4 books on Machine Learning that you cannot miss in 2021

Machine learning has taken over the discussion on technology. With hitching the wagon to the buzz and learning the basics to professionals finding relevant applications and solving complex models, the learning spectrum is diverse. Here are 4 books that cater to everybody on their journey to better understanding machine learning.

Machine Learning: A Practitioner’s Approach

The book is authored by Vinod Chandra and Anand Hareendran S. The book’s target demography is largely the aspiring computer science engineering graduates. The book provides an introduction to machine learning and various techniques. The learning algorithms are given an in-depth approach. The book also substantiates the theory with important case studies and relevant examples.

Approaching (Almost) Any Machine Learning Problem

The book is authored by Abhishek Thakur. The book caters to people who have had a basic knowledge of deep learning and machine learning. The book helps in solving the problems with the algorithms rather than just giving a basic explanation. The book especially helps if stuck with machine learning problems.

Data Mining and Data Warehousing: Principles and Practical Techniques

The book is authored by Parteek Bhatia. Data mining and warehousing are the essentials to understanding cognitive technologies. Especially aspiring computer science graduates need to learn decision tree, distance metrics, associate mining and so on. The book caters especially to those aspirers. Chapters are dedicated to these topics and their practical application using R language data mining tools.

Machine Learning for Absolute Beginners

The book is authored by Oliver Theobald. The book is designed for beginners with plain English explanation of machine learning concepts. The best part: No coding experience necessary. The book also adds visual examples to make the reading engaging and easy. The book also includes chapters on Python to better understand a machine learning model.