10 Popular Must-Read Free eBooks on Machine Learning – Analytics Insight

10 Popular Must-Read Free eBooks on Machine Learning – Analytics Insight

Machine LearningMachine LearningML has the ability to solve complex problems in an accurate, reliable, and timely manner.

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 making forecasts and assisting them in making smart decisions. Today, we will learn about the 10 Popular 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 last decade. This book is an essential addition to anyone’s ML and AI learning plan, as it walks you through the data pipeline step-by-step and shows you how to use the leading machine and Deep Learning libraries, such as scikit-learn and TensorFlow.

An Introduction to Statistical Learning 

This book presents statistical learning and explains how to use groundbreaking statistical and ML approaches in a straightforward and intuitive manner.

Approaching (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 concerned about how and what can be used to solve ML and deep learning challenges, rather than explaining the algorithms. If you’re searching for pure fundamentals, this book isn’t for you. Instead, it’s for those who want to learn how to solve ML problems.

Machine Learning Yearning 

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

Understanding Machine Learning: From Theory to Algorithms 

This book is a logical next phase since it is newer, deeper, and more advanced. This will dig into more algorithms and their explanations, as well as include a link to practicality. The emphasis on theory should serve as a reminder to beginners about how important it is to truly comprehend 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 take you through some of the most groundbreaking techniques in the field if you’re looking for another book to challenge and drive you. This will not only aid in the creation of even better Python ML solutions, but it will also assist in a better understanding of the language. As a result, you’ll have a stronger grasp of one of the world’s fastest-growing languages.

Bayesian Reasoning and Machine Learning

ML is approached in this book using Bayesian statistics. If you’re considering a career in ML, this book is a must-read. It’s also a vital branch for any aspiring Data Scientist. Integrating conditional probabilities and modifying these probabilities as new information is presented as a part of Bayesian reasoning.

Hands-on Machine Learning with Scikit-learn and Tensorflow 

Two of the most common Python libraries for machine 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 goes into how to use these two methods in practice.

Python Deep Learning 

ML’s cutting edge is Deep Learning. In simple way, it’s Machine Learning with more complexity and experience, which can then be used to power various types of AI.

Python Deep Learning can draw on existing Python and Machine Learning skills to create more comprehensive Deep Learning models that can be used in a variety of applications, such as image recognition and gaming.

Practical Machine Learning Cookbook 

This book is for developers, data analysts & scientists, and statisticians who have a basic understanding of ML and statistics and need assistance coping with the difficult scenarios they face on a daily basis when working in the area of ML to improve system efficiency and accuracy. As a reader, it is presumed that you are familiar with mathematics. A basic understanding of R is needed.


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