Humans are granted with incredible power by machine learning. The task is run in an automated manner, it also makes our life comfortable and things are also improved at a large scale and it also has the power to create a tool who thinks well than the humans. This article is mainly to make you aware about the world which is beyond the video tutorials and blogs. Here are some books through which a beginner can go.
This book is written by the great Andrew Ng. this book is still not completed and is in progress, but many chapters have been released and they can get easily downloaded for free. If you want to speed up in building the artificial intelligence system, this book will give you the right way as it teaches you how to make decisions which are required with organizing a project of machine learning. the updates are on the site for the releasing of a new chapter.
If you want to start learning machine learning, this is the best-known book as this is the book which should be given the priority while going through machine learning books. There are data scientists who have read this book multiple times. This book was written very long back but the topics are much more relevant to today's time. There are many topics which are the collaborative filtering techniques, search engine features and support vector machines. The book uses Python to deliver machine learning in a fascinating manner.
Drew Conway and John Myles White wrote this book. In R, it is majorly based on data analyst. Having a basic knowledge and a grasp of R is best suited for beginners. In data wrangling, the use of advanced R is covered. It has case studies which are interesting which will help you to understand the machine learning algorithm.
This book is quite popular among all, which was written by Trevor Hastie, Robert Tibshirani, and Jerome. Various algorithms of machine learning are explained mathematically from a statistical perspective. The world which is created by statics and machine learning is also provided. It also lays emphasis on the mathematical derivations which define the underlying logic behind an algorithm. In this book, you will find the beginners friendly versions by some of the same authors, which is called introduction to the statistical language.
Yaser Abu Mostafa, Malik Magdon Ismail, and Hsuan-Tien Lin wrote this book. Machine learning introduction is provided, which is perfect. Through this book, you can understand the complex areas of machine learning. The information is provided which is to the point and no extra or incorrect explanation is provided. Go through the online tutorial of Yaser Abu Mostafa as well.
Christopher M Bishop has written this book. This book serves as an excellent reference for students keen to understand the use of statistical techniques in machine learning and pattern recognition. This book assumes the knowledge of linear algebra and multivariate calculus. It provides a comprehensive introduction to statistical pattern recognition techniques using practice exercises.