Statistical Learning Or Machine Learning first?

[u/Aljariri0]

ISLP book, I finished the first 2 chapters, but this book is not easy, and I want some guys to study this book together. Any tips to study this book?

[u/siegevjorn]

Best strategy for studying ML right now is a top down approach. It takes too long to study all the breadth of knowledge to the depth it requires to build the foundation of ML. And then there is DL. By the time you finish buidling knowledge you need multivariable calculus, linear algebra, probabilty theory, statistics, information theory, optimization theory, and numerical analysis studied.

Frankly some important concepts are not relevant anymore. Like kernel SVM, quite difficult to derive since you need depth in optimization, is not being used anymore. For tabular data, xgboost is the go-to algorithm.

But all those concepts are built in modern frameworks. Numpy, Scikit learn, scipy, pytorch, tensorflow, and jax. Just learning to use these tools takes substantial amount time for individuals.

And in production, the application field is moving so fast and it's becoming more important to make a useful product out of the tech stack.

[u/Aljariri0 → u/siegevjorn]

so i can said skip this book, and maybe study book like hands-on-machine learning by Keras and TensorFlow ??

[u/siegevjorn → u/Aljariri0]

No. That book is quite outdated.

[u/Aljariri0 → u/siegevjorn]

bro there is 3rd version, and new version with PyTorch

[u/siegevjorn → u/Aljariri0]

Check the published date yourself and get the latest one. 2022 is ancient old.

[u/Lamarour → u/siegevjorn]

There is one published in 2025, currently reading it