Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features
Learn applied machine learning with a solid foundation in theory
Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices
Book Description
Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.

Why PyTorch?

PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.

You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).

This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn
Explore frameworks, models, and techniques for machines to 'learn' from data
Use scikit-learn for machine learning and PyTorch for deep learning
Train machine learning classifiers on images, text, and more
Build and train neural networks, transformers, and boosting algorithms
Discover best practices for evaluating and tuning models
Predict continuous target outcomes using regression analysis
Dig deeper into textual and social media data using sentiment analysis
Who this book is for
If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.

Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.

Table of Contents
Giving Computers the Ability to Learn from Data
Training Simple Machine Learning Algorithms for Classification
A Tour of Machine Learning Classifiers Using Scikit-Learn
Building Good Training Datasets - Data Preprocessing
Compressing Data via Dimensionality Reduction
Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Combining Different Models for Ensemble Learning
Applying Machine Learning to Sentiment Analysis
Predicting Continuous Target Variables with Regression Analysis
Working with Unlabeled Data - Clustering Analysis
(N.B. Please use the Look Inside option to see further chapters)