Need guidance on choosing the right ML reference book
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I'm currently in the second year of my undergraduate degree, and I'm really passionate about machine learning. I've been learning consistently over the past few months, mostly through free YouTube courses and documentation. So far, I've covered the core ML algorithms and I make sure to understand the underlying mathematics and intuition instead of just memorizing things. However, one thing I keep struggling with is the lack of proper guidance. Every few weeks I start questioning whether I'm following the right roadmap or if I'm missing something important. I feel like YouTube resources are great for getting started, but they often don't go deep enough or provide the structured learning I'm looking for. I've heard a lot of good things about Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron (3rd edition), and it seems to be recommended by many people as a solid reference book. I'm thinking of studying it thoroughly instead of jumping between random resources. My main confusion is this: Should I go with the TensorFlow/Keras edition, or should I use the PyTorch version instead? As someone still building a strong ML foundation, which ecosystem would be the better investment to learn first? I'd also really appreciate any advice from people who have already been through this stage. If you think there's a better book, a better roadmap, or something you wish you had known when you were starting out, I'd love to hear it. I'm still a beginner in the grand scheme of things, so any guidance or suggestions would be greatly appreciated. Thanks in advance! submitted by /u/DebuggingLyfe |
![I implemented the YOLO26n model inference from scratch using ARM64 Assembly Language (No framework) [P]](https://jobssz.com/wp-content/uploads/2026/07/wiyelkfpsifh1.jpeg)
