot more manageable and in the format we want.", 'start': 24297.756, 'duration': 4.902}, {'end': 24304.579, 'text': 'and in the right sequential order.', 'start': 24303.258, 'duration': 1.321}, {'end': 24315.925, 'text': "And if we go back to, there we go, our error quality, you can see right here we're looking at these columns going across.", 'start': 24305.579, 'duration': 10.346}], 'summary': 'Data reorganized by date time 03 co, grouped and sequenced for improved manageability and in the right order.', 'duration': 35.076, 'max_score': 24280.849, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA24280849.jpg'}, {'end': 24610.334, 'src': 'embed', 'start': 24578.102, 'weight': 3, 'content': [{'end': 24581.262, 'text': "If it's less than the minimum IQR, it's an outlier.", 'start': 24578.102, 'duration': 3.16}, {'end': 24586.864, 'text': 'And if the max value is greater than the max IQR, we have an outlier.', 'start': 24581.342, 'duration': 5.522}, {'end': 24588.204, 'text': "And that's all this is doing.", 'start': 24586.884, 'duration': 1.32}, {'end': 24592.586, 'text': 'Low outliers found, minimum value, high outliers found.', 'start': 24588.784, 'duration': 3.802}, {'end': 24596.848, 'text': 'Really important actually, outliers are almost everything in data sometimes.', 'start': 24593.506, 'duration': 3.342}, {'end': 24602.791, 'text': 'Sometimes you do this project just to find the outliers because you want to know crime detection.', 'start': 24596.928, 'duration': 5.863}, {'end': 24604.592, 'text': "What are we looking for? We're looking for the outliers.", 'start': 24602.971, 'duration': 1.621}, {'end': 24610.334, 'text': "What doesn't fit a normal business deal? And then we'll go ahead and throw in, just throw in a lot of code.", 'start': 24604.712, 'duration': 5.622}], 'summary': 'Identifying outliers is crucial in data analysis, as they can influence decision-making and reveal important insights. outliers can be critical for crime detection and identifying abnormal business deals.', 'duration': 32.232, 'max_score': 24578.102, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA24578102.jpg'}], 'start': 23695.245, 'title': 'Lstm neural network modeling', 'summary': 'Focuses on setting up an lstm neural network for air quality data analysis, including reshaping input data, creating a sequential model with specific layers, and fitting the model for 500 iterations.', 'chapters': [{'end': 23935.818, 'start': 23695.245, 'title': 'Importing keras and tensorflow for sequential model', 'summary': 'Discusses the process of importing keras and tensorflow for a sequential model, including the explanation of sequential, functional, and subclassing models, and the usage of pre-built layers such as dense and long short-term memory (lstm) for neural networks.', 'duration': 240.573, 'highlights': ['Keras and TensorFlow are imported for a sequential model, which involves importing specific packages under Keras and understanding the sequential, functional, and subclassing models.', 'The sequential model in Keras follows a one-directional flow, while functional models can have complex graph directions and subclassing allows adding custom subclasses for external computations.', 'Pre-built layers in Keras, such as dense (for standard neural network) and LSTM (for long short-term memory), are discussed, with the LSTM being suitable for sequential data.', 'Keras offers a variety of built-in layers, including dense layers, long short-term memory layers, and convolutional layers, making it popular and versatile for processing graphics and custom layers.']}, {'end': 24236.752, 'start': 23936.218, 'title': 'Data formatting for air quality analysis', 'summary': 'Discusses the process of formatting and analyzing air quality data, including details on column structure, data types, and the steps involved in combining date and time for analysis using pandas and python.', 'duration': 300.534, 'highlights': ['The data consists of thousands of rows and columns, including measurements such as O3, CO, NO2, SO2, CO2, VOC, PM1, PM2.5, PM4, and PM10, providing a comprehensive dataset for air quality analysis.', 'The process involves combining date and time columns, followed by converting them into a date-time format, which is crucial for further analysis and visualization of the air quality data.', 'Data formatting and pre-processing are emphasized as essential steps, comprising a significant portion of the coding process, ensuring the accuracy and relevance of the analysis for air quality assessment.']}, {'end': 24552.923, 'start': 24239.159, 'title': 'Data reorganization and descriptive analytics', 'summary': 'Focuses on reorganizing the data by date time, dropping unnecessary columns, creating a new data frame grouped by date time, and performing descriptive analytics such as mean, standard deviation, and quantile calculations for each variable.', 'duration': 313.764, 'highlights': ['The data is reorganized by grouping it together by date time and in the format desired for better manageability and sequential order, resulting in a more manageable data format.', 'The unnecessary columns are dropped, and a new data frame, DF2, is created by grouping by date time and calculating the mean value, providing a clearer representation of the data.', 'Descriptive analytics, including mean, standard deviation, minimum, maximum, and quartile numbers, are obtained using the DF2 describe function, offering valuable insights into the dataset.', 'The quantile for each variable is calculated, providing a deeper understanding of the data distribution and variability, contributing to comprehensive data analysis and interpretation.', "The minimum and maximum IQR, computed using the Q1 and Q3 values, offer insights into the spread and distribution of the data, enabling a detailed examination of the dataset's variability and outliers."]}, {'end': 25150.652, 'start': 24553.704, 'title': 'Data outliers and data set preparation', 'summary': 'Discusses identifying and handling outliers in data, including the identification of low and high outliers, and then focuses on preparing the data set for analysis, including handling skewed data and splitting the data set into training and testing sets. it also covers creating a data set matrix and looking back at historical data to predict future values.', 'duration': 596.948, 'highlights': ['Identifying and Handling Outliers The chapter discusses identifying outliers by comparing values with minimum and maximum IQR, and then handling the outliers by setting their values to null, emphasizing the importance of handling outliers in data analysis and data preparation.', 'Handling Skewed Data and Data Set Splitting It covers the transformation of skewed data using a logarithmic scale, generating a histogram to visualize the data distribution, and splitting the data set into training and testing sets with a 75% to 25% split, ensuring a balanced representation for analysis.', 'Creating Data Set Matrix and Predicting Future Values The chapter details the creation of a data set matrix with a look-back of one, defining the X and Y data for prediction, and explaining the concept of using historical data to predict future values in the data set.']}, {'end': 25663.332, 'start': 25150.652, 'title': 'Lstm neural network modeling', 'summary': 'Discusses the process of setting up an lstm neural network, including reshaping input data, creating a sequential model with lstm and dense layers, and compiling and fitting the model with specific parameters, running for 500 iterations.', 'duration': 512.68, 'highlights': ['The chapter discusses the process of setting up an LSTM neural network, including reshaping input data, creating a sequential model with LSTM and dense layers, and compiling and fitting the model with specific parameters, running for 500 iterations. The chapter covers the process of setting up an LSTM neural network, including reshaping input data, creating a sequential model with LSTM and dense layers, and compiling and fitting the model with specific parameters, running for 500 iterations.', 'The need to reshape the input array in the form of sample time step features to accommodate the long short-term memory layer is emphasized. Emphasizes the need to reshape the input array in the form of sample time step features to accommodate the long short-term memory layer.', 'The process of creating a sequential model with LSTM and dense layers is explained, with a focus on the sequential nature and the role of each layer type in the model. Explains the process of creating a sequential model with LSTM and dense layers, highlighting the sequential nature and the role of each layer type in the model.', 'The compilation of the model with specific parameters, including the loss function and optimizer selection, is detailed, along with the process of fitting the model with train data through multiple epochs and batch iterations. Details the compilation of the model with specific parameters, including the loss function and optimizer selection, as well as the process of fitting the model with train data through multiple epochs and batch iterations.', 'The potential impact of data size on model training time is discussed, highlighting the considerations when running the model on large datasets and the time it may take for completion. Discusses the potential impact of data size on model training time, highlighting the considerations when running the model on large datasets and the time it may take for completion.']}], 'duration': 1968.087, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA23695245.jpg', 'highlights': ['Keras and TensorFlow are imported for a sequential model, involving importing specific packages under Keras and understanding the sequential, functional, and subclassing models.', 'The data consists of thousands of rows and columns, including measurements such as O3, CO, NO2, SO2, CO2, VOC, PM1, PM2.5, PM4, and PM10, providing a comprehensive dataset for air quality analysis.', 'The data is reorganized by grouping it together by date time and in the format desired for better manageability and sequential order, resulting in a more manageable data format.', 'Identifying and Handling Outliers The chapter discusses identifying outliers by comparing values with minimum and maximum IQR, and then handling the outliers by setting their values to null, emphasizing the importance of handling outliers in data analysis and data preparation.', 'The chapter discusses the process of setting up an LSTM neural network, including reshaping input data, creating a sequential model with LSTM and dense layers, and compiling and fitting the model with specific parameters, running for 500 iterations.']}, {'end': 26913.275, 'segs': [{'end': 25694.383, 'src': 'embed', 'start': 25664.417, 'weight': 0, 'content': [{'end': 25667.88, 'text': 'And then we want to start looking at putting it into some other framework,', 'start': 25664.417, 'duration': 3.463}, {'end': 25674.086, 'text': 'like Spark or something that will build the process on there more across multiple processors and multiple computers.', 'start': 25667.88, 'duration': 6.206}, {'end': 25682.532, 'text': "And if we scroll all the way down to the bottom, you're going to see here's our square mean error, 0.0088.", 'start': 25675.367, 'duration': 7.165}, {'end': 25688.938, 'text': "If we scroll way up, you'll see it kind of oscillates between 0.088 and 0.089.", 'start': 25682.532, 'duration': 6.406}, {'end': 25694.383, 'text': "It's right around 250 where you start seeing that oscillation where it's really not going anywhere.", 'start': 25688.938, 'duration': 5.445}], 'summary': 'The square mean error oscillates between 0.088 and 0.089, with an average around 0.0088, suggesting the need for a more scalable framework like spark.', 'duration': 29.966, 'max_score': 25664.417, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA25664417.jpg'}, {'end': 25914.929, 'src': 'embed', 'start': 25884.732, 'weight': 1, 'content': [{'end': 25886.653, 'text': 'If you remember, we did the square means error.', 'start': 25884.732, 'duration': 1.921}, {'end': 25888.514, 'text': 'This is standard deviation.', 'start': 25887.073, 'duration': 1.441}, {'end': 25890.035, 'text': "That's why these numbers are different.", 'start': 25888.554, 'duration': 1.481}, {'end': 25892.977, 'text': "It's saying the same thing that we just talked about.", 'start': 25891.035, 'duration': 1.942}, {'end': 25897.099, 'text': '3.16 is less than 4.40.', 'start': 25894.677, 'duration': 2.422}, {'end': 25897.919, 'text': 'Model is good enough.', 'start': 25897.099, 'duration': 0.82}, {'end': 25899.92, 'text': "We're saying, hey, this model is valid.", 'start': 25898.099, 'duration': 1.821}, {'end': 25901.041, 'text': 'We have a valid model here.', 'start': 25899.94, 'duration': 1.101}, {'end': 25902.582, 'text': 'So we can go ahead and go with that.', 'start': 25901.281, 'duration': 1.301}, {'end': 25909.726, 'text': "And along with putting a formal print out of there, we want to go ahead and plot what's going on.", 'start': 25903.642, 'duration': 6.084}, {'end': 25914.929, 'text': "And this, we just want a pretty graphed here so that people can see what's going on.", 'start': 25911.146, 'duration': 3.783}], 'summary': 'Valid model with standard deviation of 3.16 and 4.40 indicates model validity.', 'duration': 30.197, 'max_score': 25884.732, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA25884732.jpg'}, {'end': 26319.903, 'src': 'embed', 'start': 26297.364, 'weight': 2, 'content': [{'end': 26304.993, 'text': 'And this is really the core right here of TensorFlow and Keras is being able to build your data model quickly and efficiently.', 'start': 26297.364, 'duration': 7.629}, {'end': 26311.918, 'text': 'And, of course, with any data science putting out a pretty graph so that your shareholders again.', 'start': 26305.393, 'duration': 6.525}, {'end': 26319.903, 'text': "we want to take and reduce the information down to something people can look at and say oh, that's what's going on.", 'start': 26311.918, 'duration': 7.985}], 'summary': 'Tensorflow and keras enable quick and efficient data model building, facilitating clear data visualization for stakeholders.', 'duration': 22.539, 'max_score': 26297.364, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA26297364.jpg'}, {'end': 26484.165, 'src': 'embed', 'start': 26458.976, 'weight': 3, 'content': [{'end': 26464.839, 'text': 'They can be used in almost every industry to improve efficiency and optimize the operations.', 'start': 26458.976, 'duration': 5.863}, {'end': 26475.205, 'text': 'Smart devices are everyday objects made intelligent with advanced computations which include artificial intelligence and machine learning.', 'start': 26465.78, 'duration': 9.425}, {'end': 26484.165, 'text': 'They are electronic gadgets that are able to connect, share and interact with its users and other smart devices.', 'start': 26476.343, 'duration': 7.822}], 'summary': 'Smart devices enhance efficiency across industries with advanced technology.', 'duration': 25.189, 'max_score': 26458.976, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA26458976.jpg'}], 'start': 25664.417, 'title': 'Analyzing model performance, prediction, and ai technologies', 'summary': 'Discusses evaluating machine learning model performance with a mean square error of 0.0088, examining prediction accuracy with a root mean square error of 3.16, creating train and test predictions for plotting, and exploring the top 10 ai technologies including natural language generation and smart devices.', 'chapters': [{'end': 25705.994, 'start': 25664.417, 'title': 'Analyzing model performance and optimization', 'summary': 'Discusses the evaluation of a machine learning model, highlighting a mean square error of 0.0088 and the observation that oscillation occurs around 250 epochs, suggesting the redundancy of a full 500 epochs for retraining.', 'duration': 41.577, 'highlights': ["Observation of mean square error of 0.0088. The mean square error of 0.0088 is highlighted as a key metric for evaluating the model's performance.", 'Identification of oscillation around 250 epochs. Noting the occurrence of oscillation around 250 epochs, indicating that a full 500 epochs for retraining may be redundant.']}, {'end': 25937.465, 'start': 25706.474, 'title': 'Model prediction and evaluation', 'summary': "Discusses the process of running predictions on both training and test data to evaluate the model's performance, using root mean square error calculations to compare the model's accuracy, with the test score of 3.16 indicating a valid model.", 'duration': 230.991, 'highlights': ["The process of running predictions on both training and test data allows for the evaluation of the model's performance, where the test score of 3.16 indicates a valid model, as it is less than the threshold of 4.40.", "Utilizing root mean square error calculations to compare the error between the programmed data and new data, helping to identify any bias and assess the model's accuracy.", "The importance of evaluating the model's performance by comparing the error for both training and testing data, with the test score being the key metric for demonstrating the model's effectiveness when presenting the results to others."]}, {'end': 26319.903, 'start': 25937.825, 'title': 'Creating train and test predictions for plotting', 'summary': 'Focuses on creating train and test predictions for plotting in data analytics using tensorflow and keras, emphasizing the importance of efficiently building data models and producing visually interpretable graphs.', 'duration': 382.078, 'highlights': ['The chapter emphasizes the importance of efficiently building data models and producing visually interpretable graphs, highlighting the process of creating train and test predictions for plotting in data analytics using TensorFlow and Keras.', 'Explains the process of creating train and test predictions for plotting, showcasing the original data set, train prediction, and test prediction, and emphasizing the need for visually interpretable graphs for effective communication with shareholders.', 'Discusses the importance of not overcomplicating the sequential model in TensorFlow and Keras, advising against using an excessive number of layers to avoid system crashes and highlighting the advantage of quickly running models as a data scientist.', 'Emphasizes the core functionality of TensorFlow and Keras in efficiently building data models and producing visually interpretable graphs for effective communication with shareholders, reducing complex information into easily understandable visuals.']}, {'end': 26913.275, 'start': 26320.123, 'title': 'Top 10 ai technologies', 'summary': 'Discusses the top 10 artificial intelligence technologies, including natural language generation, smart devices, virtual agents, speech recognition, and more, with examples of vendors and applications.', 'duration': 593.152, 'highlights': ['Natural Language Generation is a trendy technology used in customer service, report generation, and summarizing business intelligence insights, with sample vendors like ATVO, Automated Insights, SAS, and more. Natural Language Generation is widely used in customer service, report generation, and summarizing business intelligence insights, with sample vendors like ATVO, Automated Insights, SAS, Cambridge Semantics, Digital Reasoning, and Narrative Science.', 'Smart Devices are becoming more popular and are used in almost every industry to improve efficiency and optimize operations, including smart watches, smart glasses, smart phones, and smart speakers. Smart Devices are electronic gadgets that are able to connect, share, and interact with users and other smart devices, used in almost every industry to improve efficiency and optimize operations.', 'Virtual Agents like Google Assistant and Amazon Alexa can make reservations, book appointments, place orders, and provide product information, with companies providing virtual agents including Microsoft, Google, Amazon, and Assist AI. Virtual Agents like Google Assistant and Amazon Alexa can make reservations, book appointments, place orders, and provide product information, with companies providing virtual agents including Microsoft, Google, Amazon, and Assist AI.', 'Speech Recognition is used in interactive voice response systems and mobile applications, with companies offering speech recognition services including Nuance Communications, OpenText, NICE, and Wearint Systems. Speech Recognition is used in interactive voice response systems and mobile applications, with companies offering speech recognition services including Nuance Communications, OpenText, NICE, and Wearint Systems.', 'Machine Learning platforms are becoming more popular with the help of algorithms, APIs, big data, and applications and training tools, and are widely used for categorization and prediction, with sample vendors like Amazon, Fractal Analytics, Google, and Microsoft. Machine Learning platforms are becoming more popular with the help of algorithms, APIs, big data, and applications and training tools, and are widely used for categorization and prediction, with sample vendors like Amazon, Fractal Analytics, Google, and Microsoft.']}], 'duration': 1248.858, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PXwUEJVSAeA/pics/PXwUEJVSAeA25664417.jpg', 'highlights': ["The mean square error of 0.0088 is highlighted as a key metric for evaluating the model's performance.", 'The test score of 3.16 indicates a valid model, as it is less than the threshold of 4.40.', 'The process of creating train and test predictions for plotting in data analytics using TensorFlow and Keras is emphasized.', 'Smart Devices are electronic gadgets that are able to connect, share, and interact with users and other smart devices, used in almost every industry to improve efficiency and optimize operations.']}], 'highlights': ["AI's pervasive use in smartphones, cars, social media feeds, video games, banking, surveillance, and many other aspects of daily life.", 'The global AI market is expected to reach a revenue of $118 billion by 2025.', 'The future of AI includes automated transportation, augmented human-robot interactions, and home robots for elderly assistance.', 'Reinforcement learning is likened to human learning and its importance in teaching machines how to learn is highlighted, offering a significant advancement in the field of AI and machine learning.', "Implementation of multiple linear regression in Python to predict company's profit based on R&D, administration costs, and marketing costs achieving 94.6% accuracy.", 'Achieved 94.6% accuracy in predicting loan repayments using decision tree algorithm, a powerful tool for banks.', 'KNN is a fundamental concept in machine learning, providing a basis for various other algorithms, and it is primarily used for classification tasks.', 'The chapter covers various matrix operations including addition, subtraction, scalar multiplication, matrix and vector multiplication, matrix to matrix multiplication, transpose, identity matrix, and inverse matrix in NumPy, with examples and use cases for data science and plotting.', 'Calculus is essential in neural networks and reverse propagation, providing mathematical solutions for solving complex multivariate differentiations and integrations, crucial for data analysis and back-end scripting.', 'The chapter covers basic statistics such as minimum, maximum, range, mean, quartiles, and descriptive statistics using Pandas, providing a quick and comprehensive way to analyze data.', 'The chapter covers the creation of a Naive Bayes model, achieving 65 correct predictions and 3 incorrect predictions.', 'Deep learning helps make predictions about natural events, speech comprehension, image recognition, real-time advertising.', 'TensorFlow 2.0 is a popular open source library for building machine learning and deep learning models, with scalability across machines and large data sets.', 'Keras and TensorFlow are imported for a sequential model, involving importing specific packages under Keras and understanding the sequential, functional, and subclassing models.', "The mean square error of 0.0088 is highlighted as a key metric for evaluating the model's performance.", 'Smart Devices are electronic gadgets that are able to connect, share, and interact with users and other smart devices, used in almost every industry to improve efficiency and optimize operations.']}