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Best Books To Learn Machine Learning For Beginners And Experts

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  • Difficulty Level : Expert
  • Last Updated : 25 Nov, 2019
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Alan Turing stated in 1947 that “What we want is a machine that can learn from experience.
And this concept is a reality today in the form of Machine Learning! Generally speaking, Machine Learning involves studying computer algorithms and statistical models for a specific task using patterns and inference instead of explicit instructions. And there is no doubt that Machine Learning is an insanely popular career choice today. According to Indeed, Machine Learning Engineer Is The Best Job of 2019 with a 344% growth and an average base salary of $146,085 per year.

Keeping this in mind, if you want to learn Machine Learning, there are many books available in the market (for programmers at all stages of learning). In this article, we have compiled the best books for ML, both for rank amateurs and technical whiz kids!!! Each of these books is extremely popular so it is up to you to choose the ones you like according to your learning sensibilities. So without further ado, let’s see them!

Books For ML

First, let’s start simple and focus on the best Machine Learning books for beginners and then we will move on to more complicated books!

Best Machine Learning Books for Beginners

1. Machine Learning For Absolute Beginners: A Plain English Introduction (2nd Edition)


Author: Oliver Theobald

You want to learn Machine Learning but have no idea how? Well, before you embark on your epic journey into machine learning, there are some important theoretical and statistical principles you should know first. And that’s where this book comes in! It is a practical and high-level introduction to Machine Learning for absolute beginners.
Machine Learning For Absolute Beginners teaches you everything basic from learning how to download free datasets to the tools and machine learning libraries you will need. Topics like Data scrubbing techniques, Regression analysis, Clustering, Basics of Neural Networks, Bias/Variance, Decision Trees, etc. are also covered. So, if you haven’t had that Lion King moment yet, where you proudly gaze on the expanse of ML-like Simba looks over the Pride Lands of Africa, then this is the best book to gently hoist you up and offer you a clear lay of the land. Buy Machine Learning For Absolute Beginners Book

2. Machine Learning (in Python and R) For Dummies (1st Edition)


Authors: John Paul Mueller and Luca Massaron

For common people, Machine Learning can be a mind-boggling concept. But for those of us in the know, it is invaluable!!! It is impossible to handle things like web search results, real-time ads on web pages, automation or even spam filtering (Yeah!) without ML. And so this book provides you with a no-nonsense guide that can serve as an entry point into the mysterious world of ML.
Machine Learning For Dummies will help you to ‘speak’ certain languages, such as Python and R that will, in turn, teach machines to handle pattern-oriented tasks and data analysis. You will also learn how to code in R using R Studio and in Python using Anaconda. Buy Machine Learning For Dummies Book

3. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started (1st Edition)


Authors: Drew Conway & John Myles

In case you are a programmer now interested in data crunching, then this book is perfect for you! (Lets first clarify that the Hacker in the title refers to a good programmer and not a secretive computer cracker!) So this book will help you get started with Machine Learning using lots of hands-on case studies rather than boring math-heavy presentations that are more common.
Machine Learning for Hackers focuses on specific problems in each chapter such as classification, prediction, optimization, and recommendation. It will also teach you to analyze different sample datasets and write simple machine learning algorithms in the R programming language. Buy Machine Learning for Hackers Book

4. Machine Learning: The New AI (The MIT Press Essential Knowledge Series)


Author: Ethem Alpaydin

Machine Learning has an insane range of applications in modern times, from product recommendations to voice recognition and even those that are not commonly used like self-driving cars! Now, the basis of ML is data and as data has grown bigger (Big data!), it is no surprise that ML has also advanced as it is fundamental in the process of converting data into knowledge.
Machine Learning: The New AI focuses on basic Machine Learning, ranging from the evolution to important learning algorithms and their example applications. This book also focuses on machine learning algorithms for pattern recognition; artificial neural networks, reinforcement learning, data science and the ethical and legal implications of ML for data privacy and security. Buy Machine Learning: The New AI Book

Best Machine Learning Books for Intermediates/Experts

1. Pattern Recognition and Machine Learning (1st Edition)


Author: Christopher M. Bishop

In case you want to dive deep into the mysterious world of Pattern Recognition and Machine Learning, then this is the correct book for you! In fact, this is the first book that presents the Bayesian viewpoint on pattern recognition. So while this book deals with tough topics that require at least some knowledge of multivariate calculus, basic linear algebra, and data science, this is also the best book to hammer Pattern Recognition into your brain!!!
Pattern Recognition and Machine Learning has increasing difficulty level chapters on probability and machine learning based on patterns in datasets. So this book starts from the general introduction in Pattern Recognition using live examples to get the point across. Buy Pattern Recognition and Machine Learning Book

2. Fundamentals of Machine Learning for Predictive Data Analytics


Author: John D. Kelleher

If you have understood Machine Learning basics and now want to get into Predictive Data Analytics, then this is the book for you!!! Machine Learning can be used to create predictive models by extracting patterns from large datasets. And this application of ML using Predictive Data Analytics is analyzed in detail in this book using both theoretical concepts and practical applications.
Even though the name “Fundamentals of Machine Learning for Predictive Data Analytics” is a mouthful, still this book will describe the Predictive Data Analytics trajectory in detail: from data to insight to decision. It also describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning, each with a nontechnical conceptual explanation followed by mathematical models and algorithms illustrated by detailed worked examples. Buy Fundamentals of Machine Learning for Predictive Data Analytics Book

3. Machine Learning: The Art and Science of Algorithms that Make Sense of Data (1st Edition)

Machine-Learning-The-Art-and-Science of Algorithms

Author: Peter Flach

If you are at the intermediate or expert level in ML and want a “back to the basics” approach, then this book is the way to go! It does full justice to the incredible complexity and richness of Machine Learning and without losing sight of its unifying principles (And that’s a feat!).
Machine Learning: The Art and Science of Algorithms has various case studies with increasing complexity and many examples and illustrations as well (To make sure it’s not boring!) Also, a wide range of logical, geometric and statistical models are covered in the book along with complex and new topics like matrix factorization and ROC analysis. Buy Machine Learning: The Art and Science of Algorithms that Make Sense of Data Book

4. Programming Collective Intelligence: Building Smart Web 2.0 Applications (1st Edition)

Programming Collective Intelligence: Building Smart Web 2.0 Applications

Author: Toby Segaran

Do you want to understand and then harness the power behind search rankings, product recommendations, social bookmarking or even online matchmaking!!! If you do, then congratulations, you have chosen the correct book. This book demonstrates how you can build various applications for Web 2.0 to mine the enormous amount of data that is created by approximately 3 Billion people on the Internet.
Programming Collective Intelligence handles this using Machine Learning and helps you draw conclusions about user experience, marketing, personal tastes, and human behavior in general. All of the Machine Learning algorithms in this book are described with the code that can be used anywhere from your web site, blog, Wiki, or even a specialized application. Buy Programming Collective Intelligence Book

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