Book Image

Python Machine Learning - Third Edition

By : Sebastian Raschka, Vahid Mirjalili
Book Image

Python Machine Learning - Third Edition

By: Sebastian Raschka, Vahid Mirjalili

Overview of this book

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. 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 working 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, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This 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.
Table of Contents (21 chapters)

Get started with machine learning

If you want to become a machine learning practitioner or a better problem solver, or maybe you are even considering a career in machine learning research, then this book is for you! For a novice, the theoretical concepts behind machine learning can be quite overwhelming, but the many practical books that have been published in recent years will help you to get started in machine learning by implementing powerful learning algorithms.

Practice and theory

Being exposed to practical code examples and working through example applications of machine learning are great ways to dive into this field. Also, concrete examples help to illustrate the broader concepts by putting the learned material directly into action. However, remember that with great power comes great responsibility!

In addition to offering hands-on experience with machine learning using the Python programming language and Python-based machine learning libraries, this book introduces the mathematical concepts behind machine learning algorithms, which are essential for using machine learning successfully. Thus, this book is different from a purely practical book; this is a book that discusses the necessary details regarding machine learning concepts and offers intuitive, yet informative, explanations on how machine learning algorithms work, how to use them, and, most importantly, how to avoid the most common pitfalls.

Why Python?

Before we dive deeper into the machine learning field, let's answer your most important question: "Why Python?" The answer is simple: it is powerful, yet very accessible. Python has become the most popular programming language for data science because it allows us to forget the tedious parts of programming and offers us an environment where we can quickly jot down our ideas and put concepts directly into action.

Explore the machine learning field

If you type "machine learning" as a search term into Google Scholar, it will return an overwhelmingly large number—3,250,000 publications. Of course, we cannot discuss all the nitty-gritty details of all the different algorithms and applications that have emerged in the last 60 years. However, in this book, we will embark on an exciting journey, covering all the essential topics and concepts to give you a head start in this field. If you find that your thirst for knowledge is not satisfied, you can use the many useful resources that this book references to follow up on the essential breakthroughs in this field.

We, the authors, can truly say that the study of machine learning made us better scientists, thinkers, and problem solvers. In this book, we want to share this knowledge with you. Knowledge is gained by learning, the key to this is enthusiasm, and the real mastery of skills can only be achieved through practice.

The road ahead may be bumpy on occasions, and some topics may be more challenging than others, but we hope that you will embrace this opportunity and focus on the reward. Remember that we are on this journey together, and throughout this book, we will add many powerful techniques to your arsenal that will help you to solve even the toughest problems the data-driven way.