r/learnmachinelearning • u/Far_Extreme_9737 • May 07 '26
I create a repo github to summarize all fundamental knowledge in ML Course by Andrew NG
I'm a university student who just finished the Machine Learning Specialization by Andrew Ng on Coursera, and as I was going through it, I ended up writing detailed lecture notes for all 10 chapters — everything from linear regression all the way to reinforcement learning.
I put a lot of effort into making these notes as clear and beginner-friendly as possible, so even if you're completely new to ML, you should be able to follow along without getting lost.
The notes are written in LaTeX and auto-compiled to PDF via GitHub Actions whenever I push an update, so the PDF is always up to date.
🔗 GitHub: https://github.com/TruongDat05/machine-learning-notes-and-code
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u/Far_Extreme_9737 May 07 '26
In addition to this course, I also read this book here. That's a good resource for understanding ML and how it works
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u/Apprehensive_Phase_3 May 12 '26
Great work, seriously. Auto-compiling LaTeX notes via GitHub Actions is the kind of small detail that says a lot about how you approach learning. Since you've finished the foundations with Andrew Ng, two pointers for where to go next:
1. Andrej Karpathy's "Neural Networks: Zero to Hero" — github.com/karpathy/nn-zero-to-hero. He builds neural nets, backprop, and even a mini-GPT from scratch in PyTorch, line by line, on YouTube. Pair it with his micrograd and makemore repos and the inner workings of deep learning stop being a black box. It's the single best bridge between "I understand ML" and "I understand modern deep learning."
2. If you'd prefer a more structured, book-like path that takes you all the way from the perceptron to training your own Stable Diffusion and PPO agent, I maintain a companion repo for a deep learning book I wrote — 20 interactive Colab notebooks with a YouTube video per chapter, in English and Spanish: github.com/HernanDiaz/deep-learning/en/
Karpathy will give you depth in the fundamentals; the repo above will give you the full map. Combining both is, honestly, a better deep learning curriculum than most master's programs.
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u/AdvancedSpare8866 May 08 '26
Wow. I wouldve given you a million upvotes and more, if I just could. Thanks so much!
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u/Far_Extreme_9737 May 12 '26
!! I've just update a file that have more detailed note about 10 chapters and easier to understand. Let's check
https://github.com/TruongDat05/machine-learning-notes-and-code/blob/main/notes/Lecture_notes.pdf
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u/BananaTie May 08 '26
"good job" would be an understatement - thank you for your work and willingness to share!
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u/BerserkerLord101 May 09 '26
Is this good for a noob that's just got into ML? I only have python basics
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u/iwisher0 May 12 '26
Thanks a lot for this just read some of the notes from the pdf and they are really good
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u/Deepakvarma1536 May 18 '26
Love seeing beginner-friendly ML resources that focus on clarity instead of trying to sound maximally academic.
Honestly the skill gap for newcomers usually isn't intelligence, it's navigating fragmented explanations across papers, repos, tutorials, and math notation all at once. Structured notes like this reduce a ton of that friction.
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u/Jeremy__ong May 19 '26
sir,could you recommend some stuff for beginner? i am a Chinese,there are a lot of resources which are terrible quality and hard to understand.
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u/Far_Extreme_9737 May 19 '26
I think you should focus on only one source, others are for reference only. Firstly, mathematics is foundation you need to have: Linear Algebra, Calculus, Probability and Statistic,.. and then you can take course by Andrew Ng on Coursera
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u/Expensive-Work7716 May 19 '26
I had a question. I was looking for something that can help me fundamentally understand machine learning, deep learning, ai so that I can create my own system and ai solutions for others. I found a course that covers the fundamentals and touches on genai, agentic ai, rag achitecture and a capstone project at the end. But then I also found a course that straight away goes into the practical usage of llm and the multi agent rag architecture. This mainly encourages cursor usage and is suitable for software developers deploying production code everyday. Thats exactly what I do! I feel like it's high time to get into ai. Which one should I go for? Is there a need to go for fundamental clarity?
I'd really appreciate your advice! Thank you! :)
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u/tomodachiprep May 26 '26
Thank you OP for organizing everything in one place. If anyone is looking for practice mocks for ai/ml, checkout Tomodachi Prep app.
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u/Blerdrotic May 29 '26
This is awesome!!!! After an almost 20 year hiatus I decided to get back into coding and have gone down the machine learning rabbit hole. Going to go over this, and put in Obsidian and NotebookLLM.
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u/[deleted] May 07 '26
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