title
Your First Step into ML | Learn The Basics of Machine Learning | Great Learning

description
🔥1000+ Free Courses With Free Certificates: https://www.mygreatlearning.com/academy?ambassador_code=GLYT_DES_Top_SEP22&utm_source=GLYT&utm_campaign=GLYT_DES_Top_SEP22 What is Machine Learning? Why is there a need for ML? How does it work? So if you are someone who wants to get started with ML or someone who wants to know how it works. Then you are in the right place. We will be discussing the basics and the dynamics of this subject so that you can get an idea of it and take your first step in the ML world. #GreatLakes #GreatLearning #ML - Great Learning has collaborated with the University of Texas at Austin for the PG Program in Artificial Intelligence and Machine Learning and with UT Austin McCombs School of Business for the PG Program in Analytics and Business Intelligence. About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://bit.ly/2s92TDX - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube

detail
{'title': 'Your First Step into ML | Learn The Basics of Machine Learning | Great Learning', 'heatmap': [], 'summary': 'Covers fundamental concepts of machine learning, contrasting it with traditional programming and emphasizing its applications in real-life scenarios such as fraud detection, medical diagnosis, and decision making. it also includes practical exercises in python, data preprocessing using pandas, and building linear regression models, providing a comprehensive overview for beginners.', 'chapters': [{'end': 304.112, 'segs': [{'end': 58.303, 'src': 'embed', 'start': 33.769, 'weight': 0, 'content': [{'end': 40.513, 'text': 'So this talk is primarily for people who are interested to get started with machine learning.', 'start': 33.769, 'duration': 6.744}, {'end': 46.817, 'text': 'So if you have heard about machine learning and you wonder how it works, then this is the right talk for you.', 'start': 40.893, 'duration': 5.924}, {'end': 58.303, 'text': 'Also, in the world of technology, what happens commonly is when new things come out, a lot of documentation, a lot of tutorials,', 'start': 48.538, 'duration': 9.765}], 'summary': 'Introductory talk for beginners interested in machine learning.', 'duration': 24.534, 'max_score': 33.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY033769.jpg'}, {'end': 185.729, 'src': 'embed', 'start': 151.091, 'weight': 1, 'content': [{'end': 155.071, 'text': 'are witnessing machine learning and how it is elevating the user experience.', 'start': 151.091, 'duration': 3.98}, {'end': 163.995, 'text': "So we'll then go and look, where are we already witnessing machine learning in action, and we are all experiencing it, whether we know it or not.", 'start': 155.851, 'duration': 8.144}, {'end': 173.04, 'text': "So after we cover these basic topics, we'll actually get to the core of this talk, where we will discuss how can a machine learn.", 'start': 165.736, 'duration': 7.304}, {'end': 185.729, 'text': "So, if you have any programming background or any tech background, you'll know that machine learning to, uh is, is something like magic.", 'start': 174.961, 'duration': 10.768}], 'summary': 'Machine learning is elevating user experience, witnessed in action by all, discussed in the talk.', 'duration': 34.638, 'max_score': 151.091, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY0151091.jpg'}, {'end': 245.307, 'src': 'embed', 'start': 215.166, 'weight': 2, 'content': [{'end': 217.648, 'text': 'After that, we get to a hands-on part.', 'start': 215.166, 'duration': 2.482}, {'end': 224.814, 'text': 'This is where we will actually be writing code in Python, and we will be solving a couple of real-life problems using machine learning.', 'start': 218.028, 'duration': 6.786}, {'end': 229.317, 'text': 'So one such problem is to predict the weight of people based on their height.', 'start': 225.694, 'duration': 3.623}, {'end': 235.182, 'text': 'We will use a dataset which has weight and height, and we will see how we can make our machine learn.', 'start': 230.758, 'duration': 4.424}, {'end': 238.581, 'text': 'and predict the weight of people given their height.', 'start': 235.979, 'duration': 2.602}, {'end': 239.762, 'text': "So that's the first problem.", 'start': 238.701, 'duration': 1.061}, {'end': 245.307, 'text': "The next problem we'll do is we will try to create machine learning.", 'start': 240.623, 'duration': 4.684}], 'summary': 'Hands-on python coding for machine learning to predict weight based on height.', 'duration': 30.141, 'max_score': 215.166, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY0215166.jpg'}], 'start': 5.964, 'title': 'Machine learning fundamentals', 'summary': 'Introduces the fundamentals of machine learning, emphasizing its simplicity and relevance to beginners, discusses its impact on user experience, and covers solving real-life problems using python with hands-on activities.', 'chapters': [{'end': 151.091, 'start': 5.964, 'title': 'First steps in machine learning', 'summary': 'Introduces the target audience, content agenda, and the need for machine learning, emphasizing its simplicity and relevance to beginners.', 'duration': 145.127, 'highlights': ['The talk is designed for beginners interested in starting with machine learning and aims to simplify the understanding of the topic in plain English, catering to the common struggle of grasping new technologies filled with jargons and buzzwords.', "The VP of Technology, Vinod Venkatraman, will cover the agenda, including what machine learning is, the need for machine learning in today's day and age, and examples of software, websites, and services utilizing machine learning.", 'The talk will provide a plain English overview of machine learning, addressing the need for the new technology and its popularity, making it suitable for individuals who have faced difficulties in understanding machine learning due to its complex jargons and buzzwords.']}, {'end': 213.424, 'start': 151.091, 'title': 'Machine learning: elevating user experience', 'summary': 'Discusses witnessing machine learning in action, its impact on user experience, and the exploration of different types of machine learning algorithms.', 'duration': 62.333, 'highlights': ['The chapter explores witnessing machine learning in action, impacting user experience.', 'The discussion delves into unraveling the concept of machine learning and understanding its core principles.', 'Exploration of different types of machine learning algorithms to create a framework for problem-solving.']}, {'end': 304.112, 'start': 215.166, 'title': 'Hands-on machine learning with python', 'summary': 'Will cover solving real-life problems using machine learning, such as predicting weight based on height, shortlisting resumes using python, and discussing further learning opportunities in a plain english manner with hands-on activities.', 'duration': 88.946, 'highlights': ['We will be solving a couple of real-life problems using machine learning, such as predicting the weight of people based on their height and shortlisting resumes using Python.', 'The first problem involves predicting the weight of people based on their height using a dataset, demonstrating practical application of machine learning.', "The next problem entails creating a Python program to shortlist resumes based on candidates' degree, percentile, and work experience, showcasing the practical use of machine learning in recruitment.", 'The talk will conclude with a discussion on further learning opportunities in machine learning, presented in plain English with hands-on activities.']}], 'duration': 298.148, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY05964.jpg', 'highlights': ['The talk is designed for beginners interested in starting with machine learning and aims to simplify the understanding of the topic in plain English, catering to the common struggle of grasping new technologies filled with jargons and buzzwords.', 'The chapter explores witnessing machine learning in action, impacting user experience.', 'We will be solving a couple of real-life problems using machine learning, such as predicting the weight of people based on their height and shortlisting resumes using Python.']}, {'end': 925.405, 'segs': [{'end': 738.666, 'src': 'embed', 'start': 712.634, 'weight': 0, 'content': [{'end': 717.174, 'text': 'machine learning is when the machine learns without being explicitly programmed to do so.', 'start': 712.634, 'duration': 4.54}, {'end': 720.616, 'text': 'So traditionally, software has been built by explicit programming.', 'start': 717.834, 'duration': 2.782}, {'end': 724.158, 'text': 'The machine only does what the engineer actually programs it to do.', 'start': 720.636, 'duration': 3.522}, {'end': 727.92, 'text': "If he programs the machine to act as a calculator, it'll act as a calculator.", 'start': 724.858, 'duration': 3.062}, {'end': 730.942, 'text': "If he programs the machine to act like a game, it'll act like a game.", 'start': 728.5, 'duration': 2.442}, {'end': 738.666, 'text': 'Whereas here, without explicitly programming, we are actually telling the machine that, okay, these are.', 'start': 731.722, 'duration': 6.944}], 'summary': 'Machine learning enables machines to learn without explicit programming, unlike traditional software.', 'duration': 26.032, 'max_score': 712.634, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY0712634.jpg'}, {'end': 810.7, 'src': 'embed', 'start': 784.561, 'weight': 1, 'content': [{'end': 789.943, 'text': 'we need systems which can continuously learn and adapt and improve.', 'start': 784.561, 'duration': 5.382}, {'end': 791.383, 'text': 'so what do i mean by that?', 'start': 789.943, 'duration': 1.44}, {'end': 797.344, 'text': 'right, so if we have a machine learning based system enabled, when people start writing once in new ways,', 'start': 791.383, 'duration': 5.961}, {'end': 800.405, 'text': 'you know the machine can learn automatically as soon as you point them to.', 'start': 797.344, 'duration': 3.061}, {'end': 803.338, 'text': 'okay, these are the new ways it which people are writing once.', 'start': 800.405, 'duration': 2.933}, {'end': 810.7, 'text': 'Imagine that with actually the traditional way where the developer will now have to take up a task to handle this new way of writing one.', 'start': 804.418, 'duration': 6.282}], 'summary': 'Continuous learning ml systems adapt to new writing styles, reducing developer workload.', 'duration': 26.139, 'max_score': 784.561, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY0784561.jpg'}], 'start': 305.614, 'title': 'Understanding machine learning', 'summary': "Contrasts machine learning with traditional programming, providing examples such as resume shortlisting and handwritten digit recognition, highlighting the shift from explicit programming to the machine learning process. it emphasizes the significance of machine learning in today's data-driven world and the need for systems that can continuously learn, adapt, and improve from the vast amount of data being generated.", 'chapters': [{'end': 614.059, 'start': 305.614, 'title': 'Understanding machine learning', 'summary': 'Explains the concept of machine learning, contrasting it with traditional programming and providing examples such as resume shortlisting and handwritten digit recognition, highlighting the shift from explicit programming to the machine learning process.', 'duration': 308.445, 'highlights': ['Machine learning is the process of a machine learning without being explicitly programmed, contrasting it with traditional programming. This statement contrasts traditional programming, where the business logic is explicitly programmed into the code, with machine learning, where the machine learns from the data using a machine learning technique.', 'Using the example of resume shortlisting, it explains how traditionally the business logic for shortlisting resumes is explicitly programmed, whereas in machine learning, the machine learns from the data to determine the qualities to look for in a resume. Traditionally, the business logic for shortlisting resumes is explicitly programmed, while with machine learning, the machine learns from the data to determine the qualities to look for in a resume.', 'Another example provided is the task of recognizing handwritten digits, where traditionally the engineer would explicitly program the characteristics of each digit, while in machine learning, the machine is given a lot of examples and figures out the patterns on its own. In the task of recognizing handwritten digits, traditionally the engineer would explicitly program the characteristics of each digit, while in machine learning, the machine is given a lot of examples and figures out the patterns on its own.']}, {'end': 925.405, 'start': 614.059, 'title': 'Machine learning: basics and importance', 'summary': "Explains the concept of machine learning and its significance in today's data-driven world, emphasizing the need for systems that can continuously learn, adapt, and improve from the vast amount of data being generated, and highlights the difference between explicit programming and machine learning.", 'duration': 311.346, 'highlights': ['Machine learning is the process of enabling a machine to learn without explicit programming, utilizing historical data to recognize patterns and make future predictions, thus providing a scalable solution for recognizing handwritten digits. Machine learning enables the machine to learn without explicit programming, using historical data for recognizing patterns and making predictions, providing a scalable solution for recognizing handwritten digits.', 'The need for machine learning arises from the constant generation of data, making it impractical for humans to handcraft and code all the business logic into programs, and machine learning allows systems to continuously learn, adapt, and improve based on the data provided. Machine learning is necessary due to the volume of data being generated, as it is impractical for humans to handcraft and code all business logic into programs, and machine learning enables systems to continuously learn, adapt, and improve based on the provided data.', 'Explicit programming involves instructing the machine to perform specific tasks, while machine learning involves pointing the machine to historical data and pre-classified facts, allowing it to learn and continuously improve without explicit programming. Explicit programming requires instructing the machine to perform specific tasks, whereas machine learning involves pointing the machine to historical data and pre-classified facts, enabling it to learn and continuously improve without explicit programming.']}], 'duration': 619.791, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY0305614.jpg', 'highlights': ['Machine learning contrasts traditional programming by enabling the machine to learn without explicit programming.', 'Machine learning allows systems to continuously learn, adapt, and improve based on the provided data.', 'Traditional programming involves explicitly programming business logic, while machine learning learns from data to determine qualities.']}, {'end': 1558.361, 'segs': [{'end': 959.502, 'src': 'embed', 'start': 925.405, 'weight': 1, 'content': [{'end': 926.526, 'text': 'probably images coming a second.', 'start': 925.405, 'duration': 1.121}, {'end': 929.268, 'text': 'And how does a human being ever catch up? Cannot catch up.', 'start': 926.626, 'duration': 2.642}, {'end': 934.012, 'text': 'Right. So, to handle the explosion of data, machines have to start looking at.', 'start': 930.148, 'duration': 3.864}, {'end': 940.197, 'text': 'you know, be able to look at this data in this case, which is images, and you know, tag people in it.', 'start': 934.012, 'duration': 6.185}, {'end': 942.679, 'text': 'It has to do it automatically.', 'start': 940.717, 'duration': 1.962}, {'end': 948.163, 'text': "So that's the other reason we need machine learning is basically to just handle the amount of data that is getting generated.", 'start': 943.119, 'duration': 5.044}, {'end': 952.778, 'text': 'Next, is to reduce mundane work for human beings.', 'start': 950.236, 'duration': 2.542}, {'end': 954.319, 'text': 'Now, just imagine that job.', 'start': 953.218, 'duration': 1.101}, {'end': 957.28, 'text': 'Day in, day out, you sit and you keep tagging faces on photographs.', 'start': 954.359, 'duration': 2.921}, {'end': 959.502, 'text': 'That is just a waste of the human race.', 'start': 957.361, 'duration': 2.141}], 'summary': 'Machines use machine learning to process images and tag people to handle the explosion of data, reducing mundane work for humans.', 'duration': 34.097, 'max_score': 925.405, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY0925405.jpg'}, {'end': 1002.605, 'src': 'embed', 'start': 978.351, 'weight': 0, 'content': [{'end': 986.155, 'text': 'So machine learning, once you enable the machine learning system, once you have that, it will reduce the mundane work for us.', 'start': 978.351, 'duration': 7.804}, {'end': 991.338, 'text': 'And coming to the last point, it is to reduce errors and improve efficiency.', 'start': 988.177, 'duration': 3.161}, {'end': 998.042, 'text': 'So all said and done, human beings in general are more creative than machines.', 'start': 992.579, 'duration': 5.463}, {'end': 1002.605, 'text': 'Human beings have this creative angle in their brain.', 'start': 999.623, 'duration': 2.982}], 'summary': 'Machine learning reduces mundane work, errors, and improves efficiency; human creativity is unmatched.', 'duration': 24.254, 'max_score': 978.351, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY0978351.jpg'}, {'end': 1180.419, 'src': 'embed', 'start': 1152.746, 'weight': 3, 'content': [{'end': 1159.69, 'text': 'It is continuously learning based on all the signals it is getting from the device and all the data that you are exposing to it.', 'start': 1152.746, 'duration': 6.944}, {'end': 1164.534, 'text': 'And it is trying to act as a virtual assistant.', 'start': 1162.212, 'duration': 2.322}, {'end': 1169.177, 'text': "So in today's day and age, we don't want every human being to have a human assistant.", 'start': 1165.314, 'duration': 3.863}, {'end': 1172.599, 'text': 'Instead, Google is trying to solve it with a virtual assistant, with a Google assistant.', 'start': 1169.277, 'duration': 3.322}, {'end': 1174.175, 'text': 'sitting right in your phones.', 'start': 1173.174, 'duration': 1.001}, {'end': 1180.419, 'text': 'So Google Assistant is using machine learning to actually learn about you, right??', 'start': 1175.055, 'duration': 5.364}], 'summary': 'Google assistant uses machine learning to act as a virtual assistant on phones, learning from user data.', 'duration': 27.673, 'max_score': 1152.746, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY01152746.jpg'}, {'end': 1301.52, 'src': 'embed', 'start': 1277.521, 'weight': 4, 'content': [{'end': 1284.183, 'text': "So Google Maps is essentially learning the business problem it's trying to solve is to help people get from place A to B.", 'start': 1277.521, 'duration': 6.662}, {'end': 1288.518, 'text': 'And to do that, it is learning from various things.', 'start': 1285.937, 'duration': 2.581}, {'end': 1290.738, 'text': 'It is looking at satellite images, looking at traffic.', 'start': 1288.578, 'duration': 2.16}, {'end': 1296.599, 'text': 'It is looking at what routes normally other people take when they want to go from A to B.', 'start': 1291.158, 'duration': 5.441}, {'end': 1297.8, 'text': 'And it is continuously improving.', 'start': 1296.599, 'duration': 1.201}, {'end': 1301.52, 'text': "Maybe first time it will suggest you a road that's not very good.", 'start': 1297.88, 'duration': 3.64}], 'summary': 'Google maps aims to help people navigate by learning from satellite images, traffic, and popular routes, continuously improving its recommendations.', 'duration': 23.999, 'max_score': 1277.521, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY01277521.jpg'}, {'end': 1465.061, 'src': 'embed', 'start': 1416.985, 'weight': 5, 'content': [{'end': 1425.788, 'text': 'But if a machine can learn and recommend, then we can be certain that we are providing similar experience to all of our users, and at some quality.', 'start': 1416.985, 'duration': 8.803}, {'end': 1428.228, 'text': "And for Netflix, that's critical.", 'start': 1427.108, 'duration': 1.12}, {'end': 1435.75, 'text': "So if people open Netflix to watch something or they're just getting bored, they open Netflix and nothing that comes on the screen is appealing,", 'start': 1428.308, 'duration': 7.442}, {'end': 1438.01, 'text': "then they'll just close Netflix and go to some competition.", 'start': 1435.75, 'duration': 2.26}, {'end': 1439.671, 'text': 'maybe Amazon Prime not sure.', 'start': 1438.01, 'duration': 1.661}, {'end': 1443.992, 'text': 'But yeah, so for them, machine learning is critical.', 'start': 1440.191, 'duration': 3.801}, {'end': 1446.912, 'text': 'Next, online advertising.', 'start': 1445.812, 'duration': 1.1}, {'end': 1454.754, 'text': 'So again, online advertising, the domain, machine learning has made a lot of, helped the domain a lot.', 'start': 1447.532, 'duration': 7.222}, {'end': 1462.218, 'text': "So here, based on the user and his, you know what he's right now looking for, right?", 'start': 1455.333, 'duration': 6.885}, {'end': 1463.78, 'text': 'Sometimes these creepy things happen right?', 'start': 1462.278, 'duration': 1.502}, {'end': 1465.061, 'text': "You're looking to buy a Nike shoes.", 'start': 1463.8, 'duration': 1.261}], 'summary': 'Machine learning crucial for netflix user experience and online advertising personalization.', 'duration': 48.076, 'max_score': 1416.985, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY01416985.jpg'}, {'end': 1514.367, 'src': 'embed', 'start': 1482.884, 'weight': 7, 'content': [{'end': 1486.326, 'text': "and is trying to understand what is the thing right now that you're looking to purchase.", 'start': 1482.884, 'duration': 3.442}, {'end': 1492.109, 'text': 'And then it is trying to use that to show you more relevant, more meaningful ads.', 'start': 1487.386, 'duration': 4.723}, {'end': 1496.451, 'text': "So that's how online advertising and machine learning are being used.", 'start': 1493.309, 'duration': 3.142}, {'end': 1500.773, 'text': 'So moving on, credit card fraud.', 'start': 1498.792, 'duration': 1.981}, {'end': 1508.877, 'text': 'So one of the risks with having credit cards is that it is easy to swipe and use.', 'start': 1503.074, 'duration': 5.803}, {'end': 1514.367, 'text': "If you lose your credit card, It's easy for anyone to go to a store, swipe it, and use it.", 'start': 1509.877, 'duration': 4.49}], 'summary': 'Online advertising uses machine learning for more relevant ads. credit card fraud risks include easy swipe and use by unauthorized persons.', 'duration': 31.483, 'max_score': 1482.884, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY01482884.jpg'}], 'start': 925.405, 'title': 'Machine learning applications', 'summary': 'Highlights the need for machine learning to handle data explosion, reduce errors, and improve efficiency. it also discusses machine learning applications in google assistant, google maps, netflix, online advertising, and credit card fraud prevention.', 'chapters': [{'end': 1111.002, 'start': 925.405, 'title': 'Need for machine learning', 'summary': 'Explains the need for machine learning to handle the explosion of data, reduce mundane work for human beings, and reduce errors while improving efficiency, which is crucial due to the limitations of human capacity and the advantages of machine learning in handling repetitive tasks and continuous learning.', 'duration': 185.597, 'highlights': ['Machine learning is needed to handle the explosion of data Machines are required to handle the vast amount of data being generated, as human beings cannot catch up with the data explosion.', 'Machine learning reduces mundane work for human beings Automation of tasks like tagging faces in photographs reduces mundane work for human beings, enabling them to focus on creative and innovative work.', 'Machine learning reduces errors and improves efficiency Machines, being bug-free, can perform repetitive tasks without errors, continuously learning and improving efficiency, unlike human beings prone to making errors with repetitive tasks.']}, {'end': 1558.361, 'start': 1111.002, 'title': 'Machine learning in everyday services', 'summary': 'Discusses how machine learning is used in google assistant to learn user preferences, in google maps to optimize routes and in netflix for personalized recommendations, with examples from online advertising and credit card fraud prevention.', 'duration': 447.359, 'highlights': ['Google Assistant Google Assistant uses machine learning to learn user preferences, appointments, and travel patterns, providing personalized assistance based on data signals and user input.', 'Google Maps Google Maps employs machine learning to optimize routes based on real-time traffic data and user behavior, continuously improving suggestions for efficient navigation.', 'Netflix Netflix utilizes machine learning for personalized recommendations, analyzing user and similar user preferences to enhance content discovery and user engagement.', 'Online Advertising Machine learning in online advertising leverages user data to deliver relevant and meaningful ads, based on past activities and current interests, improving ad targeting and user experience.', 'Credit Card Fraud Prevention Systems use machine learning to detect irregularities in credit card usage, learning from past spending behavior to identify deviations and introduce further authentication measures to prevent fraud.']}], 'duration': 632.956, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY0925405.jpg', 'highlights': ['Machine learning reduces errors and improves efficiency Machines, being bug-free, can perform repetitive tasks without errors, continuously learning and improving efficiency, unlike human beings prone to making errors with repetitive tasks.', 'Machine learning reduces mundane work for human beings Automation of tasks like tagging faces in photographs reduces mundane work for human beings, enabling them to focus on creative and innovative work.', 'Machine learning is needed to handle the explosion of data Machines are required to handle the vast amount of data being generated, as human beings cannot catch up with the data explosion.', 'Google Assistant Google Assistant uses machine learning to learn user preferences, appointments, and travel patterns, providing personalized assistance based on data signals and user input.', 'Google Maps Google Maps employs machine learning to optimize routes based on real-time traffic data and user behavior, continuously improving suggestions for efficient navigation.', 'Netflix Netflix utilizes machine learning for personalized recommendations, analyzing user and similar user preferences to enhance content discovery and user engagement.', 'Online Advertising Machine learning in online advertising leverages user data to deliver relevant and meaningful ads, based on past activities and current interests, improving ad targeting and user experience.', 'Credit Card Fraud Prevention Systems use machine learning to detect irregularities in credit card usage, learning from past spending behavior to identify deviations and introduce further authentication measures to prevent fraud.']}, {'end': 2237.945, 'segs': [{'end': 1583.687, 'src': 'embed', 'start': 1558.781, 'weight': 3, 'content': [{'end': 1567.403, 'text': 'And if things are off suddenly, if a one lakh swipe happens on your credit card, it can tag it and enable further authentication mechanisms.', 'start': 1558.781, 'duration': 8.622}, {'end': 1569.344, 'text': 'maybe send an OTP, which is needed, or whatever.', 'start': 1567.403, 'duration': 1.941}, {'end': 1571.641, 'text': "But the aim is that's a critical thing.", 'start': 1570.1, 'duration': 1.541}, {'end': 1577.984, 'text': 'So without machine learning, again, a human being cannot sit and do this for all the users of the credit card.', 'start': 1571.681, 'duration': 6.303}, {'end': 1583.687, 'text': 'So medical diagnosis, similar.', 'start': 1580.866, 'duration': 2.821}], 'summary': 'Machine learning enables automated detection of unusual credit card activities, such as a one lakh swipe, and triggers further authentication mechanisms, making it critical for large-scale operations.', 'duration': 24.906, 'max_score': 1558.781, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY01558781.jpg'}, {'end': 1648.912, 'src': 'embed', 'start': 1622.021, 'weight': 2, 'content': [{'end': 1629.997, 'text': 'So very critical machine learning again is being used there, right? So there are more examples, speech recognition, self-driving cars.', 'start': 1622.021, 'duration': 7.976}, {'end': 1631.518, 'text': 'So self-driving cars have this problem.', 'start': 1630.057, 'duration': 1.461}, {'end': 1633.48, 'text': 'They have to interpret the world in front of them.', 'start': 1631.558, 'duration': 1.922}, {'end': 1640.986, 'text': 'When a car is driving, normally the driver is interpreting the world and the machine, the car itself, is just providing different controls.', 'start': 1633.5, 'duration': 7.486}, {'end': 1648.912, 'text': 'Accelerate, brake, steer, indicators, lights, wipers.', 'start': 1642.787, 'duration': 6.125}], 'summary': 'Machine learning used in speech recognition and self-driving cars, which interpret the world in front of them.', 'duration': 26.891, 'max_score': 1622.021, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY01622021.jpg'}, {'end': 1734.553, 'src': 'embed', 'start': 1704.045, 'weight': 0, 'content': [{'end': 1705.026, 'text': "it's happening all across.", 'start': 1704.045, 'duration': 0.981}, {'end': 1707.688, 'text': "it's happening on your phones, it's happening on your laptops.", 'start': 1705.026, 'duration': 2.662}, {'end': 1713.784, 'text': "it's happening everywhere, and Machine learning is actually the core differentiating factor for many products today.", 'start': 1707.688, 'duration': 6.096}, {'end': 1720.807, 'text': 'If you have a standard product which will just do what it is supposed to do, what the programmer programmed it to do, it can only do so much.', 'start': 1714.464, 'duration': 6.343}, {'end': 1729.631, 'text': 'But if you have a machine learning enabled software or a service, then you have the levers to actually wow your users right?', 'start': 1722.768, 'duration': 6.863}, {'end': 1731.832, 'text': 'So Google Maps wows me whenever I use it.', 'start': 1730.011, 'duration': 1.821}, {'end': 1734.553, 'text': 'Netflix wows me whenever I launch it.', 'start': 1733.052, 'duration': 1.501}], 'summary': 'Machine learning is a core differentiator for many products today, enabling them to wow users.', 'duration': 30.508, 'max_score': 1704.045, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY01704045.jpg'}, {'end': 1765.653, 'src': 'embed', 'start': 1742.416, 'weight': 1, 'content': [{'end': 1750.802, 'text': "So maybe doctors can vouch for how, in medical diagnosis, all this image, the scan thing that is happening, how good accuracy it's giving.", 'start': 1742.416, 'duration': 8.386}, {'end': 1753.504, 'text': 'So essentially, it is happening across all domains.', 'start': 1751.323, 'duration': 2.181}, {'end': 1760.569, 'text': 'And if you learn the skill of how to be able to create systems which are machine learning enabled,', 'start': 1754.185, 'duration': 6.384}, {'end': 1765.653, 'text': 'then you will be creating products which have the potential to wow its users.', 'start': 1760.569, 'duration': 5.084}], 'summary': 'Machine learning enables high accuracy medical diagnoses and product innovation.', 'duration': 23.237, 'max_score': 1742.416, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY01742416.jpg'}, {'end': 1998.962, 'src': 'embed', 'start': 1971.406, 'weight': 4, 'content': [{'end': 1973.868, 'text': "So the core strength of machines is they're good with numbers,", 'start': 1971.406, 'duration': 2.462}, {'end': 1979.791, 'text': "they're extremely fast in mathematical computations and they're capable of remembering the past.", 'start': 1973.868, 'duration': 5.923}, {'end': 1984.013, 'text': 'Whatever they did in the past or whatever you told them in the past.', 'start': 1980.852, 'duration': 3.161}, {'end': 1987.415, 'text': 'So these are the key things that machines are good at.', 'start': 1984.754, 'duration': 2.661}, {'end': 1990.777, 'text': 'Numbers, mathematical computation, and remembering.', 'start': 1988.316, 'duration': 2.461}, {'end': 1995.26, 'text': 'Remembering what you told it or remembering what it computed.', 'start': 1992.538, 'duration': 2.722}, {'end': 1997.641, 'text': 'So that is the core strength.', 'start': 1996.741, 'duration': 0.9}, {'end': 1998.962, 'text': 'So that answers our first question.', 'start': 1997.661, 'duration': 1.301}], 'summary': 'Machines excel in numbers, computations, and memory.', 'duration': 27.556, 'max_score': 1971.406, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY01971406.jpg'}, {'end': 2111.374, 'src': 'embed', 'start': 2082.518, 'weight': 6, 'content': [{'end': 2089.221, 'text': 'oh, when this happens, when the number is like this in this range, then this happens, then that happens, so on and so forth.', 'start': 2082.518, 'duration': 6.703}, {'end': 2092.442, 'text': 'So what can a machine learn from? It can learn from data.', 'start': 2090.061, 'duration': 2.381}, {'end': 2100.449, 'text': 'right past events and their outcomes, more specifically, the data represented as past events and their outcomes,', 'start': 2093.206, 'duration': 7.243}, {'end': 2105.551, 'text': "and all of that represented as numbers, because they're awesome with numbers.", 'start': 2100.449, 'duration': 5.102}, {'end': 2111.374, 'text': 'okay, now we will answer the third questions.', 'start': 2105.551, 'duration': 5.823}], 'summary': 'Machine learning learns from past events and outcomes represented as numbers.', 'duration': 28.856, 'max_score': 2082.518, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY02082518.jpg'}, {'end': 2224.017, 'src': 'embed', 'start': 2190.168, 'weight': 7, 'content': [{'end': 2195.352, 'text': 'So these are two things where a machine is learning from data and making predictions.', 'start': 2190.168, 'duration': 5.184}, {'end': 2204.88, 'text': 'The other thing that a machine can learn is to look at data and detect patterns inside it and insights.', 'start': 2196.673, 'duration': 8.207}, {'end': 2207.942, 'text': 'So that is the other kind of learning we want to have.', 'start': 2206.321, 'duration': 1.621}, {'end': 2209.343, 'text': "Let's say there is no historical data.", 'start': 2208.022, 'duration': 1.321}, {'end': 2210.304, 'text': 'There is just one data set.', 'start': 2209.363, 'duration': 0.941}, {'end': 2224.017, 'text': 'There is just a data set of, let us say, all the locations where there is a likelihood.', 'start': 2211.53, 'duration': 12.487}], 'summary': 'Machines can learn to make predictions and detect patterns from data, even with limited historical data available.', 'duration': 33.849, 'max_score': 2190.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY02190168.jpg'}], 'start': 1558.781, 'title': 'Machine learning applications and fundamentals', 'summary': 'Discusses applications of machine learning, including fraud detection, medical diagnosis, and self-driving cars, emphasizing the potential impact and the importance of learning machine learning skills. it also explores the fundamentals of machine learning, focusing on its core strengths in numbers and mathematical computations, learning from data, and making predictions and detecting patterns.', 'chapters': [{'end': 1788.871, 'start': 1558.781, 'title': 'Applications of machine learning', 'summary': "Discusses critical applications of machine learning such as fraud detection on credit cards, medical diagnosis using image data, and self-driving cars, highlighting the potential to 'wow' users and the importance of learning machine learning skills.", 'duration': 230.09, 'highlights': ['Machine learning is used for critical tasks like fraud detection on credit cards, where it can tag a suspicious transaction and enable further authentication mechanisms, reducing the need for manual monitoring and enhancing security.', 'In medical diagnosis, machine learning utilizes image data to learn from a large number of scans, enabling the prediction of conditions like tumors with reduced errors and greater objectivity, showcasing the impact of machine learning in healthcare.', 'Self-driving cars rely on machine learning to interpret the world, identifying elements like stop signs, pedestrians, speed breakers, and weather conditions, illustrating the complexity of the problem and the potential future impact of machine learning in transportation.', "Machine learning is a core differentiating factor for many products today, enabling services like Google Maps and Netflix to 'wow' users with personalized recommendations, emphasizing the value of machine learning-enabled software in enhancing user experience across various domains."]}, {'end': 2237.945, 'start': 1789.792, 'title': 'Machine learning fundamentals', 'summary': 'Explores the core strengths of machines in numbers and mathematical computations, their ability to learn from data represented as numbers, and their role in making predictions and detecting patterns.', 'duration': 448.153, 'highlights': ['Machines are extremely good with numbers and mathematical computation, capable of remembering past events or outcomes, and learning from data represented as numbers and making predictions.', 'The core strength of machines lies in their proficiency with numbers, mathematical computations, and remembering past events or outcomes, which is essential for learning from data and making predictions.', 'Machines learn from data represented as past events and their outcomes, particularly represented as numbers, to make predictions and detect patterns, which is vital for their learning process.', 'Machines can learn from past outcomes to predict future outcomes and from their mistakes to make better predictions in the future, emphasizing the importance of learning from data and making predictions in machine learning.']}], 'duration': 679.164, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY01558781.jpg', 'highlights': ["Machine learning is a core differentiating factor for many products today, enabling services like Google Maps and Netflix to 'wow' users with personalized recommendations, emphasizing the value of machine learning-enabled software in enhancing user experience across various domains.", 'In medical diagnosis, machine learning utilizes image data to learn from a large number of scans, enabling the prediction of conditions like tumors with reduced errors and greater objectivity, showcasing the impact of machine learning in healthcare.', 'Self-driving cars rely on machine learning to interpret the world, identifying elements like stop signs, pedestrians, speed breakers, and weather conditions, illustrating the complexity of the problem and the potential future impact of machine learning in transportation.', 'Machine learning is used for critical tasks like fraud detection on credit cards, where it can tag a suspicious transaction and enable further authentication mechanisms, reducing the need for manual monitoring and enhancing security.', 'Machines are extremely good with numbers and mathematical computation, capable of remembering past events or outcomes, and learning from data represented as numbers and making predictions.', 'The core strength of machines lies in their proficiency with numbers, mathematical computations, and remembering past events or outcomes, which is essential for learning from data and making predictions.', 'Machines learn from data represented as past events and their outcomes, particularly represented as numbers, to make predictions and detect patterns, which is vital for their learning process.', 'Machines can learn from past outcomes to predict future outcomes and from their mistakes to make better predictions in the future, emphasizing the importance of learning from data and making predictions in machine learning.']}, {'end': 2663.916, 'segs': [{'end': 2267.139, 'src': 'embed', 'start': 2239.682, 'weight': 0, 'content': [{'end': 2248.107, 'text': 'So these are the two major kinds of learnings we want the machine to have to learn from data that has happened in the past and to predict future outcomes.', 'start': 2239.682, 'duration': 8.425}, {'end': 2253.13, 'text': 'And the next is look at a data set in itself and to detect patterns in it.', 'start': 2249.908, 'duration': 3.222}, {'end': 2258.073, 'text': 'So these are the things that we want a machine to learn.', 'start': 2255.812, 'duration': 2.261}, {'end': 2261.435, 'text': 'So we have answered so far what are machines good at.', 'start': 2259.254, 'duration': 2.181}, {'end': 2267.139, 'text': 'Then we answered what can a machine learn from.', 'start': 2263.477, 'duration': 3.662}], 'summary': 'Machine learning aims to learn from past data and predict future outcomes, as well as detect patterns in datasets.', 'duration': 27.457, 'max_score': 2239.682, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY02239682.jpg'}, {'end': 2320.054, 'src': 'embed', 'start': 2292.589, 'weight': 2, 'content': [{'end': 2298.893, 'text': 'One thing a machine can do, given it is awesome with numbers and mathematics, is to find a mathematical function.', 'start': 2292.589, 'duration': 6.304}, {'end': 2305.848, 'text': 'which can generate the output as close as possible to the actual outputs.', 'start': 2300.046, 'duration': 5.802}, {'end': 2308.269, 'text': 'So you have these input-output pairs.', 'start': 2306.849, 'duration': 1.42}, {'end': 2313.571, 'text': "Let's take the case of the Sensex index.", 'start': 2308.369, 'duration': 5.202}, {'end': 2317.573, 'text': 'So let us say on first April, Sensex was at 30,000.', 'start': 2314.332, 'duration': 3.241}, {'end': 2319.454, 'text': "So that's an input-output pair.", 'start': 2317.573, 'duration': 1.881}, {'end': 2320.054, 'text': 'First April, 30,000.', 'start': 2319.474, 'duration': 0.58}], 'summary': 'Machines can find mathematical functions to generate outputs close to actual outputs using input-output pairs, such as modeling the sensex index at 30,000 on april 1st.', 'duration': 27.465, 'max_score': 2292.589, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY02292589.jpg'}, {'end': 2470.346, 'src': 'embed', 'start': 2448.321, 'weight': 1, 'content': [{'end': 2458.723, 'text': 'so such form of you know machine learning where the, where the machine comes up with a function right with which it can gen, it can predict values.', 'start': 2448.321, 'duration': 10.402}, {'end': 2463.384, 'text': 'and it came up with the function looking at all the data you gave, the input output pairs that you gave.', 'start': 2458.723, 'duration': 4.661}, {'end': 2466.265, 'text': 'so such form of machine learning is called regression analysis.', 'start': 2463.384, 'duration': 2.881}, {'end': 2470.346, 'text': 'so this is one type of machine learning, a suit of machine learning algorithms,', 'start': 2466.265, 'duration': 4.081}], 'summary': 'Regression analysis is a type of machine learning that predicts values based on input-output pairs.', 'duration': 22.025, 'max_score': 2448.321, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY02448321.jpg'}, {'end': 2653.268, 'src': 'embed', 'start': 2625.616, 'weight': 3, 'content': [{'end': 2630.378, 'text': 'So it has this input, these three values for each day, and whether you went out to play that day or not.', 'start': 2625.616, 'duration': 4.762}, {'end': 2637.042, 'text': 'Given all of this, the machine can actually learn this, can come up with this flowchart.', 'start': 2632.66, 'duration': 4.382}, {'end': 2641.358, 'text': 'And then any day when it is, you know, it can look.', 'start': 2638.396, 'duration': 2.962}, {'end': 2644.761, 'text': "Okay, what is the humidity? What is the outlook today? It's sunny.", 'start': 2642.339, 'duration': 2.422}, {'end': 2647.083, 'text': 'What is the humidity? Looks like normal.', 'start': 2645.422, 'duration': 1.661}, {'end': 2648.564, 'text': 'So it can suggest you to go out and play.', 'start': 2647.183, 'duration': 1.381}, {'end': 2650.405, 'text': "Hey, you haven't played for four days.", 'start': 2649.365, 'duration': 1.04}, {'end': 2653.268, 'text': "Why don't you go out and play? This is the normal time in which you go out and play.", 'start': 2650.425, 'duration': 2.843}], 'summary': 'Machine learning model predicts outdoor play based on weather data.', 'duration': 27.652, 'max_score': 2625.616, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY02625616.jpg'}], 'start': 2239.682, 'title': 'Machine learning fundamentals and methods', 'summary': 'Covers the significance of machine learning in predicting future outcomes from past data, and the application of regression analysis to generate outputs based on input parameters for decision making, illustrated with the example of sensex index data.', 'chapters': [{'end': 2349.259, 'start': 2239.682, 'title': 'Machine learning fundamentals', 'summary': 'Explains the importance of machine learning in predicting future outcomes from past data, and the process of finding mathematical functions to generate outputs based on input-output pairs, using the example of sensex index data.', 'duration': 109.577, 'highlights': ['Machines are trained to learn from past data and predict future outcomes, as well as to detect patterns within a given dataset, emphasizing the core objectives of machine learning.', 'Machines can learn from historical data represented as input-output pairs by finding mathematical functions that generate outputs close to the actual outputs, demonstrated using the example of Sensex index data on specific dates.', 'The example of Sensex index data on specific dates is used to illustrate the process of finding mathematical functions to generate outputs based on input-output pairs, showcasing the practical application of machine learning in financial data analysis.']}, {'end': 2663.916, 'start': 2349.259, 'title': 'Machine learning methods', 'summary': 'Explains the concept of machine learning, focusing on regression analysis to predict outputs and generating flow charts based on input parameters for decision making.', 'duration': 314.657, 'highlights': ['Machine learning involves regression analysis to predict outputs based on input data points. The machine learns to find a mathematical function that generates the output as close as possible to the actual outputs, enabling it to make predictions for new values.', 'Another form of machine learning is generating a flow chart based on input parameters for decision making. The machine can analyze input parameters like outlook, humidity, and wind to generate a flow chart for decision making, such as whether to go out and play based on weather conditions.']}], 'duration': 424.234, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY02239682.jpg', 'highlights': ['Machines are trained to learn from past data and predict future outcomes, emphasizing the core objectives of machine learning.', 'Machine learning involves regression analysis to predict outputs based on input data points.', 'The example of Sensex index data on specific dates is used to illustrate the process of finding mathematical functions to generate outputs based on input-output pairs, showcasing the practical application of machine learning in financial data analysis.', 'The machine can analyze input parameters like outlook, humidity, and wind to generate a flow chart for decision making, such as whether to go out and play based on weather conditions.', 'Machines can learn from historical data represented as input-output pairs by finding mathematical functions that generate outputs close to the actual outputs, demonstrated using the example of Sensex index data on specific dates.']}, {'end': 3704.306, 'segs': [{'end': 2738.288, 'src': 'embed', 'start': 2708.483, 'weight': 0, 'content': [{'end': 2709.563, 'text': "It's called decision trees.", 'start': 2708.483, 'duration': 1.08}, {'end': 2712.724, 'text': 'Right So we have looked at regression analysis.', 'start': 2710.583, 'duration': 2.141}, {'end': 2714.445, 'text': 'We have looked at decision trees.', 'start': 2713.265, 'duration': 1.18}, {'end': 2727.281, 'text': 'OK So now if you remember this slide, let me go back to it.', 'start': 2716.566, 'duration': 10.715}, {'end': 2731.504, 'text': 'So we want the machine to learn from past outcomes to predict future outcomes.', 'start': 2728.482, 'duration': 3.022}, {'end': 2732.705, 'text': 'We have done that.', 'start': 2732.104, 'duration': 0.601}, {'end': 2734.526, 'text': 'We can do that with regression analysis.', 'start': 2732.945, 'duration': 1.581}, {'end': 2738.288, 'text': 'We can do that with decision trees and many, many more algorithms.', 'start': 2734.586, 'duration': 3.702}], 'summary': 'Using regression and decision trees, machines learn from past outcomes to predict future outcomes.', 'duration': 29.805, 'max_score': 2708.483, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY02708483.jpg'}, {'end': 2897.352, 'src': 'embed', 'start': 2869.246, 'weight': 1, 'content': [{'end': 2873.927, 'text': 'into different clusters so that you can do something meaningful with it.', 'start': 2869.246, 'duration': 4.681}, {'end': 2880.228, 'text': 'So this type of machine learning, where the machine learns from data to create insights or patterns in the data,', 'start': 2874.087, 'duration': 6.141}, {'end': 2884.309, 'text': 'to identify patterns or derive insights from data.', 'start': 2880.228, 'duration': 4.081}, {'end': 2890.531, 'text': 'one such learning is cluster analysis, where you break the machines, will take your data and will break it into as many clusters as you said.', 'start': 2884.309, 'duration': 6.222}, {'end': 2892.251, 'text': 'but all those clusters are supposed to be meaningful.', 'start': 2890.531, 'duration': 1.72}, {'end': 2897.352, 'text': "They're actually closer to each other, so they have some correlation, something similar.", 'start': 2892.291, 'duration': 5.061}], 'summary': 'Cluster analysis breaks data into meaningful clusters based on similarity.', 'duration': 28.106, 'max_score': 2869.246, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY02869246.jpg'}, {'end': 3076.99, 'src': 'embed', 'start': 3049.519, 'weight': 2, 'content': [{'end': 3052.321, 'text': 'So normally 70% of the data is given to the machine to learn.', 'start': 3049.519, 'duration': 2.802}, {'end': 3053.861, 'text': '30% is withheld,', 'start': 3052.321, 'duration': 1.54}, {'end': 3064.586, 'text': "with which we test the machine's learning and we keep doing it till we actually get a model or till we actually get the machine to learn accurately,", 'start': 3053.861, 'duration': 10.725}, {'end': 3065.867, 'text': 'give good accuracy on the test.', 'start': 3064.586, 'duration': 1.281}, {'end': 3068.868, 'text': 'so this is called a train test cycle.', 'start': 3066.727, 'duration': 2.141}, {'end': 3076.99, 'text': 'so we keep doing it, we train it again and again and again till we, you know, have given it good data, good representational data, and then,', 'start': 3068.868, 'duration': 8.122}], 'summary': '70% of data is used for machine learning, 30% for testing, creating accurate model through train test cycle.', 'duration': 27.471, 'max_score': 3049.519, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY03049519.jpg'}, {'end': 3335.551, 'src': 'embed', 'start': 3310.375, 'weight': 3, 'content': [{'end': 3317.101, 'text': 'So whenever a human being is involved in helping the machine learn, it is called supervised learning,', 'start': 3310.375, 'duration': 6.726}, {'end': 3325.387, 'text': 'where we teach the machine with input-output pairs so that it can generate outputs for new inputs, where we, as in human beings, right?', 'start': 3317.101, 'duration': 8.286}, {'end': 3327.369, 'text': 'So that is called supervised learning.', 'start': 3326.008, 'duration': 1.361}, {'end': 3329.05, 'text': 'So the machine is learning under supervision.', 'start': 3327.389, 'duration': 1.661}, {'end': 3335.551, 'text': 'So regression analysis, there, again, we are telling it take the 70% data and learn from it.', 'start': 3330.23, 'duration': 5.321}], 'summary': 'Supervised learning involves teaching machines with input-output pairs to generate outputs for new inputs. in regression analysis, 70% of the data is used for learning.', 'duration': 25.176, 'max_score': 3310.375, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY03310375.jpg'}, {'end': 3452.784, 'src': 'embed', 'start': 3428.177, 'weight': 4, 'content': [{'end': 3439.1, 'text': "Let's actually build a couple of these machine learning models in Python, which is my preferred language for doing machine learning, and play around.", 'start': 3428.177, 'duration': 10.923}, {'end': 3447.502, 'text': "So the two hands-on exercises we're gonna do is to build a machine learning model which can predict weight of people based on their height.", 'start': 3439.2, 'duration': 8.302}, {'end': 3452.784, 'text': 'So we have a data set where, for different people, we have their weights and heights,', 'start': 3448.86, 'duration': 3.924}], 'summary': 'Build machine learning models in python to predict weight based on height.', 'duration': 24.607, 'max_score': 3428.177, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY03428177.jpg'}], 'start': 2665.072, 'title': 'Machine learning fundamentals', 'summary': 'Introduces decision trees in machine learning, discusses cluster analysis, and provides an overview of machine learning fundamentals, including a 70/30 training/testing split and hands-on exercises in python.', 'chapters': [{'end': 2738.288, 'start': 2665.072, 'title': 'Machine learning: decision trees', 'summary': 'Introduces decision trees as a form of machine learning algorithm that allows machines to learn from past data and create flow charts to make decisions, without the need for explicit coding, and it can be used to predict future outcomes.', 'duration': 73.216, 'highlights': ['The chapter introduces decision trees as a form of machine learning algorithm that allows machines to learn from past data and create flow charts to make decisions, without the need for explicit coding, and it can be used to predict future outcomes.', 'Decision trees are a form of machine learning algorithm where the machine tries to come up with a flow chart based on which it can make decisions, such as yes or no decisions.', 'Regression analysis and decision trees are highlighted as methods for the machine to learn from past outcomes in order to predict future outcomes.']}, {'end': 2971.1, 'start': 2739.729, 'title': 'Types of machine learning: cluster analysis', 'summary': "Discusses the concept of cluster analysis, a type of machine learning where the machine learns from data to create insights or patterns in the data, to identify patterns or derive insights from data. it explains how the machine can segregate data into clusters based on given parameters and highlights the importance of evaluating a machine's learning through testing.", 'duration': 231.371, 'highlights': ['The machine can segregate data into clusters based on given parameters, such as splitting the data into a specific number of buckets, and it tries to put closer things together, creating meaningful clusters. (e.g., segregating users based on their behavior and purchases for targeted marketing)', 'Cluster analysis is a type of machine learning where the machine learns from data to create insights or patterns in the data, to identify patterns or derive insights from data. It focuses on segmenting data into meaningful clusters that have correlation and similarity. (e.g., breaking data into clusters based on user behavior for targeted marketing)', "The chapter emphasizes the importance of evaluating a machine's learning through testing, similar to how humans evaluate their learning through tests, to determine whether the machine has learned well or poorly. (e.g., using tests to evaluate the effectiveness of the machine's learning)"]}, {'end': 3704.306, 'start': 2971.921, 'title': 'Machine learning fundamentals and terminology', 'summary': 'Provides an overview of machine learning, explaining the process of training and testing a machine learning model using a 70/30 split, introduces basic machine learning terminology, and distinguishes between supervised and unsupervised learning. it also presents hands-on exercises to build machine learning models in python.', 'duration': 732.385, 'highlights': ['Process of Training and Testing a Machine Learning Model The process of training and testing a machine learning model involves giving 70% of the data to the machine for learning and using the remaining 30% for testing, iterating the train-test cycle to improve accuracy.', 'Basic Machine Learning Terminology Introduction of machine learning terminology including machine learning model, features, dependent variable, training data set, test data set, precision, and recall.', 'Supervised and Unsupervised Learning Distinguishing between supervised learning, where the machine learns with human supervision using input-output pairs, and unsupervised learning, where the machine derives insights from data without human intervention.', 'Hands-on Exercises in Python The presentation includes hands-on exercises to build machine learning models in Python, focusing on predicting weight based on height and shortlisting resumes using historical data.']}], 'duration': 1039.234, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY02665072.jpg', 'highlights': ['Decision trees enable machines to learn from past data and make decisions without explicit coding.', 'Cluster analysis helps machines identify patterns and create meaningful clusters from data.', 'The process of training and testing a machine learning model involves a 70/30 data split for learning and testing.', 'Supervised learning involves human supervision using input-output pairs, while unsupervised learning derives insights without human intervention.', 'The chapter includes hands-on exercises in Python for building machine learning models.']}, {'end': 4483.603, 'segs': [{'end': 3933.875, 'src': 'embed', 'start': 3905.273, 'weight': 0, 'content': [{'end': 3910.495, 'text': "So I'm taking this data frame and I'm creating a new data frame with just these two columns, height and weight.", 'start': 3905.273, 'duration': 5.222}, {'end': 3912.116, 'text': "That's what I'm doing here.", 'start': 3911.296, 'duration': 0.82}, {'end': 3917.388, 'text': 'Awesome So now we have data in a way that we can understand.', 'start': 3914.127, 'duration': 3.261}, {'end': 3924.211, 'text': "So first learning, most likely the data you get won't be in the way or in the format that you want.", 'start': 3918.389, 'duration': 5.822}, {'end': 3927.172, 'text': 'And with Pandas you can easily do this conversion right?', 'start': 3925.111, 'duration': 2.061}, {'end': 3933.875, 'text': 'So with what one, two, three, four lines of code we could actually get data to a state in which we can work with.', 'start': 3927.892, 'duration': 5.983}], 'summary': 'Using pandas, a new data frame was created with height and weight columns to understand and work with the data.', 'duration': 28.602, 'max_score': 3905.273, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY03905273.jpg'}, {'end': 4015.976, 'src': 'embed', 'start': 3982.804, 'weight': 1, 'content': [{'end': 3984.085, 'text': "You'll be looking at how the data looks.", 'start': 3982.804, 'duration': 1.281}, {'end': 3988.409, 'text': "What is the variations with height and weight? So you'll use some form of visualization.", 'start': 3984.625, 'duration': 3.784}, {'end': 3991.111, 'text': 'So I want to introduce you to Matplotlib.', 'start': 3989.189, 'duration': 1.922}, {'end': 3996.742, 'text': 'which is another package in python which comes pre-installed with anaconda, so it will already be there.', 'start': 3991.678, 'duration': 5.064}, {'end': 4005.568, 'text': "so if you go to environments and we search for, you know matplotlib it's already installed.", 'start': 3996.742, 'duration': 8.826}, {'end': 4008.05, 'text': "if it's not, you can just check this and install it.", 'start': 4005.568, 'duration': 2.482}, {'end': 4008.41, 'text': 'but it is.', 'start': 4008.05, 'duration': 0.36}, {'end': 4009.651, 'text': 'that is the beauty of anaconda.', 'start': 4008.41, 'duration': 1.241}, {'end': 4011.032, 'text': "it is a, it's a distribution.", 'start': 4009.651, 'duration': 1.381}, {'end': 4015.976, 'text': 'it comes with all the bells and whistles, all the packages that you would normally need.', 'start': 4011.032, 'duration': 4.944}], 'summary': 'Introduction to using matplotlib for data visualization in anaconda.', 'duration': 33.172, 'max_score': 3982.804, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY03982804.jpg'}, {'end': 4100.331, 'src': 'embed', 'start': 4071.837, 'weight': 2, 'content': [{'end': 4074.198, 'text': 'People of same height does not mean they will have similar weights.', 'start': 4071.837, 'duration': 2.361}, {'end': 4075.879, 'text': 'It depends on a lot of things, right?', 'start': 4074.338, 'duration': 1.541}, {'end': 4086.167, 'text': 'now let us use try to build a model to predict the weight of people given their height right,', 'start': 4077.845, 'duration': 8.322}, {'end': 4092.369, 'text': 'and what i want to do is to build a linear regression model, which is essentially something like you know.', 'start': 4086.167, 'duration': 6.202}, {'end': 4094.97, 'text': 'let me show you like this.', 'start': 4092.369, 'duration': 2.601}, {'end': 4096.43, 'text': 'so this is a scatter plot we have done.', 'start': 4094.97, 'duration': 1.46}, {'end': 4100.331, 'text': 'i wanted to learn a straight line linear regression.', 'start': 4096.43, 'duration': 3.901}], 'summary': 'Building a linear regression model to predict weight from height.', 'duration': 28.494, 'max_score': 4071.837, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY04071837.jpg'}, {'end': 4394.55, 'src': 'embed', 'start': 4371.451, 'weight': 4, 'content': [{'end': 4379.538, 'text': 'So for that it also gives us something called R value, which is a measure of how much you know well the machine has learned.', 'start': 4371.451, 'duration': 8.087}, {'end': 4385.362, 'text': 'So R squared, once you print, if the value is closer to one, it is a good learning.', 'start': 4380.819, 'duration': 4.543}, {'end': 4387.684, 'text': "If its value is closer to zero, it's not learned very well.", 'start': 4385.402, 'duration': 2.282}, {'end': 4388.985, 'text': "Let's run this.", 'start': 4388.405, 'duration': 0.58}, {'end': 4390.727, 'text': 'So its value is actually 0.25.', 'start': 4389.606, 'duration': 1.121}, {'end': 4392.208, 'text': 'So it is closer to zero.', 'start': 4390.727, 'duration': 1.481}, {'end': 4393.309, 'text': "So it hasn't learned.", 'start': 4392.568, 'duration': 0.741}, {'end': 4394.55, 'text': "It's not a very accurate model.", 'start': 4393.329, 'duration': 1.221}], 'summary': 'The r squared value of 0.25 indicates poor machine learning accuracy.', 'duration': 23.099, 'max_score': 4371.451, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY04371451.jpg'}, {'end': 4468.338, 'src': 'embed', 'start': 4442.375, 'weight': 3, 'content': [{'end': 4449.418, 'text': 'if I have to build a model which has to predict the weight of the person, given what all are the things that it needs to look at?', 'start': 4442.375, 'duration': 7.043}, {'end': 4451.659, 'text': 'Height alone is not enough, right?', 'start': 4449.778, 'duration': 1.881}, {'end': 4454.12, 'text': 'There must be other factors.', 'start': 4452.779, 'duration': 1.341}, {'end': 4457.121, 'text': "maybe gender is a factor, whether it's a male or a female.", 'start': 4454.12, 'duration': 3.001}, {'end': 4464.877, 'text': 'maybe you know the country matters, because in india maybe they eat, you know, different kinds of food.', 'start': 4458.234, 'duration': 6.643}, {'end': 4468.338, 'text': 'maybe they look, maybe in iceland they eat different kind of food.', 'start': 4464.877, 'duration': 3.461}], 'summary': 'Model factors for predicting weight include height, gender, and country-specific diet.', 'duration': 25.963, 'max_score': 4442.375, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY04442375.jpg'}], 'start': 3704.306, 'title': 'Data preprocessing and linear regression model', 'summary': "Covers data preprocessing using pandas with a dataset containing 25,000 records and introduces visualization with matplotlib. additionally, it discusses building a linear regression model to predict weight based on height using a dataset of 25,000 points, resulting in a slope of 16.78 and an intercept of -37.45. however, the model's r value of 0.25 indicates poor learning, suggesting the need to consider additional factors for accurate weight prediction.", 'chapters': [{'end': 4027.517, 'start': 3704.306, 'title': 'Data preprocessing with pandas and visualization with matplotlib', 'summary': 'Covers data preprocessing using pandas, including data format conversion and cleaning, with a dataset containing 25,000 records, followed by an introduction to visualization using matplotlib for exploratory data analytics.', 'duration': 323.211, 'highlights': ['The dataset contains 25,000 records, and with Pandas, the speaker demonstrates data format conversion and cleaning, including converting height from inches to feet and weight from pounds to kilograms, making the data more relatable and understandable. The dataset contains 25,000 records; Pandas is used to convert height from inches to feet and weight from pounds to kilograms, making the data more relatable and understandable.', 'The speaker emphasizes the importance of data format conversion and cleaning, illustrating how with just a few lines of code in Pandas, the data can be transformed into a workable format. The speaker emphasizes the importance of data format conversion and cleaning, illustrating how with just a few lines of code in Pandas, the data can be transformed into a workable format.', 'An introduction to visualization using Matplotlib for exploratory data analytics is provided, with the speaker explaining its pre-installed availability in Anaconda, highlighting its usefulness for data analysis. An introduction to visualization using Matplotlib for exploratory data analytics is provided, with the speaker explaining its pre-installed availability in Anaconda, highlighting its usefulness for data analysis.']}, {'end': 4483.603, 'start': 4028.318, 'title': 'Linear regression model for weight prediction', 'summary': "Discusses building a linear regression model to predict weight based on height using a data set of 25,000 points, obtaining a slope of 16.78 and an intercept of -37.45. however, the model's r value of 0.25 indicates poor learning, suggesting the need to consider additional factors for accurate weight prediction.", 'duration': 455.285, 'highlights': ['The chapter discusses building a linear regression model to predict weight based on height using a data set of 25,000 points, obtaining a slope of 16.78 and an intercept of -37.45.', "The model's R value of 0.25 indicates poor learning, suggesting the need to consider additional factors for accurate weight prediction.", 'The need to consider additional factors such as gender, country, lifestyle, and occupation for accurate weight prediction is highlighted, emphasizing that height alone is not sufficient for accurate predictions.']}], 'duration': 779.297, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY03704306.jpg', 'highlights': ['The dataset contains 25,000 records; Pandas is used to convert height from inches to feet and weight from pounds to kilograms, making the data more relatable and understandable.', 'An introduction to visualization using Matplotlib for exploratory data analytics is provided, with the speaker explaining its pre-installed availability in Anaconda, highlighting its usefulness for data analysis.', 'The chapter discusses building a linear regression model to predict weight based on height using a data set of 25,000 points, obtaining a slope of 16.78 and an intercept of -37.45.', 'The need to consider additional factors such as gender, country, lifestyle, and occupation for accurate weight prediction is highlighted, emphasizing that height alone is not sufficient for accurate predictions.', "The model's R value of 0.25 indicates poor learning, suggesting the need to consider additional factors for accurate weight prediction.", 'The speaker emphasizes the importance of data format conversion and cleaning, illustrating how with just a few lines of code in Pandas, the data can be transformed into a workable format.']}, {'end': 5254.024, 'segs': [{'end': 4532.813, 'src': 'embed', 'start': 4506.12, 'weight': 2, 'content': [{'end': 4511.902, 'text': 'And then, if it looks at all those attributes not just the height, all the other attributes that we spoke about and we build a model,', 'start': 4506.12, 'duration': 5.782}, {'end': 4512.902, 'text': "it'll be much better.", 'start': 4511.902, 'duration': 1}, {'end': 4519.401, 'text': 'Right It will have a much better learning because it is now looking at all those things and then learning.', 'start': 4514.163, 'duration': 5.238}, {'end': 4523.144, 'text': 'So the problem with what we did.', 'start': 4520.963, 'duration': 2.181}, {'end': 4525.607, 'text': 'here, though, we have learned how to do linear regression.', 'start': 4523.144, 'duration': 2.463}, {'end': 4530.31, 'text': 'we passed only the height and we said the weight is primarily dependent on the height, which is not the case.', 'start': 4525.607, 'duration': 4.703}, {'end': 4532.813, 'text': 'The weight is dependent on a lot of other things.', 'start': 4530.991, 'duration': 1.822}], 'summary': 'Building a model considering all attributes yields better learning results. weight is not solely dependent on height.', 'duration': 26.693, 'max_score': 4506.12, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY04506120.jpg'}, {'end': 4597.846, 'src': 'embed', 'start': 4567.774, 'weight': 1, 'content': [{'end': 4570.795, 'text': 'And the decision we are going to take is whether to shortlist a resume or not.', 'start': 4567.774, 'duration': 3.021}, {'end': 4572.736, 'text': "So now let's look at our data set.", 'start': 4571.576, 'duration': 1.16}, {'end': 4575.7, 'text': "Let's do this a little quickly in the interest of time.", 'start': 4573.82, 'duration': 1.88}, {'end': 4587.063, 'text': 'Yes So this is our data set.', 'start': 4577.381, 'duration': 9.682}, {'end': 4587.824, 'text': 'This is handcrafted.', 'start': 4587.123, 'duration': 0.701}, {'end': 4589.964, 'text': 'This is something I wrote myself.', 'start': 4587.884, 'duration': 2.08}, {'end': 4591.284, 'text': "So it's not a big data set.", 'start': 4590.424, 'duration': 0.86}, {'end': 4597.846, 'text': "It's not a real data set, but we can learn, see how to do decision trees using it.", 'start': 4591.304, 'duration': 6.542}], 'summary': 'Deciding on shortlisting resumes based on a small handcrafted dataset for decision tree modeling.', 'duration': 30.072, 'max_score': 4567.774, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY04567774.jpg'}, {'end': 4947.243, 'src': 'embed', 'start': 4917.786, 'weight': 0, 'content': [{'end': 4923.669, 'text': 'But just showing the machine this data, asking it to build a decision tree, it has come up with the decision tree.', 'start': 4917.786, 'duration': 5.883}, {'end': 4930.199, 'text': 'which we are able to call the different inputs of grade and experience and see what it predicts.', 'start': 4924.457, 'duration': 5.742}, {'end': 4933.119, 'text': 'All of that in this much code.', 'start': 4931.719, 'duration': 1.4}, {'end': 4934.96, 'text': 'So simple.', 'start': 4934.44, 'duration': 0.52}, {'end': 4941.461, 'text': 'Okay So those are all the things that I wanted to cover.', 'start': 4937.6, 'duration': 3.861}, {'end': 4947.243, 'text': 'Now maybe I can spend the next 10 minutes answering questions.', 'start': 4943.802, 'duration': 3.441}], 'summary': 'Machine builds decision tree from data in simple code.', 'duration': 29.457, 'max_score': 4917.786, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY04917786.jpg'}, {'end': 5037.235, 'src': 'embed', 'start': 5012.941, 'weight': 3, 'content': [{'end': 5020.406, 'text': 'and the other type of ai is essentially deep learning or neural network based learning, where there are,', 'start': 5012.941, 'duration': 7.465}, {'end': 5029.791, 'text': 'where the study is primarily around how neural networks which mimic how which is a mimic attempt at mimicking how our brain works can be used,', 'start': 5020.406, 'duration': 9.385}, {'end': 5033.453, 'text': 'train the machine right.', 'start': 5031.371, 'duration': 2.082}, {'end': 5037.235, 'text': 'so machine learning is actually a subset of artificial intelligence.', 'start': 5033.453, 'duration': 3.782}], 'summary': 'Ai encompasses deep learning and neural networks, with machine learning being a subset.', 'duration': 24.294, 'max_score': 5012.941, 'thumbnail': ''}], 'start': 4483.603, 'title': 'Machine learning and decision trees', 'summary': 'Emphasizes the importance of a holistic approach to machine learning, highlighting the limitations of linear regression. it also covers the use of decision trees for resume shortlisting, including regression analysis and the creation of a decision tree based on grade and work experience.', 'chapters': [{'end': 4540.339, 'start': 4483.603, 'title': 'Holistic approach to machine learning', 'summary': 'Emphasizes the importance of providing a holistic picture with all relevant attributes for machine learning to predict accurately, highlighting the limitations of linear regression using only height as an input.', 'duration': 56.736, 'highlights': ['The importance of providing a holistic picture with all relevant attributes for machine learning to predict accurately, emphasizing the limitations of linear regression using only height as an input.', 'The weight is dependent on a lot of other things, not just height, which leads to the machine not learning well with linear regression.', "The machine's learning will be much better when it looks at all attributes and then learns, as opposed to just focusing on one attribute like height.", 'Emphasizing the need to give the machine all the attributes which matter and then give it the weight of the person for better learning and prediction.']}, {'end': 5254.024, 'start': 4542.661, 'title': 'Resume shortlisting with decision trees', 'summary': "Covers the use of decision trees for resume shortlisting, including the use of regression analysis and the creation of a decision tree based on grade and work experience, demonstrating the machine's ability to build the tree and predict shortlisting outcomes.", 'duration': 711.363, 'highlights': ['The machine learned a decision tree based on grade and work experience, accurately predicting shortlisting outcomes with a simple code implementation. The machine learned a decision tree based on grade and work experience, accurately predicting shortlisting outcomes with a simple code implementation.', 'The data set used for decision tree creation included historical shortlisting data based on grade and work experience. The data set used for decision tree creation included historical shortlisting data based on grade and work experience.', 'The chapter also explained the distinction between artificial intelligence, machine learning, and deep learning, clarifying that machine learning is a subset of AI. The chapter also explained the distinction between artificial intelligence, machine learning, and deep learning, clarifying that machine learning is a subset of AI.', 'The session concluded with encouragement for continued learning in machine learning and AI. The session concluded with encouragement for continued learning in machine learning and AI.']}], 'duration': 770.421, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/_t5AOMyoeY0/pics/_t5AOMyoeY04483603.jpg', 'highlights': ['The machine learned a decision tree based on grade and work experience, accurately predicting shortlisting outcomes with a simple code implementation.', 'The data set used for decision tree creation included historical shortlisting data based on grade and work experience.', 'Emphasizes the importance of providing a holistic picture with all relevant attributes for machine learning to predict accurately, emphasizing the limitations of linear regression using only height as an input.', 'The chapter also explained the distinction between artificial intelligence, machine learning, and deep learning, clarifying that machine learning is a subset of AI.']}], 'highlights': ['Machine learning reduces errors and improves efficiency, enabling machines to perform repetitive tasks without errors.', 'Machine learning is needed to handle the explosion of data, as human beings cannot catch up with the data explosion.', 'Google Assistant uses machine learning to learn user preferences, appointments, and travel patterns, providing personalized assistance based on data signals and user input.', 'Google Maps employs machine learning to optimize routes based on real-time traffic data and user behavior, continuously improving suggestions for efficient navigation.', 'Netflix utilizes machine learning for personalized recommendations, analyzing user and similar user preferences to enhance content discovery and user engagement.', 'Online Advertising leverages user data to deliver relevant and meaningful ads, based on past activities and current interests, improving ad targeting and user experience.', 'Credit Card Fraud Prevention Systems use machine learning to detect irregularities in credit card usage, learning from past spending behavior to identify deviations and introduce further authentication measures to prevent fraud.', 'In medical diagnosis, machine learning utilizes image data to learn from a large number of scans, enabling the prediction of conditions like tumors with reduced errors and greater objectivity, showcasing the impact of machine learning in healthcare.', 'Self-driving cars rely on machine learning to interpret the world, identifying elements like stop signs, pedestrians, speed breakers, and weather conditions, illustrating the complexity of the problem and the potential future impact of machine learning in transportation.', "Machine learning is a core differentiating factor for many products today, enabling services like Google Maps and Netflix to 'wow' users with personalized recommendations, emphasizing the value of machine learning-enabled software in enhancing user experience across various domains.", 'Machines learn from data represented as past events and their outcomes, particularly represented as numbers, to make predictions and detect patterns, which is vital for their learning process.', 'Machines can learn from past outcomes to predict future outcomes and from their mistakes to make better predictions in the future, emphasizing the importance of learning from data and making predictions in machine learning.', 'Machines are trained to learn from past data and predict future outcomes, emphasizing the core objectives of machine learning.', 'Machine learning involves regression analysis to predict outputs based on input data points.', 'Decision trees enable machines to learn from past data and make decisions without explicit coding.', 'Cluster analysis helps machines identify patterns and create meaningful clusters from data.', 'Supervised learning involves human supervision using input-output pairs, while unsupervised learning derives insights without human intervention.', 'The chapter includes hands-on exercises in Python for building machine learning models.', 'Pandas is used to convert height from inches to feet and weight from pounds to kilograms, making the data more relatable and understandable.', 'An introduction to visualization using Matplotlib for exploratory data analytics is provided, with the speaker explaining its pre-installed availability in Anaconda, highlighting its usefulness for data analysis.', 'The chapter discusses building a linear regression model to predict weight based on height using a data set of 25,000 points, obtaining a slope of 16.78 and an intercept of -37.45.', 'The need to consider additional factors such as gender, country, lifestyle, and occupation for accurate weight prediction is highlighted, emphasizing that height alone is not sufficient for accurate predictions.', "The model's R value of 0.25 indicates poor learning, suggesting the need to consider additional factors for accurate weight prediction.", 'The machine learned a decision tree based on grade and work experience, accurately predicting shortlisting outcomes with a simple code implementation.', 'The data set used for decision tree creation included historical shortlisting data based on grade and work experience.', 'Emphasizes the importance of providing a holistic picture with all relevant attributes for machine learning to predict accurately, emphasizing the limitations of linear regression using only height as an input.', 'The chapter also explained the distinction between artificial intelligence, machine learning, and deep learning, clarifying that machine learning is a subset of AI.', 'The talk is designed for beginners interested in starting with machine learning and aims to simplify the understanding of the topic in plain English, catering to the common struggle of grasping new technologies filled with jargons and buzzwords.', 'The chapter explores witnessing machine learning in action, impacting user experience.', 'We will be solving a couple of real-life problems using machine learning, such as predicting the weight of people based on their height and shortlisting resumes using Python.', 'Machine learning contrasts traditional programming by enabling the machine to learn without explicit programming.', 'Machine learning allows systems to continuously learn, adapt, and improve based on the provided data.', 'Traditional programming involves explicitly programming business logic, while machine learning learns from data to determine qualities.']}