Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

by Kyle Gallatin, Chris Albon

This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks. Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure that it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications. You'll find recipes

413 pages, Paperback

Published September 5, 2023

Reviews

[Zhuzi_20 · Rating 1 out of 5]

The introduction to machine learning can be accomplished with Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.
This book seems to lack value, as it mainly focuses on how to use PyTorch. The key is the thinking behind the knowledge, not the knowledge itself. PyTorch is just a tool; any tool can be chosen for use.