Top 5: Best Machine Learning Books in Python

Machine Learning is the scientific study of algorithms, mathematics and statistical models used by computer systems to perform a particular task without giving explicit instructions. And mastering machine learning by reading just one book is impossible. Therefore, you will need to read a lot of machine learning books & practice a lot.

There are many books on machine learning that use Python as their primary implementation language. But in this post, we listed the top 5 books that are recognized by leaders, experts, and technology professionals in this field.



Best Python Machine Learning Books

Hands-on Machine Learning with Scikit-Learn, Keras & Tensorflow

Hands-on Machine Learning with Scikit-Learn, Keras & Tensorflow
Hands-on Machine Learning

Most probably the best book on machine learning with Python. It’s written by Aurélien Géron. This book is designed specifically for readers who are already familiar with Python programming. It’s a detailed and fast-paced introduction to Machine Learning essentials with Scikit-learn, Keras & Tensorflow. You will learn how to use these libraries to create intelligent ML models and systems. This book is also jam-packed with exercises to reinforce your understanding, and covers many machine learning topics with variety of complexity as well as Neural Networks & Deep Learning.

Machine Learning in Action

Machine Learning in Action
Machine Learning in Action

This book is written by Peter Harrington. It’s divided into 3 main topics – supervised classification, supervised regression, and unsupervised approaches. It goes into a lot of detail on these topics & compares many machine learning algorithms. As the book is mostly mathematically focused, those Python programmers with an applied mathematics background will find this one very interesting. Through the implementation of Hadoop, MapReduce and Amazon Web Services (AWS), the book also discusses the emerging field of “big data.”

What actually Machine Learning is? What Programming languages are best for performing ML Algorithms? What are the best books? Got a lot of questions like this? Click View More.

Data Science from Scratch

Data Science from Scratch
Data Science from Scratch

This book is written by Joel Grus. This book starts with a crash course in Python. And then covers the basics of linear algebra, statistics, and probability and explain how and when they’re used in data science & machine learning. Then dives into the fundamentals of machine learning & implementation some models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering. Have some projects on recommendation systems, NLP, network analysis, MapReduce etc. It’s a very good book to understand machine learning basics.

Building Machine Learning Systems with Python

Building Machine Learning Systems with Python
Building ML Systems with Python

This book is written by Willi Richert & Luis Pedro Coelho. It introduces the use of scikit-learn & other Python scientific libraries for regression and classification tasks in a significant detail. And spends a lot of time at text-based classification and sentiment analysis, which is a very hot topic nowadays. In a recommendation case, the book also discusses the use of regression, which is likely more applicable to data scientists and customer analytics engineers. But beginners may find this book not that easy as is it is designed for the python expert.

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow
Python Machine Learning

This book is written by Sebastian Raschka & Vahid Mirjalili. This book focuses on a practical approach to key frameworks in data science, machine learning, and deep learning. It uses the most powerful Python libraries to implement machine learning and deep learning (Such as TensorFlow, Scikit-learn & Keras). And has a detailed explanation on topics like regression analysis, clustering, sentiment analysis, deep convolutional neural networks, embedding a Machine Learning Model into a Web Application and so on.

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