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.
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.
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.
Author: Ian H. Witten
Author: Toby Segaran
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.
Author: Giancarlo Zaccone
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.
Author: James A. Anderson
Author: Nick Bostrom
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.