Now that you have learned and practiced all the basics of machine learning, you want to read something advanced or next-level in the topic.
So, below are some recommendations for you:
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started (1st Edition)
This book is for you if you are a programmer interested in computing. As a result, this book contains many practical examples rather than dull math presentations. Begin by studying ML. It’s more typical.
Each chapter focuses on various concerns such as classification, prediction, optimization, and suggestions. It also explains how to use the R programming language to analyze several sample datasets and develop simple machine learning algorithms.
Machine Learning: The New AI (The MIT Press Essential Knowledge Series)
Recently, machine learning has been employed in bizarre applications ranging from product suggestions to speech recognition to applications that aren’t as well-known as self-driving automobiles.
Data is now the base of ML. Thus, it’s no surprise that as data grows larger (big data! ), ML, the foundation for transforming data into knowledge, has grown as well.
It focuses on the most critical aspects of machine learning, from evolution through key learning algorithms and applications.
In addition, the book discusses machine learning techniques for pattern recognition.
Artificial neural networks, reinforcement learning, data science, and data protection and security are affected by machine learning’s ethical and legal consequences.