10 Books Suggestion For Machine Learning | Good Books To Read

Reading is important because it enables and develops the mind. A good book can reduce your efforts. that’s why we categorize books selection in two choices, selection first for beginners and another is for practical purpose. Although beginners can select a proper machine learning based on personal interest on basis of types of machine learning field. Sciencetrap.com is making a list of Machine learning books for beginners and practical purpose.

1.Machine Learning for Hackers


This book helps you started with machine learning. The book introductory text helps to those who are not from machine learning background. The authors of the book Drew Conway and John Myles White introduced to many of techniques useful for making systems that can easily recognize and make use of data. the book also explains roles in R language, and parse strings. the code and data existing on this book would be very useful. The chapters of the books are focused on problems in machine learning. R is essential to work with the examples. The book covers data exploration, spam filtering, statistics, predictions, introduction to R and similar techniques.    

2.Machine Learning


This is an introductory book on Machine Learning. Area of machine learning such as Neural Networks, Genetic Algorithms, Reinforcement Learning is covered in this book. The author presents the basic concepts of machine learning in a structured way. The book was first published on 30 April 1986. The chapters of this book explain decision tree learning, artificial neural networks, Bayesian learning, computational learning theory, learning sets of rules and good detail for most of the algorithms.

3.Learning From Data

You can start with reading this one. Authors of the book put the basics of the subject, you can easily understand. This book is mostly recommended by the practitioner. it provides a short course in Machine Learning. the combination of theoretical and the practical is well-adjusted. the book contains algorithms and code that you can put into a data set.

4.Data Mining: Practical Machine Learning Tools and Techniques

Author: Ian H. Witten

this book provides practical examples for machine learning. The book is focused on data mining. The book introduces WEKA machine learning workbench. WEKA is a great tool.

5.Programming Collective Intelligence

Author: Toby Segaran

The programming collective intelligence code references and libraries have been updated. This book puts concepts with interesting ideas. You can understand many examples in one place.  The practical guide and east explanations on machine learning. choice of machine learns algorithm solving problems such as clustering, classification, and optimization. You can buy this book from here.   The book introduces lots of algorithms and the logic of the explanations are great.

6.Applied Predictive Modeling

Author: Max Kuhn, Kjell Johnson

This book provides an introduction to predictive models. And a proper guide to applying predictive models. It deals with many exciting research and professional fields. Predictive modeling is a subfield of data science. The examples provide on books help to the readers. With the help of this book, you can predictive modeling in practice.

7.Getting Started with TensorFlow

Author: Giancarlo Zaccone

After Google’s TensorFlow engine you might be want to understand the tensorflow. This book helps to the various aspects of tensor flow. Starting the learning path for the robust, and customizable library of machine learning.

8.Deep Learning (Adaptive Computation and Machine Learning series)

Author:   Ian Goodfellow


At the current state, the introduction for Machine learning is easily available on the internet. The book covers various popular modern neural architectures. This book has been systematized into three parts. Part I of the book is mostly review, basic mathematics, for Machine Learning. Part II is about strategies, deep learning algorithms. And part III is more for research and neural models.

9.An Introduction to Neural Networks (Bradford Books)

Author: James A. Anderson

Books are really important for us. books are helpful to spread knowledge (collected in a long time) to others in short period. The details about the Artificial Neural Networks are good in this book. The author of the book James A. Anderson effectively explains Based on notes. This book is for cognitive science and neuroscience students. 

10.Superintelligence: Paths, Dangers, Strategies

Author: Nick Bostrom

if machine brains beat human brains in intelligence, then this new superintelligence could replace humans for lifeform on Earth. the author of the book Nick Bostrom is the philosopher who argues with the understanding the future of humanity. The human brain has more capabilities than others. Elon Musk agreed with this book that artificial intelligence is potentially more dangerous than nuclear weapons. However recently Elon Musk announced a company Neuralink to merge human and computer brain for superintelligence.

See more: Beginner’s Guide To Machine Learning


Make sure you understand the mathematics really well for machine learning. many peoples suggest that doing machine learning in R is very helpful.

One thought on “10 Books Suggestion For Machine Learning | Good Books To Read

  • July 2, 2017 at 4:02 PM

    The phenomenon of Machine Learning and Artificial Intelligence, is thoroughly covered in the books mentioned below.


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