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Machine Learning With Python | Machine Learning Tutorial | Python Machine Learning | Simplilearn

description
🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&utm_medium=DescriptionFF&utm_source=youtube 🔥AI Engineer Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=SCE-AIMasters&utm_medium=DescriptionFF&utm_source=youtube 🔥AI & Machine Learning Bootcamp(US Only): https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=MachineLearningwithPython-Q59X518JZHE&utm_medium=Descriptionff&utm_source=youtube 🔥 Purdue Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=MachineLearningwithPython-Q59X518JZHE&utm_medium=Descriptionff&utm_source=youtube This Machine Learning with Python tutorial gives an introduction to Machine Learning and how to implement Machine Learning algorithms in Python. By the end of this video, you will be able to understand Machine Learning workflow, steps to download Anaconda, types of Machine Learning and hands-on in Python for Linear Regression and K-Means clustering algorithms. Below are the topics covered in this Machine Learning tutorial: 1. Why Machine Learning? ( 01:09 ) 2. Applications of Machine Learning ( 01:50 ) 3. How does Machine Learning work? ( 03:33 ) 4. Machine Learning Workflow ( 04:53 ) 5. Steps to download Anaconda ( 06:13 ) 6. Types of Machine Learning ( 09:53 ) 7. Linear Regression Demo ( 13:51 ) 8. K-Means Clustering Demo ( 26:02 ) 9. Use Case - Weather Analysis ( 39:27 ) What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning, and follow the path towards your dream career- https://www.simplilearn.com/machine-learning-career-guide-pdf?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube You can also go through the Slides here: https://goo.gl/AMDVtD Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: 🔥Free Machine Learning Course: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearningwithPython&utm_medium=Description&utm_source=youtube 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

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{'title': 'Machine Learning With Python | Machine Learning Tutorial | Python Machine Learning | Simplilearn', 'heatmap': [{'end': 534.943, 'start': 463.624, 'weight': 0.752}, {'end': 636.019, 'start': 598.744, 'weight': 0.773}, {'end': 1202.988, 'start': 1000.148, 'weight': 0.854}, {'end': 1336.615, 'start': 1296.751, 'weight': 0.741}, {'end': 1568.064, 'start': 1528.668, 'weight': 0.784}], 'summary': "Tutorial on machine learning with python covers the basics of machine learning, python, and its applications like paypal's fraud detection. it delves into anaconda installation and provides examples of supervised and unsupervised learning, linear regression models, k-means clustering, and weather analysis using machine learning algorithms and python.", 'chapters': [{'end': 371.042, 'segs': [{'end': 73.589, 'src': 'embed', 'start': 46.449, 'weight': 0, 'content': [{'end': 51.393, 'text': "And then we'll get back into more detail as to exactly what kinds of machine learning there are.", 'start': 46.449, 'duration': 4.944}, {'end': 56.638, 'text': "And finally, we'll do a linear regression demo and a k-means clustering demo,", 'start': 51.774, 'duration': 4.864}, {'end': 61.282, 'text': 'where you actually get to write a couple scripts and get your hands dirty and do some Python work.', 'start': 56.638, 'duration': 4.644}, {'end': 65.224, 'text': "And finally, we'll wrap it up with a use case weather analysis.", 'start': 61.702, 'duration': 3.522}, {'end': 68.006, 'text': "So let's get started with the first two bullet points.", 'start': 65.805, 'duration': 2.201}, {'end': 71.348, 'text': 'Why use machine learning and applications of machine learning?', 'start': 68.286, 'duration': 3.062}, {'end': 73.589, 'text': 'Why use machine learning?', 'start': 72.048, 'duration': 1.541}], 'summary': 'Introduction to machine learning, linear regression, k-means clustering, and weather analysis use case.', 'duration': 27.14, 'max_score': 46.449, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE46449.jpg'}, {'end': 155.919, 'src': 'embed', 'start': 126.862, 'weight': 2, 'content': [{'end': 134.067, 'text': "From automated cars to scientific discoveries, any of these things are all part of today's world of machine learning.", 'start': 126.862, 'duration': 7.205}, {'end': 136.485, 'text': "And let's take a look at the search engine, the first one.", 'start': 134.383, 'duration': 2.102}, {'end': 143.45, 'text': 'Imagine if you were at Google or Bing or Yahoo.com and someone typed.', 'start': 137.125, 'duration': 6.325}, {'end': 149.514, 'text': 'you typed in a search to one of those and that goes across to somebody and they look at it, they have to read it.', 'start': 143.45, 'duration': 6.064}, {'end': 155.919, 'text': 'then they go to the library, they look up the information, they come back and then they type back the answer to you,', 'start': 149.514, 'duration': 6.405}], 'summary': 'Machine learning impacts search engines, like google or bing, by processing and providing quick responses to user queries.', 'duration': 29.057, 'max_score': 126.862, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE126862.jpg'}], 'start': 4.075, 'title': 'Machine learning basics and process', 'summary': "Introduces the basics of machine learning and python, highlighting its significance and applications such as paypal's fraud detection, and search engine, voice, and number plate recognition. it also explains the machine learning process, including learning phase, pre-processing, training, testing, and deployment.", 'chapters': [{'end': 189.495, 'start': 4.075, 'title': 'Intro to machine learning with python', 'summary': "Addresses the basics of machine learning and python, covering why use machine learning and applications, including paypal's fraud detection, and various applications like search engine results, voice recognition, and number plate recognition.", 'duration': 185.42, 'highlights': ['PayPal uses machine learning to detect fraud, leading to automated and reliable tools that stopped different frauds. PayPal utilizes machine learning to combat fraud, resulting in the automation of fraud detection and prevention, improving reliability and reducing fraudulent activities.', 'Applications of machine learning include search engine results, voice recognition, number plate recognition, and dream reader, showcasing the wide-ranging impact of machine learning in various domains. Machine learning has diverse applications, such as improving search engine results, enhancing voice recognition, implementing number plate recognition, and even dream reading, demonstrating its broad impact across different fields.', 'The chapter covers the basics of machine learning and Python, aiming to make the complex topics accessible to beginners and enable them to write simple scripts for machine learning in Python. The tutorial aims to simplify machine learning and Python for beginners, empowering them to write basic scripts and delve into machine learning concepts.']}, {'end': 371.042, 'start': 189.535, 'title': 'Machine learning process', 'summary': 'Explains the use of machine learning to make life easier and more consistent, detailing the learning phase, pre-processing, training, testing, and deployment in the machine learning workflow.', 'duration': 181.507, 'highlights': ['Machine learning helps make life easier and processes more consistent and reliable. The use of machine learning is to make life easier and processes more consistent and reliable.', 'The learning phase involves pre-processing, learning from data, and testing with supervised, unsupervised, and a third field of data. The learning phase involves pre-processing, learning from data, and testing with supervised, unsupervised, and a third field of data.', 'The machine learning workflow includes defining objectives, preparing and collecting data, selecting appropriate algorithms, training the model, testing, and deploying the model for prediction. The machine learning workflow includes defining objectives, preparing and collecting data, selecting appropriate algorithms, training the model, testing, and deploying the model for prediction.']}], 'duration': 366.967, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE4075.jpg', 'highlights': ['PayPal utilizes machine learning to combat fraud, resulting in the automation of fraud detection and prevention, improving reliability and reducing fraudulent activities.', 'Machine learning has diverse applications, such as improving search engine results, enhancing voice recognition, implementing number plate recognition, and even dream reading, demonstrating its broad impact across different fields.', 'The tutorial aims to simplify machine learning and Python for beginners, empowering them to write basic scripts and delve into machine learning concepts.', 'The use of machine learning is to make life easier and processes more consistent and reliable.', 'The learning phase involves pre-processing, learning from data, and testing with supervised, unsupervised, and a third field of data.', 'The machine learning workflow includes defining objectives, preparing and collecting data, selecting appropriate algorithms, training the model, testing, and deploying the model for prediction.']}, {'end': 586.274, 'segs': [{'end': 412.606, 'src': 'embed', 'start': 371.422, 'weight': 0, 'content': [{'end': 376.044, 'text': "We're going to need a tool to do this in, so let's look at downloading Anaconda.", 'start': 371.422, 'duration': 4.622}, {'end': 382.966, 'text': 'Now Anaconda, which utilizes Jupyter notebooks, is one of the top tools used by data scientists.', 'start': 376.644, 'duration': 6.322}, {'end': 384.807, 'text': "It's one of my favorite ones.", 'start': 383.466, 'duration': 1.341}, {'end': 392.909, 'text': "It's also very easy and simple so that anybody who's developing program or script can use it to get right in and jump ahead.", 'start': 385.287, 'duration': 7.622}, {'end': 397.181, 'text': 'Now Anaconda, which you might also reference as Jupyter Notebooks,', 'start': 393.553, 'duration': 3.628}, {'end': 403.434, 'text': "because part of that and we'll show you in just a minute is part of that group of programs and applications is a wonderful tool.", 'start': 397.181, 'duration': 6.253}, {'end': 412.606, 'text': 'And it is used by both people who are just beginning and starting out in Python or data science, all the way to advanced people and professionals.', 'start': 404.161, 'duration': 8.445}], 'summary': 'Anaconda, with jupyter notebooks, is a top tool for data scientists, easy for all levels.', 'duration': 41.184, 'max_score': 371.422, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE371422.jpg'}, {'end': 534.943, 'src': 'heatmap', 'start': 463.624, 'weight': 0.752, 'content': [{'end': 466.927, 'text': "You'll see here the Welcome to Anaconda 5.0.", 'start': 463.624, 'duration': 3.303}, {'end': 468.628, 'text': '1 64-bit setup.', 'start': 466.927, 'duration': 1.701}, {'end': 471.29, 'text': 'This wizard will guide you through the different steps.', 'start': 469.148, 'duration': 2.142}, {'end': 478.514, 'text': 'Throughout the wizard, unless you have a special setup, you can go ahead and just let it select the defaults.', 'start': 472.09, 'duration': 6.424}, {'end': 482.997, 'text': 'And once you get to the end, you can go ahead and click on Install to start the installation.', 'start': 479.035, 'duration': 3.962}, {'end': 491.025, 'text': "Once you have installed Anaconda onto your computer, you'll be able to find Anaconda Prompt, which will open up a window.", 'start': 483.558, 'duration': 7.467}, {'end': 498.273, 'text': "In this window, you'll type Jupyter Notebook, that's J-U-P-Y-T-R, space Notebook.", 'start': 491.705, 'duration': 6.568}, {'end': 504.42, 'text': 'You might also have the Jupyter Notebook startup in there too, which will do this automatically for you.', 'start': 498.933, 'duration': 5.487}, {'end': 508.284, 'text': 'Jupyter Notebook will then open up your default browser window.', 'start': 505.06, 'duration': 3.224}, {'end': 514.604, 'text': "Well, you'll then have your running Anaconda through Jupyter set up, and you'll be able to select New in Python.", 'start': 508.916, 'duration': 5.688}, {'end': 521.313, 'text': "You'll see, on the upper right-hand corner you'll have a New button and you'll have a drop-down where you then select Python 3, or,", 'start': 515.063, 'duration': 6.25}, {'end': 524.537, 'text': 'if you installed it, Python 2.7..', 'start': 521.313, 'duration': 3.224}, {'end': 528.66, 'text': "Once you've opened up your new Python notebook, you'll have a line you can type in.", 'start': 524.537, 'duration': 4.123}, {'end': 532.282, 'text': 'And you may want to just try quick script in Python, print.', 'start': 528.68, 'duration': 3.602}, {'end': 534.943, 'text': "In this case, it'll say print this line will be printed.", 'start': 532.442, 'duration': 2.501}], 'summary': 'Anaconda 5.0 installation guide for jupyter notebook setup and python usage.', 'duration': 71.319, 'max_score': 463.624, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE463624.jpg'}], 'start': 371.422, 'title': 'Anaconda installation and usage', 'summary': 'Discusses the benefits of using anaconda for data science, provides guidance on downloading and selecting the appropriate version and explains the installation process of anaconda 5.0.1 64-bit setup and jupyter notebook setup for running python scripts.', 'chapters': [{'end': 456.53, 'start': 371.422, 'title': 'Downloading anaconda for data science', 'summary': "Discusses the benefits of using anaconda, a popular tool among data scientists, which is easy to use and suitable for both beginners and professionals, and provides guidance on how to download anaconda and select the appropriate version based on user's needs.", 'duration': 85.108, 'highlights': ['Anaconda is a top tool used by data scientists, suitable for both beginners and professionals, and is easy and simple to use.', 'Anaconda can be downloaded from www.anaconda.com, where users can select the appropriate version and Python version 3.6 is recommended for beginners.', 'Users can also download Python version 2.7 if they have older modules they might need, and can add new versions in Anaconda if required.']}, {'end': 586.274, 'start': 456.94, 'title': 'Anaconda installation and jupyter notebook setup', 'summary': 'Explains the installation process of anaconda 5.0.1 64-bit setup and how to set up and use jupyter notebook through anaconda, enabling users to run python scripts and continue programming in a user-friendly environment.', 'duration': 129.334, 'highlights': ['The chapter explains the installation process of Anaconda 5.0.1 64-bit setup. The installation process of Anaconda 5.0.1 64-bit setup is detailed, guiding users through the different steps and default selections.', 'How to set up and use Jupyter Notebook through Anaconda, enabling users to run Python scripts and continue programming. The process of setting up and using Jupyter Notebook through Anaconda is explained, including opening Anaconda Prompt, launching Jupyter Notebook, and writing and executing Python scripts.', 'Demonstration of using Jupyter Notebook in Anaconda with a practical example. A practical demonstration of using Jupyter Notebook in Anaconda is provided, showcasing the steps to create and execute Python scripts within the Jupyter environment.']}], 'duration': 214.852, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE371422.jpg', 'highlights': ['Anaconda is a top tool for data scientists, suitable for beginners and professionals.', 'Anaconda can be downloaded from www.anaconda.com, allowing selection of appropriate version.', 'Python version 3.6 is recommended for beginners using Anaconda.', 'The installation process of Anaconda 5.0.1 64-bit setup is detailed.', 'Guidance on setting up and using Jupyter Notebook through Anaconda is provided.', 'Demonstration of using Jupyter Notebook in Anaconda with a practical example.']}, {'end': 1098.246, 'segs': [{'end': 656.844, 'src': 'heatmap', 'start': 598.744, 'weight': 7, 'content': [{'end': 600.346, 'text': "And we'll start with supervised learning.", 'start': 598.744, 'duration': 1.602}, {'end': 605.43, 'text': 'If you remember before, we had mentioned supervised learning and unsupervised learning.', 'start': 600.928, 'duration': 4.502}, {'end': 609.071, 'text': 'And I said there was a third one, which is reinforcement learning.', 'start': 605.85, 'duration': 3.221}, {'end': 610.552, 'text': "So let's look a little deeper.", 'start': 609.491, 'duration': 1.061}, {'end': 613.373, 'text': 'What is supervised learning? Supervised learning.', 'start': 610.652, 'duration': 2.721}, {'end': 618.515, 'text': 'Machine learning model learns from the past input and makes future prediction as output.', 'start': 613.513, 'duration': 5.002}, {'end': 620.436, 'text': 'Types of supervised learning.', 'start': 618.995, 'duration': 1.441}, {'end': 626.117, 'text': 'The two main kinds of supervised learning that are used today is classification and regression.', 'start': 620.976, 'duration': 5.141}, {'end': 631.478, 'text': 'Classification is concerned with building models that separate data into distinct classes.', 'start': 626.657, 'duration': 4.821}, {'end': 636.019, 'text': 'As you can see, the guy on the left, he has yes, no, very common, true, false.', 'start': 632.098, 'duration': 3.921}, {'end': 642.58, 'text': "They can have more than two classes, but usually you see a lot of classification where it's two decisions to make.", 'start': 636.799, 'duration': 5.781}, {'end': 645.441, 'text': "Let this person have a loan or don't let him have a loan.", 'start': 643.14, 'duration': 2.301}, {'end': 650.862, 'text': "Or in the cases earlier, let them bill to PayPal or don't let them bill to PayPal.", 'start': 645.821, 'duration': 5.041}, {'end': 656.844, 'text': 'Common algorithm used, the two common ones are decision tree and support vector machine.', 'start': 651.422, 'duration': 5.422}], 'summary': 'Supervised learning involves classification and regression, using algorithms like decision trees and support vector machines.', 'duration': 58.1, 'max_score': 598.744, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE598744.jpg'}, {'end': 744.099, 'src': 'embed', 'start': 716.909, 'weight': 0, 'content': [{'end': 722.094, 'text': "The first one is, are the parents visiting? If the parents are visiting, yes, then we'll probably watch a movie.", 'start': 716.909, 'duration': 5.185}, {'end': 724.556, 'text': "If no, then it's going to depend on the weather.", 'start': 722.434, 'duration': 2.122}, {'end': 727.98, 'text': "If the weather is rainy, we'll probably stay inside and do something.", 'start': 724.918, 'duration': 3.062}, {'end': 730.522, 'text': "If it's sunny, we'll probably go outside and play tennis.", 'start': 728.14, 'duration': 2.382}, {'end': 736.766, 'text': "So you can see this basic setup generates a tree based on what's going on and different information coming in.", 'start': 731.202, 'duration': 5.564}, {'end': 740.439, 'text': "Let's take a look now at supervised learning regression.", 'start': 737.298, 'duration': 3.141}, {'end': 744.099, 'text': 'In this model, we see we have the past data coming in.', 'start': 740.839, 'duration': 3.26}], 'summary': "Decision-making based on parents' visit, weather, and activities. introduction to supervised learning regression.", 'duration': 27.19, 'max_score': 716.909, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE716909.jpg'}, {'end': 798.118, 'src': 'embed', 'start': 768.925, 'weight': 3, 'content': [{'end': 776.608, 'text': "we can now then put that same information that's happened like the last three days and try to predict what today's weather is going to be based on that information that came in.", 'start': 768.925, 'duration': 7.683}, {'end': 780.63, 'text': 'This is a good view of supervised learning, the regression model.', 'start': 777.228, 'duration': 3.402}, {'end': 784.231, 'text': "Let's look at another example of supervised learning regression.", 'start': 781.05, 'duration': 3.181}, {'end': 787.092, 'text': "In this one, we're going to take a look at real estate.", 'start': 784.751, 'duration': 2.341}, {'end': 790.094, 'text': 'Very common use of linear regression.', 'start': 787.392, 'duration': 2.702}, {'end': 798.118, 'text': "And let's say we're in a market and we know that a small house sold for $1 amount and a larger house sold for a larger dollar amount.", 'start': 790.354, 'duration': 7.764}], 'summary': 'Supervised learning using regression to predict weather and real estate prices.', 'duration': 29.193, 'max_score': 768.925, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE768925.jpg'}], 'start': 587.114, 'title': 'Types and examples of supervised learning', 'summary': 'Explains three types of machine learning, with a focus on supervised learning, covering classification and regression. it provides examples using decision tree and support vector machine. additionally, it delves into supervised learning regression, using past data to predict future outcomes, and demonstrates linear regression implementation with python code. key examples include weather prediction and real estate pricing.', 'chapters': [{'end': 730.522, 'start': 587.114, 'title': 'Supervised learning: types and examples', 'summary': 'Explains the three types of machine learning, focusing on supervised learning, its types - classification and regression, and examples using decision tree and support vector machine.', 'duration': 143.408, 'highlights': ['Supervised learning involves the machine learning model learning from past input and making future predictions as output, with two main types - classification and regression.', 'Classification in supervised learning is concerned with building models that separate data into distinct classes, such as deciding whether to approve a loan or bill to PayPal.', 'Common algorithms used in classification are decision tree and support vector machine.', 'Regression, another type of supervised learning, predicts continuous output value based on previous input data, with algorithms like linear regression and polynomial regression being commonly used.', 'Examples of supervised learning include using past data of images of girls and boys to train a model, and using a decision tree to make weekend plans based on various factors like parents visiting and weather conditions.']}, {'end': 1098.246, 'start': 731.202, 'title': 'Supervised learning regression', 'summary': 'Covers supervised learning regression, explaining how past data is used to predict future outcomes, using examples of weather prediction and real estate pricing, and demonstrates the implementation of linear regression with python code.', 'duration': 367.044, 'highlights': ['The chapter covers supervised learning regression, explaining how past data is used to predict future outcomes, using examples of weather prediction and real estate pricing. The model uses past data to predict future outcomes, such as weather prediction based on temperature and rainfall, and real estate pricing based on house size and sale amount.', 'The demonstration of the implementation of linear regression with Python code. The transcript provides a step-by-step demonstration of implementing linear regression with Python code, including importing necessary libraries, loading datasets, and reshaping input data for regression analysis.']}], 'duration': 511.132, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE587114.jpg', 'highlights': ['Supervised learning involves learning from past input and making future predictions as output', 'Classification separates data into distinct classes, e.g., loan approval or bill to PayPal', 'Regression predicts continuous output value based on previous input data', 'Decision tree and support vector machine are common algorithms used in classification', 'Linear regression and polynomial regression are commonly used in regression', 'Examples of supervised learning include using past data of images of girls and boys to train a model', 'The chapter covers supervised learning regression, explaining how past data is used to predict future outcomes', 'Demonstration of the implementation of linear regression with Python code']}, {'end': 1639.506, 'segs': [{'end': 1183.434, 'src': 'embed', 'start': 1158.413, 'weight': 0, 'content': [{'end': 1164.137, 'text': 'Now here we are back in our Anaconda Jupyter notebook, and I can put this down below,', 'start': 1158.413, 'duration': 5.724}, {'end': 1169.32, 'text': 'and this is one of the nice things about this is I keep my script separate so I can test each piece one at a time.', 'start': 1164.137, 'duration': 5.183}, {'end': 1177.225, 'text': "And let's put that code we just did in there and then I'm going to run it and you'll see here where it prints out the coefficient and the intercept.", 'start': 1169.64, 'duration': 7.585}, {'end': 1183.434, 'text': "So we're going to put in size new equals 1400.", 'start': 1178.265, 'duration': 5.169}], 'summary': 'In anaconda jupyter notebook, testing code separately, prints coefficient and intercept, input size new equals 1400.', 'duration': 25.021, 'max_score': 1158.413, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE1158413.jpg'}, {'end': 1226.99, 'src': 'embed', 'start': 1202.988, 'weight': 1, 'content': [{'end': 1209.532, 'text': "Another way to do this, and this is very important to notice, although we'll be using the formula in here so you can see what's going on is.", 'start': 1202.988, 'duration': 6.544}, {'end': 1213.634, 'text': 'you can also do regress dot, predict size new.', 'start': 1209.532, 'duration': 4.102}, {'end': 1218.537, 'text': "And you'll see in here that I've taken the size new and I've put the double brackets around it.", 'start': 1213.874, 'duration': 4.663}, {'end': 1220.929, 'text': "because that's the format it needs to see in there.", 'start': 1219.008, 'duration': 1.921}, {'end': 1223.829, 'text': 'It needs to see a single row, single column.', 'start': 1220.949, 'duration': 2.88}, {'end': 1226.99, 'text': "And let's run that, and you'll see it gives the same answer.", 'start': 1224.109, 'duration': 2.881}], 'summary': "Demonstrating regression using 'regress dot, predict' with new size, yielding the same result.", 'duration': 24.002, 'max_score': 1202.988, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE1202988.jpg'}, {'end': 1336.615, 'src': 'heatmap', 'start': 1296.751, 'weight': 0.741, 'content': [{'end': 1302.555, 'text': "so we're going to be putting a formula in here and we're going to eval that formula, and that's what y is going to be equal to.", 'start': 1296.751, 'duration': 5.804}, {'end': 1308.85, 'text': 'And then finally, we want to go ahead and take our PLT plot and add those numbers, that x, y in there.', 'start': 1303.206, 'duration': 5.644}, {'end': 1317.017, 'text': "So basically, we're taking the x value from our array coming in, and then we're going to evaluate with the formula we're going to send in there also.", 'start': 1309.311, 'duration': 7.706}, {'end': 1323.713, 'text': "Finally, we're going to use that definition we just created, and you'll see right here it says graph,", 'start': 1318.171, 'duration': 5.542}, {'end': 1332.135, 'text': "and then we're going to send it the following formula the regression coefficient times x plus the regression intercept.", 'start': 1323.713, 'duration': 8.422}, {'end': 1336.615, 'text': "and then we're sending it a range of 1, 000 to 2, 700..", 'start': 1332.135, 'duration': 4.48}], 'summary': 'Evaluate formula, plot x-y values, graph regression formula with a range of 1,000 to 2,700.', 'duration': 39.864, 'max_score': 1296.751, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE1296751.jpg'}, {'end': 1568.064, 'src': 'heatmap', 'start': 1528.668, 'weight': 0.784, 'content': [{'end': 1535.552, 'text': 'Two other common algorithms used for unsupervised clustering is the hierarchical clustering and the hidden Markov model.', 'start': 1528.668, 'duration': 6.884}, {'end': 1541.577, 'text': 'Association discovers the probability of the co-occurrence of items in a collection.', 'start': 1536.172, 'duration': 5.405}, {'end': 1548.364, 'text': "The most common used algorithm for that is the APRORI algorithm, and there's also the FP growth algorithm.", 'start': 1541.838, 'duration': 6.526}, {'end': 1552.637, 'text': "Let's take a closer look at unsupervised learning and clustering.", 'start': 1548.875, 'duration': 3.762}, {'end': 1560.78, 'text': "And the basic idea of clustering is we're going to take similar items and cluster them together and move dissimilar items into their own group.", 'start': 1553.197, 'duration': 7.583}, {'end': 1562.881, 'text': 'And the K means clustering.', 'start': 1561.461, 'duration': 1.42}, {'end': 1568.064, 'text': 'The first thing we need to do is randomly initialize two points called the cluster centroids.', 'start': 1563.502, 'duration': 4.562}], 'summary': 'Unsupervised clustering involves algorithms like hierarchical, hidden markov model, apriori, and fp growth, with k means clustering using cluster centroids.', 'duration': 39.396, 'max_score': 1528.668, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE1528668.jpg'}, {'end': 1619.737, 'src': 'embed', 'start': 1594.287, 'weight': 2, 'content': [{'end': 1601.792, 'text': "We're going to keep repeating that and we're going to iterate that until the cluster centroids stop changing their positions and become static.", 'start': 1594.287, 'duration': 7.505}, {'end': 1609.574, 'text': 'In other words, when the points seem to form best around the two centroids and seem to have their own groups, very distinct groups.', 'start': 1602.591, 'duration': 6.983}, {'end': 1616.156, 'text': "once the clusters become static and there's no more adjusting or moving around, that algorithm is said to be converged.", 'start': 1609.574, 'duration': 6.582}, {'end': 1619.737, 'text': "You'll hear the term converged in most machine learning.", 'start': 1616.696, 'duration': 3.041}], 'summary': 'Iterate until centroids stop changing, forming distinct groups. convergence in ml.', 'duration': 25.45, 'max_score': 1594.287, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE1594287.jpg'}], 'start': 1099.126, 'title': 'Linear regression models and unsupervised learning', 'summary': 'Covers creating and fitting a linear regression model in python, testing the model to predict house prices, and introducing unsupervised learning, including clustering and association algorithms.', 'chapters': [{'end': 1266.022, 'start': 1099.126, 'title': 'Linear regression model in python', 'summary': 'Explains the process of creating and fitting a linear regression model using a python script, including printing the intercept and coefficient, and testing the model to predict house prices, emphasizing the importance of using the predict function for more complex machine learning.', 'duration': 166.896, 'highlights': ['The chapter explains the process of creating and fitting a linear regression model using a Python script, including printing the intercept and coefficient, and testing the model to predict house prices, emphasizing the importance of using the predict function for more complex machine learning.', "Creating a variable 'REGR' for the linear model and fitting the data of size 2 and house prices to a linear regression model.", 'Printing the intercept and coefficient of the linear regression model to observe the output.', "Testing the model by predicting the price for a new size value and emphasizing the importance of using the 'predict' function for more complex machine learning."]}, {'end': 1639.506, 'start': 1266.042, 'title': 'Linear regression and unsupervised learning', 'summary': 'Covers the implementation of linear regression for data visualization and analysis, including the creation of regression models and scatter plots, and the introduction to unsupervised learning, specifically clustering and association algorithms.', 'duration': 373.464, 'highlights': ['The process of implementing linear regression for data visualization and analysis was explained, including the creation of regression models, scatter plots, and data labeling. Linear regression implementation, creation of regression models, scatter plots, data labeling', 'An introduction to unsupervised learning was provided, focusing on clustering and association algorithms, including K-means, hierarchical clustering, hidden Markov model, APRORI algorithm, and FP growth algorithm. Introduction to unsupervised learning, clustering and association algorithms, K-means, hierarchical clustering, hidden Markov model, APRORI algorithm, FP growth algorithm', 'The process of K-means clustering was detailed, including the random initialization of cluster centroids, grouping based on distance, centroid adjustments, and convergence criteria. K-means clustering process, random initialization of cluster centroids, grouping based on distance, centroid adjustments, convergence criteria']}], 'duration': 540.38, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE1099126.jpg', 'highlights': ['Introduction to unsupervised learning, clustering and association algorithms, K-means, hierarchical clustering, hidden Markov model, APRORI algorithm, FP growth algorithm', 'The process of K-means clustering was detailed, including the random initialization of cluster centroids, grouping based on distance, centroid adjustments, and convergence criteria', 'The chapter explains the process of creating and fitting a linear regression model using a Python script, including printing the intercept and coefficient, and testing the model to predict house prices, emphasizing the importance of using the predict function for more complex machine learning', 'The process of implementing linear regression for data visualization and analysis was explained, including the creation of regression models, scatter plots, and data labeling']}, {'end': 2352.793, 'segs': [{'end': 1711.671, 'src': 'embed', 'start': 1686.103, 'weight': 0, 'content': [{'end': 1692.294, 'text': 'You start clustering these different things together until you find where is the optimal place to place a hotel.', 'start': 1686.103, 'duration': 6.191}, {'end': 1701.284, 'text': "Now we're not going to dig up all the data on hotels and the logistics of movement to different areas, not for a basic learning example.", 'start': 1693.058, 'duration': 8.226}, {'end': 1705.827, 'text': "Definitely something you'd want to dig into in a situation like this.", 'start': 1701.464, 'duration': 4.363}, {'end': 1707.228, 'text': "But let's keep it simple.", 'start': 1706.247, 'duration': 0.981}, {'end': 1711.671, 'text': "Let's roll up our sleeves and let's see what a k-means clustering looks like in Python script.", 'start': 1707.408, 'duration': 4.263}], 'summary': 'Using k-means clustering to find optimal hotel location in python.', 'duration': 25.568, 'max_score': 1686.103, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE1686103.jpg'}, {'end': 1777.766, 'src': 'embed', 'start': 1744.739, 'weight': 5, 'content': [{'end': 1748.44, 'text': "We're not going to go into all the different options you have in the matplot library.", 'start': 1744.739, 'duration': 3.701}, {'end': 1749.3, 'text': "You'd have to look that up.", 'start': 1748.46, 'duration': 0.84}, {'end': 1751.736, 'text': 'but it certainly has a lot of uses in there.', 'start': 1749.775, 'duration': 1.961}, {'end': 1755.938, 'text': 'This just gives you an idea that there is more to it than just a map plot library.', 'start': 1751.796, 'duration': 4.142}, {'end': 1762.441, 'text': "And finally, from the sklearn cluster, we're going to import k-means.", 'start': 1756.578, 'duration': 5.863}, {'end': 1767.503, 'text': "Once we've done all our imports, we want to go ahead and set up some data to use and play with.", 'start': 1762.841, 'duration': 4.662}, {'end': 1777.766, 'text': "So we're just going to take some random numbers in this case, and I'm going to set x equal to 1, 5, 1.5, 8, 1, 9,", 'start': 1767.963, 'duration': 9.803}], 'summary': 'Matplot library offers various uses, and we import k-means from sklearn cluster.', 'duration': 33.027, 'max_score': 1744.739, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE1744739.jpg'}], 'start': 1640.206, 'title': 'K-means clustering and data visualization', 'summary': 'Discusses leveraging k-means clustering to optimize hotel delivery centers, emphasizing cost reduction and efficient coverage of high-order areas, illustrated through a python script and visualization. it also covers the process of plotting and visualizing data using plt plot and scatter in a python jupyter notebook, with applications in marketing and reinforcement learning.', 'chapters': [{'end': 2013.894, 'start': 1640.206, 'title': 'K-means clustering for hotel delivery optimization', 'summary': 'Discusses using k-means clustering to optimize hotel delivery centers across the city, with an emphasis on minimizing costs and efficiently covering the areas with frequent food orders, demonstrating the process through a python script and visualization.', 'duration': 373.688, 'highlights': ['The chapter discusses the challenges faced by a hotel chain in optimizing delivery centers across the city and emphasizes the need to analyze areas with frequent food orders. Analyzing areas with frequent food orders is essential for determining the optimal number and locations of hotels required to cover the city area.', 'The chapter demonstrates the process of k-means clustering using a Python script to optimize the placement of hotel delivery centers, with a focus on minimizing the distance between hotels and delivery points. Utilizing k-means clustering in Python script allows for the efficient minimization of the distance between hotels and delivery points, optimizing the placement of delivery centers.', 'The process of k-means clustering is illustrated through a Python script, emphasizing the simplicity and ease of implementation with minimal programming. The demonstration highlights the simplicity of implementing k-means clustering through a few lines of code, making it accessible and straightforward for optimization tasks.']}, {'end': 2352.793, 'start': 2014.494, 'title': 'Plotting and visualizing data', 'summary': 'Explains how to plot and visualize data using plt plot and scatter, emphasizing the process of plotting coordinates, labels, centroids, and their visualization in a python jupyter notebook, and covers applications in marketing and reinforcement learning.', 'duration': 338.299, 'highlights': ['The process of plotting coordinates, labels, and centroids is explained, involving the use of plt.plot and plt.scatter to visualize the data. ', 'The visualization process is demonstrated in a Python Jupyter notebook with the coordinates and centroids printed out, and a graph displaying the centroids. ', 'The application of data visualization in marketing is discussed, illustrating how clustering can be used to group items that are frequently bought together, such as peanut butter and jelly. ', 'The concept of reinforcement learning is introduced, highlighting its application in teaching machines to think for themselves based on past actions and rewards, using the example of Mario Brothers game. ']}], 'duration': 712.587, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE1640206.jpg', 'highlights': ['The chapter demonstrates k-means clustering to optimize hotel delivery centers, focusing on minimizing distance between hotels and delivery points.', 'Analyzing areas with frequent food orders is essential for determining the optimal number and locations of hotels required to cover the city area.', 'The simplicity of implementing k-means clustering through a few lines of code makes it accessible and straightforward for optimization tasks.', 'The process of plotting coordinates, labels, and centroids is explained, involving the use of plt.plot and plt.scatter to visualize the data.', 'The application of data visualization in marketing is discussed, illustrating how clustering can be used to group items that are frequently bought together.', 'The concept of reinforcement learning is introduced, highlighting its application in teaching machines to think for themselves based on past actions and rewards.']}, {'end': 3014.08, 'segs': [{'end': 2557.832, 'src': 'embed', 'start': 2529.385, 'weight': 1, 'content': [{'end': 2531.407, 'text': 'And again, we went with the GG plot on here.', 'start': 2529.385, 'duration': 2.022}, {'end': 2540.836, 'text': "Now the one thing I want you to notice on here that's missing from when we did k-means before was the k-means library from sklearn.", 'start': 2531.727, 'duration': 9.109}, {'end': 2548.464, 'text': "What we're going to show you here and this is why I don't want you to worry too much if you get a little lost is what it looks like when you don't use the sklearn.", 'start': 2541.217, 'duration': 7.247}, {'end': 2550.826, 'text': 'Now remember the sklearn took a couple lines.', 'start': 2548.584, 'duration': 2.242}, {'end': 2555.11, 'text': 'We told it how many centroids we needed and then we let it do the rest of the work.', 'start': 2551.207, 'duration': 3.903}, {'end': 2557.832, 'text': "So in this example, I'm going to walk you through.", 'start': 2555.831, 'duration': 2.001}], 'summary': 'Comparison of gg plot with k-means library from sklearn, demonstrating the difference in usage and simplicity.', 'duration': 28.447, 'max_score': 2529.385, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE2529385.jpg'}, {'end': 2593.166, 'src': 'embed', 'start': 2570.515, 'weight': 0, 'content': [{'end': 2581.739, 'text': 'So, if you have saved a CSV file comma separated variables with your data in it, you can now easily import that into your program using the PD,', 'start': 2570.515, 'duration': 11.224}, {'end': 2582.34, 'text': 'the Panda.', 'start': 2581.739, 'duration': 0.601}, {'end': 2593.166, 'text': 'And in here you can see that Panda recognizes that there is the header line and then it also recognizes how much data is in there and breaks it up for you.', 'start': 2582.64, 'duration': 10.526}], 'summary': 'Panda can import csv files, recognizing headers and breaking up data.', 'duration': 22.651, 'max_score': 2570.515, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE2570515.jpg'}, {'end': 2701.193, 'src': 'embed', 'start': 2673.074, 'weight': 3, 'content': [{'end': 2675.076, 'text': "So basically, I've created my XY.", 'start': 2673.074, 'duration': 2.002}, {'end': 2676.997, 'text': "In this case, it's F1 and F2.", 'start': 2675.416, 'duration': 1.581}, {'end': 2682.101, 'text': "And then we're going to come down here and we're going to take x equals npArray.", 'start': 2678.038, 'duration': 4.063}, {'end': 2683.662, 'text': "That's your numpy array.", 'start': 2682.461, 'duration': 1.201}, {'end': 2685.263, 'text': "And we're going to create a list.", 'start': 2684.002, 'duration': 1.261}, {'end': 2688.204, 'text': "And we've done something just a little different here.", 'start': 2685.743, 'duration': 2.461}, {'end': 2692.287, 'text': "You'll see where it says zip f1 comma f2.", 'start': 2688.785, 'duration': 3.502}, {'end': 2695.689, 'text': 'That takes and that combines the f1, f2.', 'start': 2692.687, 'duration': 3.002}, {'end': 2701.193, 'text': 'So you have a list of the first values, the second values, and so on.', 'start': 2695.809, 'duration': 5.384}], 'summary': 'Created xy with f1 and f2, using nparray to create list and zip to combine values.', 'duration': 28.119, 'max_score': 2673.074, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE2673074.jpg'}, {'end': 2795.79, 'src': 'embed', 'start': 2764.938, 'weight': 5, 'content': [{'end': 2767.779, 'text': "And it's that line of that long edge of the triangle.", 'start': 2764.938, 'duration': 2.841}, {'end': 2769.64, 'text': "That's all this is calculating on here.", 'start': 2767.819, 'duration': 1.821}, {'end': 2772.58, 'text': 'And then, of course, we already talked about number of clusters.', 'start': 2769.66, 'duration': 2.92}, {'end': 2774.521, 'text': 'So k equals 3.', 'start': 2772.821, 'duration': 1.7}, {'end': 2779.003, 'text': 'And then if you remember with k-means, it generated the centroids for you automatically.', 'start': 2774.521, 'duration': 4.482}, {'end': 2786.788, 'text': 'Well, under the hood, what is happening is it goes in there and it creates the x coordinate and it creates a random one.', 'start': 2779.223, 'duration': 7.565}, {'end': 2795.79, 'text': 'In this case, it creates three of them and it creates three random y coordinates and it takes and does it between 0 and the max value of x,', 'start': 2786.848, 'duration': 8.942}], 'summary': 'Using k-means with k=3 to generate 3 centroids and random coordinates.', 'duration': 30.852, 'max_score': 2764.938, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE2764938.jpg'}], 'start': 2353.534, 'title': 'Weather analysis with machine learning and python data analysis and visualization', 'summary': 'Discusses the use of machine learning algorithms to predict weather conditions and identify regions expecting heavy rainfall using a case study with a data set of 3,000 entries. it also covers importing and visualizing data using python libraries like numpy, pandas, and matplotlib, demonstrating data manipulation, plotting, and k-means clustering with detailed code explanations.', 'chapters': [{'end': 2437.396, 'start': 2353.534, 'title': 'Weather analysis with machine learning', 'summary': 'Discusses the use of machine learning algorithms like q-learning, temporal difference, and deep adversarial networks in weather analysis, using a case study with a data set of 3,000 entries to predict weather conditions and identify regions expecting heavy rainfall, especially relevant for flood prediction.', 'duration': 83.862, 'highlights': ['The chapter introduces the use of machine learning algorithms like Q-learning, temporal difference, and deep adversarial networks in weather analysis, demonstrating their relevance in predicting weather conditions and identifying regions expecting heavy rainfall, which is particularly important for flood prediction.', 'The case study uses a data set of 3,000 entries and assumes a K equals 3, categorizing the rainfall into light, medium, and heavy, showcasing the practical application of machine learning in weather analysis and its potential impact on flood prediction.', "The chapter emphasizes the significance of using machine learning algorithms in weather analysis, particularly in predicting weather conditions and identifying areas expecting heavy rainfall, which is crucial for addressing concerns related to flooding in today's world."]}, {'end': 3014.08, 'start': 2437.904, 'title': 'Python data analysis and visualization', 'summary': 'Covers importing and visualizing data using python libraries like numpy, pandas, and matplotlib, demonstrating data manipulation, plotting, and k-means clustering with detailed code explanations.', 'duration': 576.176, 'highlights': ['The chapter demonstrates the use of numpy, pandas, and matplotlib libraries for importing and visualizing data, showcasing the process of data manipulation and plotting.', 'The transcript provides insights into k-means clustering and explains the underlying mechanics such as centroid generation, error calculation, and the use of while loops for iterative convergence.', 'The detailed walkthrough includes explanations of deep copy for array manipulation, importing data using pandas, and plotting scatter plots in Python, offering real-life examples with 3000 data entries and explanations of centroid plotting and storage.']}], 'duration': 660.546, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE2353534.jpg', 'highlights': ['The chapter introduces the use of machine learning algorithms like Q-learning, temporal difference, and deep adversarial networks in weather analysis, demonstrating their relevance in predicting weather conditions and identifying regions expecting heavy rainfall, which is particularly important for flood prediction.', 'The case study uses a data set of 3,000 entries and assumes a K equals 3, categorizing the rainfall into light, medium, and heavy, showcasing the practical application of machine learning in weather analysis and its potential impact on flood prediction.', "The chapter emphasizes the significance of using machine learning algorithms in weather analysis, particularly in predicting weather conditions and identifying areas expecting heavy rainfall, which is crucial for addressing concerns related to flooding in today's world.", 'The chapter demonstrates the use of numpy, pandas, and matplotlib libraries for importing and visualizing data, showcasing the process of data manipulation and plotting.', 'The transcript provides insights into k-means clustering and explains the underlying mechanics such as centroid generation, error calculation, and the use of while loops for iterative convergence.', 'The detailed walkthrough includes explanations of deep copy for array manipulation, importing data using pandas, and plotting scatter plots in Python, offering real-life examples with 3000 data entries and explanations of centroid plotting and storage.']}, {'end': 3329.217, 'segs': [{'end': 3223.705, 'src': 'embed', 'start': 3200.725, 'weight': 3, 'content': [{'end': 3209.713, 'text': "So once you know which area or cluster you're in from that data, you can predict what's coming in and how much to worry about the rainfall coming in.", 'start': 3200.725, 'duration': 8.988}, {'end': 3216.72, 'text': 'So you can see in here a very, one of the common uses of weather analysis for using clusters.', 'start': 3210.153, 'duration': 6.567}, {'end': 3223.705, 'text': 'And you can certainly figure out a lot of reasons in your daily life if you start looking around and seeing where, in business,', 'start': 3217.34, 'duration': 6.365}], 'summary': 'Predict rainfall in specific areas using weather analysis for business purposes.', 'duration': 22.98, 'max_score': 3200.725, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE3200725.jpg'}, {'end': 3290.873, 'src': 'embed', 'start': 3265.612, 'weight': 4, 'content': [{'end': 3270.834, 'text': 'to come up with a prediction and a commercial grade output for our machine learning workflow.', 'start': 3265.612, 'duration': 5.222}, {'end': 3272.575, 'text': 'We looked at types of machine learning.', 'start': 3271.115, 'duration': 1.46}, {'end': 3279.618, 'text': 'Supervised learning, where we have the data and the collections and the answers to train our machine learning tools.', 'start': 3273.035, 'duration': 6.583}, {'end': 3287.282, 'text': "We had unsupervised learning, where it doesn't know what the answer is, but based on its own ideas, groups things that are like together.", 'start': 3279.939, 'duration': 7.343}, {'end': 3290.873, 'text': 'And then we took a quick look at reinforcement learning.', 'start': 3288.052, 'duration': 2.821}], 'summary': 'Analyzing machine learning types for prediction and commercial use.', 'duration': 25.261, 'max_score': 3265.612, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE3265612.jpg'}, {'end': 3329.217, 'src': 'embed', 'start': 3310.958, 'weight': 0, 'content': [{'end': 3315.579, 'text': 'And for more information, you can visit www.simplylearn.com.', 'start': 3310.958, 'duration': 4.621}, {'end': 3326.934, 'text': 'Hi there, if you like this video, subscribe to the Simply Learn YouTube channel and click here to watch similar videos.', 'start': 3320.506, 'duration': 6.428}, {'end': 3329.217, 'text': 'To nerd up and get certified, click here.', 'start': 3327.234, 'duration': 1.983}], 'summary': 'Visit www.simplylearn.com for more information and subscribe to simply learn youtube channel to watch similar videos.', 'duration': 18.259, 'max_score': 3310.958, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE3310958.jpg'}], 'start': 3014.08, 'title': 'K-means clustering process and weather analysis', 'summary': 'Explains the k-means clustering process, visualizing results with scatter plots and centroids, and delves into weather analysis using k-means for predicting rainfall. it also explores various types of machine learning such as supervised, unsupervised, and reinforcement learning.', 'chapters': [{'end': 3186.749, 'start': 3014.08, 'title': 'K-means clustering process', 'summary': 'Explains the k-means clustering process, using the coordinate distances to assign points to clusters and find new centroid values, and visualizing the results with scatter plots and centroids in a programming context.', 'duration': 172.669, 'highlights': ['The process involves assigning points to clusters based on distance from centroids and finding new centroid values for better fit, by using the mean of points and adjusting centroids iteratively until convergence.', 'The visualization includes plotting the points onto scatter plots, marking the centroids, and creating maps of pressure differences using colors for visualization, providing a clear representation of the clustering results.', 'Utilizing the np.mean function to calculate the mean of points for each cluster, and the use of R, G, B, Y, C, and M colors for visualization purposes in the scatter plot.']}, {'end': 3329.217, 'start': 3187.21, 'title': 'Weather analysis and clustering for machine learning', 'summary': 'Focuses on the use of clustering in weather analysis, highlighting the application of k-means for predicting rainfall, understanding machine learning workflow, and exploring various types of machine learning such as supervised learning, unsupervised learning, and reinforcement learning.', 'duration': 142.007, 'highlights': ['The chapter focuses on the use of clustering in weather analysis, highlighting the application of k-means for predicting rainfall The blue region with the highest temperature and lowest pressure will have high rainfall, while the red and green regions will have lesser rainfall. The k-means is a powerful tool for clustering and machine learning in weather analysis.', 'Understanding machine learning workflow The chapter discusses the machine learning workflow, which includes defining an objective, preparing and collecting data, selecting algorithms, training and testing models, and coming up with predictions and commercial grade outputs.', 'Exploring various types of machine learning such as supervised learning, unsupervised learning, and reinforcement learning The chapter covers supervised learning (with data and answers), unsupervised learning (grouping based on its own ideas), and reinforcement learning. It also delves into types of supervised learning like classification and regression, as well as types of unsupervised learning including clustering and association.']}], 'duration': 315.137, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Q59X518JZHE/pics/Q59X518JZHE3014080.jpg', 'highlights': ['The process involves assigning points to clusters based on distance from centroids and finding new centroid values for better fit, by using the mean of points and adjusting centroids iteratively until convergence.', 'The visualization includes plotting the points onto scatter plots, marking the centroids, and creating maps of pressure differences using colors for visualization, providing a clear representation of the clustering results.', 'The chapter focuses on the use of clustering in weather analysis, highlighting the application of k-means for predicting rainfall The blue region with the highest temperature and lowest pressure will have high rainfall, while the red and green regions will have lesser rainfall. The k-means is a powerful tool for clustering and machine learning in weather analysis.', 'Understanding machine learning workflow The chapter discusses the machine learning workflow, which includes defining an objective, preparing and collecting data, selecting algorithms, training and testing models, and coming up with predictions and commercial grade outputs.', 'Exploring various types of machine learning such as supervised learning, unsupervised learning, and reinforcement learning The chapter covers supervised learning (with data and answers), unsupervised learning (grouping based on its own ideas), and reinforcement learning. It also delves into types of supervised learning like classification and regression, as well as types of unsupervised learning including clustering and association.', 'Utilizing the np.mean function to calculate the mean of points for each cluster, and the use of R, G, B, Y, C, and M colors for visualization purposes in the scatter plot.']}], 'highlights': ['PayPal utilizes machine learning to combat fraud, automating detection and reducing fraudulent activities.', 'Machine learning has diverse applications, impacting search engines, voice recognition, and more.', 'The tutorial simplifies machine learning and Python for beginners, empowering them to write basic scripts.', 'The use of machine learning is to make life easier and processes more consistent and reliable.', 'The machine learning workflow includes defining objectives, preparing and collecting data, selecting algorithms, training, testing, and deploying the model for prediction.', 'Anaconda is a top tool for data scientists, suitable for beginners and professionals.', 'Supervised learning involves learning from past input and making future predictions as output.', 'The process of K-means clustering was detailed, including the random initialization of cluster centroids, grouping based on distance, centroid adjustments, and convergence criteria.', 'The chapter demonstrates k-means clustering to optimize hotel delivery centers, focusing on minimizing distance between hotels and delivery points.', 'The chapter introduces the use of machine learning algorithms like Q-learning, temporal difference, and deep adversarial networks in weather analysis.', 'The process involves assigning points to clusters based on distance from centroids and finding new centroid values for better fit, by using the mean of points and adjusting centroids iteratively until convergence.']}