Machine Learning with Pytorch and SciKit Book

Last week another book on Machine Learning with Pytorch and Sci-kit learn was released by Packt written by Sebastian Raschka, Yuxi Liu & Vahid M. et.al.

In the plethora of literature on machine learning and data science, one might ask, why just another book? There are medium articles, Coursera courses, youtube videos, and coding boot camps. Did we need just another book?

I exactly had that in my mind when I received the early review request from Divya Mudaliar but it took just a chapter before I was sold its worth it. Instead of diving straight into dataframes and algorithms, the book builds up the rationale for why you should invest the time to read it. You can figure that out by the name of the very first chapter “Giving Computers the Ability to Learn from Data”

From implementing your first artificial perception network on Iris dataset to discussing non-linear methods & kernel tricks, the book has it all you can possibly encounter in your job as a data scientist & hopefully beyond.

It is a beefy book to be honest with over 700 pages so you better go slow and steady but it is worth the time and dime spent reading it. Authors make it just a tad bit more complex with every chapter but manageable nonetheless. They leave no stone unturned in ML topics. Here is a brief but not exhaustive list:

1. RNNs
2. Transformers,
3. Multilayer ANNs
4. Data Processing
5. Hyperparameter Tunings
6. Search Spaces
7. Dimensionality Reduction & Regularization
8. Ensemble Learning
9. Reinforcement Learning

If you have the time and thirst for it, go get it. You won’t regret it! Congratulations to everyone in the process for producing such a jewel.