Still Sceptical About AI & ML? Get Started With These Machine Learning Books

Machine learning has gifted humanity the power to run tasks in an automated manner. It allows us to improve things that we already do by studying a continuous stream of data related to that same task. In this article we have short-listed some Machine Learning Books.

‘Machine learning is a core, transformative way by which we’re rethinking everything we’re doing. We’re thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. We’re in the early days, but you’ll see us in a systematic way think about how we can apply machine learning to all these areas.’ - Sundar Pichai, CEO, Google

Machine learning has a wide range of applications that belongs to different fields,i.e. From space research to digital marketing. Machine learning is a complex field but that doesn’t mean that it can’t be learned.

Here Are Some Must-Read Machine Learning Books.

1. The Hundred-page Machine Learning Book

This book by Andriy Burkov explains the various topics of machine learning topics in a mere 100 pages. Written in an easy-to-follow manner, the machine learning book is endorsed by reputed thought leaders to the likes of the Director of Research at Google, Peter Norvig, and Sujeet Varakhedi, Head of Engineering at eBay. Post a thorough reading of the book, the reader will be able to build and appreciate complex Artificial intelligence systems, clear and ML-based interviews, and even start their own ml-based business.

2. Programming Collective Intelligence

This book is counted among the best books to begin understanding machine learning, the Programming Collective Intelligence by Toby Segaran was written in the year 2007. The book makes use of Python as the mode of delivering the knowledge to its readers.

3. Machine Learning For Hackers

The Machine Learning for Hackers by Drew Conway and John Myles White, is meant for the experienced programmer interested in crunching data. The word hacker here refers to adroit mathematicians. Rather than delving deeper into the mathematical theory of machine learning, the book explains numerous real-life examples to make learning ml simpler and faster.

4. Machine Learning

Written by Tom M. Mitchell, this book is a fitting book for getting started with machine learning. It offers an overarching view of machine learning theorems with summaries of the respective algorithms. The Machine Learning book is full of examples and case studies to help the reader in learning and grasping ml algorithms easily.

5. The Elements Of Statistical Learning

This book focuses on mathematical derivations to define the underlying logic of an ml algorithm. Written by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning is a must-have book. It is recommended to have a basic understanding of linear algebra before choosing this book.

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6. Learning From Data

Instead of imparting knowledge about the various advanced concepts pertaining to machine learning, this book by Yaser Abu Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin prepares its readers to understand the complex machine learning concepts thoroughly.

7. Pattern Recognition And Machine Learning

This book was written by Christopher M. Bishop, and serves as a great reference for understanding and applying statistical techniques in machine learning and pattern recognition. A basic understanding of linear algebra and multivariate calculus are prerequisites for going through the machine learning book.

8. Natural Language Processing With Python

Written by Steven Bird, Ewan Klein, and Edward Loper, Natural language processing is the backbone of machine learning systems. It uses the Python programming language to guide the reader into using NLTK, the popular suite of Python libraries and programs for symbolic and statistical natural language processing for English and NLP in general.

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9. Bayesian Reasoning And Machine Learning

Bayesian Reasoning and Machine Learning is a must-read for anyone interested in entering the field of machine learning. Written by David Barber, there is no scarcity of well-explained examples and exercises in the Bayesian Reasoning and Machine Learning book.

10. Understanding Machine Learning

Written by Shai Shalev-Shwartz and Shai Ben-David, The Understanding Machine Learning book offers a structured introduction to machine learning. The book focuses on the fundamental theories and algorithmic of machine learning, and mathematical derivations.

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11. Hands-On Machine Learning With Scikit-Learn & TensorFlow

By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow- the author Aurelien Geron helps the reader gain an inherent understanding of the concepts and tools to build an intelligent system. The reader will learn a range of techniques, i.e. from simple linear regression and progressing to deep neural networks.

12. Building Machine Learning Powered Applications

The author Emmanuel Ameisen demonstrates the practical ML concepts with the help of various elements,i.e, code snippets, illustrations, screenshots, and interviews with industry leaders. The reader will learn the necessary skills required to design, build, and deploy applications powered by machine learning (ML) with the help of this book.

13. Grokking Deep Learning

Grokking Deep Learning teaches one to build deep learning neural networks from the basic level. In his engaging was, author Andrew Trask shows one the science under the hood, so to grok for oneself every detail of training neural networks.

14. Deep Learning With Python

Deep Learning with Python introduces the field of deep learning with the help of Python language. Written by Keras creator and Google AI researcher François Chollet, this book builds an understanding through explanations and practical examples.

15. Deep Learning

This book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is an introduction to a vast of topics of deep learning, covering mathematical and conceptual background, the deep learning techniques used in industry, and also research perspectives.

Conclusion

Machine learning is a trending career option these days. The future looks all bright and shiny for it. So, it is high time to jump into the scene and make a profitable, professional career out of it.

This article sums up the 15 best machine learning books that you can go through to advance in machine learning the way you want it. I hope you find it informative and helpful.