10 Free Must-Read eBooks About Machine Learning


ML has the ability to solve complex problems accurately, reliably and quickly.

In the midst of all the excitement around Big Data, we keep hearing the term “Machine Learning”. It not only offers a lucrative career, but it also promises to solve problems and support businesses by forecasting and helping them make sound decisions. Today, we’re going to find out about the 10 Must-Read Free Machine Learning eBooks in this article.

Python machine learning

Python Machine Learning is one of the most popular ML books of the past decade. This book is an essential addition to anyone’s ML and AI learning plan, as it walks you step-by-step through the data pipeline and shows you how to use the leading machine and learning libraries in depth, such as scikit-learn and TensorFlow.

An introduction to statistical learning

This book introduces statistical learning and explains how to use revolutionary statistical and ML approaches in a simple and intuitive way.

Address (almost) any machine learning problem

This book is for those who have a basic understanding of ML and deep learning and want to learn how to apply it. The book is more interested in how and what can be used to solve ML and deep learning problems, rather than explaining algorithms. If you are looking for pure fundamentals, this book is not for you. Instead, it’s for those who want to learn how to solve ML problems.

Desire for machine learning

The focus of this book is on how to structure ML projects. It describes how to implement machine learning algorithms. After reading it, you will be able to recognize and prioritize the most beneficial aspects of your AI programs, spot errors in your ML systems, and perform various other essential activities.

Understanding Machine Learning: From Theory to Algorithms

This book is a logical next phase because it is newer, deeper and more advanced. This will deepen more algorithms and their explanations, as well as include a link to the practical aspect. The emphasis on theory should remind beginners how important it is to really understand what drives machine learning algorithms. The Advanced Theory section contains several ideas that might be beyond the reach or desire of a beginner, but it is available for review.

Advanced Python machine learning

Advanced Python Machine Learning will walk you through some of the most revolutionary techniques in the business if you’re looking for another book to challenge and guide you. This will not only help create even better Python ML solutions, but it will also help with a better understanding of the language. As a result, you will have a better grasp of one of the fastest growing languages ​​in the world.

Bayesian reasoning and machine learning

ML is discussed in this book using Bayesian statistics. If you are considering a career in ML, this book is a must read. It is also a vital branch for any budding Data Scientist. The integration of conditional probabilities and the modification of these probabilities as new information is presented within the framework of Bayesian reasoning.

Convenient machine learning with Scikit-learn and Tensorflow

Two of the most common Python libraries for machine learning and deep learning are Scikit-learn and Tensorflow. This book not only provides a clear description of the ML system in general, but it also explains how to use these two methods in practice.

Deep learning Python

The tip of ML is Deep Learning. In a simple way, it’s about machine learning with more complexity and experience, which can then be used to power different types of AI.

Python Deep Learning can build on existing Python and Machine Learning skills to create more complete deep learning models that can be used in a variety of applications, such as image recognition and games.

Machine Learning Practice Cookbook

This book is intended for developers, data analysts, scientists and statisticians who have a basic understanding of ML and statistics and who need help dealing with the difficult scenarios they face on a daily basis while working. in the field of ML to improve the efficiency and accuracy of the system. As a reader it is assumed that you are familiar with mathematics. A basic understanding of R is required.

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