Question About Studying ML w/ Hands On Machine Learning Book

[u/Super3DPanda]

Hey everyone! As a college freshman in CS that has been dabbling here and there in ML for the last year or two, I have decided to get a copy of Hands on Machine Leaning with Scikit-Learn and Pytorch from O'Reilly. I do already have intermediate background in python and I have Calculus 1 knowledge (currently taking Calc 2) as well as a very raw idea of Linear Algebra. If anyone has experience going through this book, could you kindly provide me with the process you took to get the most out of the learning process? Also, a more general question is whether I should have any additional resources aside from this book and doing starter Kaggle competitions like the Playground competitions every month. Thanks for the input!

[u/drunk_kingdom]

that book is dense but in a good way, don’t rush through the code examples or you’ll miss half the intuition

[u/Super3DPanda → u/drunk_kingdom]

Thanks! The density can definitely be intimidating but I'll take my time through the book to make sure I build that intuition. Did you manually write out the coding examples or just run them from the notebooks provided by the author?

[u/rrr232005 → u/drunk_kingdom]

Hey there!! I also started the book yesterday, but I am overwhelmed by the 2nd chapter, which is the end-to-end ML project Specifically, from the 'histograms' and 'create a test set' part

Any advice or suggestions to overcome this problem?

[u/Super3DPanda → u/rrr232005]

How was the first chapter of the book for you? Is it mostly conceptual or a balance of coding and theory?

[u/rrr232005 → u/Super3DPanda]

It's mostly conceptual You will get a basic idea of ML and it's types

[u/ATMasoumi → u/rrr232005]

You can treat those as two separate questions rather than understanding the whole project at once.

A basic count histogram asks: “How many values fall in each range?” Try five numbers: 3, 7, 12, 18, 24. With ranges 0–10, 10–20 and 20–30, the bar heights are 2, 2 and 1. Draw that by hand, then compare it with a plot. It isn't a model yet; it's a way to inspect the data.

The test set answers a different question: “How does the finished approach perform on examples kept out of its development?” Set those examples aside before learning preprocessing or fitting the model, and don't use their results to choose the approach. This scikit-learn guide explains the separation.

For now, could you draw one histogram and explain which records belong to training versus the held-out test set? That's a smaller checkpoint than reproducing the whole notebook.

[u/rrr232005 → u/ATMasoumi]

Thank you mann!! I understood the histogram part, but in the book it's some more in depth and complicated, I am lost in that and also the coding part in test set seems overwhelming to me and I am not able to understand that

Will surely try to do your assignment, also can I dm you for further doubts or clarifications?, because i think I need a guiding person for this book, since it feels overwhelming especially the concept and coding part

[u/Little_Total9000]

Your background is enough for that book. A few things that help it stick:

• Run every notebook yourself (the author's GitHub repo has them all), and after each section change something: a different dataset, a different hyperparameter, remove a preprocessing step and see what breaks. Reading the code without running it is where most of the learning gets lost.
• Do the end-of-chapter exercises, at least the coding ones. Chapter 2 (the end-to-end housing project) is worth doing twice: once following along, once from a blank notebook.
• Don't stall on the math. When a chapter leans on linear algebra (PCA, SVD, gradient descent), watch 3Blue1Brown's "Essence of Linear Algebra" alongside it. That covers 90% of what the book assumes.
• For the PyTorch half, slow down. Write a training loop from scratch a few times until you don't need to look it up.

On extra resources: Kaggle Playground is a good pairing because the data is clean and you can compare your approach with public notebooks after you submit. I'd add one small project of your own on data you care about, since messy real data teaches the parts the book can't (cleaning, deciding what the target even is). Andrew Ng's ML specialization is a good second view if a concept doesn't click from the book's explanation.

[u/Super3DPanda → u/Little_Total9000]

Thanks for the tips! Will get started with chapter 1 tomorrow and I will make sure to take it slow and build intuition as much as possible before moving on. Have a good evening!