WP Tutorials

Machine Learning for Everybody – Full Course

Machine Learning for Everybody – Full Course

Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts.

✏️ Kylie Ying developed this course. Check out her channel: https://www.youtube.com/c/YCubed

⭐️ Code and Resources ⭐️
🔗 Supervised learning (classification/MAGIC): https://colab.research.google.com/drive/16w3TDn_tAku17mum98EWTmjaLHAJcsk0?usp=sharing
🔗 Supervised learning (regression/bikes): https://colab.research.google.com/drive/1m3oQ9b0oYOT-DXEy0JCdgWPLGllHMb4V?usp=sharing
🔗 Unsupervised learning (seeds): https://colab.research.google.com/drive/1zw_6ZnFPCCh6mWDAd_VBMZB4VkC3ys2q?usp=sharing
🔗 Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters)
🔗 MAGIC dataset: https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope
🔗 Bikes dataset: https://archive.ics.uci.edu/ml/datasets/Seoul+Bike+Sharing+Demand
🔗 Seeds/wheat dataset: https://archive.ics.uci.edu/ml/datasets/seeds

🏗 Google provided a grant to make this course possible.

⭐️ Contents ⭐️
⌨️ (0:00:00) Intro
⌨️ (0:00:58) Data/Colab Intro
⌨️ (0:08:45) Intro to Machine Learning
⌨️ (0:12:26) Features
⌨️ (0:17:23) Classification/Regression
⌨️ (0:19:57) Training Model
⌨️ (0:30:57) Preparing Data
⌨️ (0:44:43) K-Nearest Neighbors
⌨️ (0:52:42) KNN Implementation
⌨️ (1:08:43) Naive Bayes
⌨️ (1:17:30) Naive Bayes Implementation
⌨️ (1:19:22) Logistic Regression
⌨️ (1:27:56) Log Regression Implementation
⌨️ (1:29:13) Support Vector Machine
⌨️ (1:37:54) SVM Implementation
⌨️ (1:39:44) Neural Networks
⌨️ (1:47:57) Tensorflow
⌨️ (1:49:50) Classification NN using Tensorflow
⌨️ (2:10:12) Linear Regression
⌨️ (2:34:54) Lin Regression Implementation
⌨️ (2:57:44) Lin Regression using a Neuron
⌨️ (3:00:15) Regression NN using Tensorflow
⌨️ (3:13:13) K-Means Clustering
⌨️ (3:23:46) Principal Component Analysis
⌨️ (3:33:54) K-Means and PCA Implementations

🎉 Thanks to our Champion and Sponsor supporters:
👾 Raymond Odero
👾 Agustín Kussrow
👾 aldo ferretti
👾 Otis Morgan
👾 DeezMaster

Learn to code for free and get a developer job: https://www.freecodecamp.org

Read hundreds of articles on programming: https://freecodecamp.org/news


Comments (41)

  1. Thank you very much, I truly appreciate this video.

  2. Magnificent Video
    Congratulations !!!!! 👋👋👋👋

  3. day 1 (8/7) -> 44:43
    day 2 (9/7) -> 58:35
    day 3 (10/7) -> 1:39:44
    day 4 (11/7) -> 2:34:28

  4. I recently completed the ML tutorial, and I wanted to express my gratitude for the outstanding content. The derivation and mathematical explanations were particularly impressive. I've been trying to grasp the fundamentals of machine learning for quite some time now, and this is the first tutorial where I genuinely understood the derivations and gained valuable knowledge about the topics plus the side by side implementation also helped alot. Thank you for creating such an informative and well-structured tutorial!

  5. tailwind is god

  6. gay bhai html css better

  7. As a complete newbie to machine learning i want to ask how good of a tutorial is this? What do i need to know before hand doing this? I already am familiar with pythob

  8. Confusion 1 @ 44:15

  9. I had to find out everything on my own without any books using my phone hotspot and finally got a laptop because I really couldn't work just on my phone. Found 4 or 5 older possible github sources that had addons for python that didn't work in my version that I recently downloaded. Did start using chatgpt, and modified that code enough. Found two different versions that I could actually run. One was from a github source and the other was from chatgpt. It's been a wild ride. Mine is an AI chatbot, and I just started writing out the data from the current run in pickle. It is wild to really find out most of the data is stored just in memory. I thought sure that one of the addons would have automatically stored and handled that. If I get this section to work to store everything that is loaded in, I'll make it a class and load my code on github so it might help others. I live by "be brave enough to suck at something new".

  10. idk if its because this video is older, but I had to force train valid and test to be panda dataframes because when we did np.split() it ended up turning the three into numpy objects instead of pd objects, and columns is not a numpy attribute

  11. Nice explanations…

  12. I want learn more about machine learning please help me

  13. Had to learn ML and had no idea where to start, how to start, though I had heard of certain terms and knew bits and pieces (and was confused). This has given me a good overview of the basics and the basic concepts and contents of ML are sort of organized in my mind now with the "what is ML?", "what are the basic methods" and most importantly, "why the methods" and "How they work" information clearly explained and examples of how to use the methods on python, so that it is now easy to take off from here – A relatively comprehensive yet brief overview – a need of the hour for me right now as I cannot afford to spend weeks or months learning everything, but needed a good overview for my tasks at hand. Searched many sources but this seems to be one of the best in clarity. Thank you!

  14. Why do you use fit_transform on test data, when we should use only transform on it?

  15. Thanks for creating this excellent course….🙏🏻

  16. incredible, One of the few learning videos where I have to slow it down to .75x Speed just to keep up. LOL. Will have to play over a few time.

  17. like vì bn nữ cute

  18. This is almost impossible for a beginner to understand!

  19. nice one

  20. thank you kylie ying your a real life saver

  21. This gender identification thing getting out of hand

  22. mechinal voice mutlipurpse disorder in head?

  23. 13:07 its not outdated thats how it simply should be!!!

  24. Watched the lecture. Never expected this from you. 💙🤗

  25. How did she do multi cursor edit of her code ? Anybody know the keyboard shortcut for a mac ?

  26. i watch a lot of ml course and I believe this is best course.I don't blame on other teacher.My learning style is familier with this course

  27. You don't know how to teach…

  28. I just finished watching the entire video, taking a few days off to rest. Meanwhile, I also typed out all the code on Colab, mimicking what was shown. Overall, this video gave me a preliminary understanding of machine learning and opened the door to this unknown field for me. Of course, there is still a lot I need to learn, and many concepts are not very clear to me yet. However, with the prevalence of ChatGPT now, I can ask questions whenever I don't understand something. Compared to the previous technical environment, ChatGPT has made my learning process faster and more efficient. Finally, I would like to thank the blogger again. Perhaps, in the not-too-distant future, I might also make videos to guide others in getting started, haha.

  29. I appreciate you helping people make money from trading. Your work really matters!

  30. its not for everybody, lot of blather about numpy

  31. am I the only one who thinks the probability calculation was wrong? >_<

  32. is it good for beginners? I know basic python though

  33. What course is this?

  34. I do not think this is a beginner friendly tutorial. This needs quite a lot of background knowledge as prerequisites on Python, Numpy, Pandas, SciKitLearn, Probability, Charts, and so on.

  35. How to best imagine the hyper plane for support vector machine?

  36. Anyone knows the name of note app?

  37. 我是中文用户为什么睡觉也给我推荐这个视频😂

  38. Thank You for a great course

  39. Please do a course on xml 🙏🙏🙏🙏

Leave your thought here

Your email address will not be published. Required fields are marked *

Enable Notifications OK No thanks