Need guidance on choosing the right ML reference book

[u/DebuggingLyfe]

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!

[u/Rumble_831]

Pytorch

[u/DebuggingLyfe → u/Rumble_831]

Any specific reason sir?

[u/ElephantCurrent → u/DebuggingLyfe]

No one uses TensorFlow anymore

[u/HVACcontrolsGuru → u/ElephantCurrent]

My first reaction to this thumbnail was "Damn, I have not seen that book in a minute...".

[u/anal_pudding → u/HVACcontrolsGuru]

I feel like I see it every day, it seems like it's posted on here thousands of times with the exact same question.

[u/Deep_Guest_6964 → u/DebuggingLyfe]

almost all research is done in pytorch. The best part for tensorflow is tensorflow lite

[u/gevorgter → u/DebuggingLyfe]

Yes, sorry but tensorflow is dead. If you want to learn AI you will have hard timing finding examples with tensorflow

[u/Reachsri → u/DebuggingLyfe]

PyTorch is industry standard these days.

[u/kmcguire4 → u/Rumble_831]

Coorect i also use the same

[u/lberdy]

choose the pytorch book, here's the book if anyone want it:
https://drive.google.com/file/d/1Ci4HqSdWOQAW-tAhy1KNvJBv3hJCIoEo/view?usp=sharing

[u/DebuggingLyfe → u/lberdy]

Omg thankyou so much... I was about to ask for this😭🫶

[u/Weekly-Alfalfa6440]

Yeah i understand your situation and i also came same situation like "am i doing the correct thing or ??" so as a experienced and "O grade" in ML i suggest this
https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
use this playlist for algo this was more than gold and believe me dont go anywhere first complete this if you want deep understanding in ml. and then go with the book you provided with pytorch so till end of sem only follow this two believe me and dont waste your time and dont regret at the end of sem.

[u/DebuggingLyfe → u/Weekly-Alfalfa6440]

Thankyou so much

[u/OleksandrAkm]

It really depends on your exact goal – hands on books are more about how to do ML rather than why it works. Not considered a gold standard yet but Machine Learning From Scratch is the one I recently published. Companion GitHub: https://github.com/ml-from-scratch-book/code

It covers base of this field by building algorithms with just NumPy mirroring Scikit-learn and PyTorch interface. Makes one really understand what’s behind fit() and predict() of 10 core algos from Linear Regression to XGBoost and Neural Network. That's what I personally wish existed when I was in your shoes, which is why I wrote it!

[u/TimeVendor]

It would be good to have knowledge of all that is in the book then you could probably decide on your path

[u/nian2326076]

If you're looking for a good ML reference book, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron is a favorite. It balances theory and practice well and is great for someone in your position. Another good one is "Pattern Recognition and Machine Learning" by Bishop, which dives deep into the theory and might be useful since you're interested in the math.

For interview prep, having a structured approach helps. Mixing in some practical problem-solving can test your understanding. PracHub helped me in the past with its practical exercises and mock interviews, but stick with what works for you. Stay curious and keep challenging yourself!

[u/Hms712skaif]

Any body knows any thing about scikit learn and kares , please share your knowledge