If You Are A Beginner Then Have Handy These Machine Learning Books To Gain Knowledge

By Jyoti Nigania |Email | Jun 27, 2019 | 3504 Views

Machine learning and artificial intelligence are growing fields and growing topics of study. While the advanced implementations of machine learning we hear about in the news might sound scary and inaccessible, the core concepts are actually pretty easy to grasp. Here is the list of the most popular resources for machine learning beginners. Some of these books will require familiarity with some coding languages and math.

1. "Machine Learning For Dummies" by John Paul Mueller and Luca Massaro
Authors: John Paul Mueller and Luca Massaro
While we're going with "absolute beginners," the popular "Dummies" series is another useful starting point. This book aims to get readers familiar with the basic concepts and theories of machine learning and how it applies to the real world. It presents the programming languages and tools integral to machine learning and illustrates how to turn seemingly-esoteric machine learning into something practical.

The book introduces a little coding in Python and R used to teach machines to find patterns and analyze results. From those small tasks and patterns, we can extrapolate how machine learning is useful in daily lives through web searches, internet ads, email filters, fraud detection, and so on. With this book, you can take a small step into the realm of machine learning.

2. "Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition)" by Oliver Theobald
Author: Oliver Theobald
The title is kind of explanatory, right? If you want the complete introduction to machine learning for beginners, this might be a good place to start. When Theobald says "absolute beginners," he absolutely means it. No mathematical background is needed, nor coding experience this is the most basic introduction to the topic for anyone interested in machine learning.

"Plain" language is highly valued here to prevent beginners from being overwhelmed by technical jargon. Clear, accessible explanations and visual examples accompany the various algorithms to make sure things are easy to follow. Some simple programming is also introduced to put machine learning in context.

3. "Programming Collective Intelligence" by Toby Segaran
Author: Toby Segaran
This is more of a practical field guide for implementing machine learning rather than an introduction to machine learning. In this book, you'll learn about how to create algorithms in machine learning to gather data useful to specific projects. It teaches readers how to create programs to access data from websites, collect data from applications, and figure out what that data means once you've collected it.

"Programming Collective Intelligence" also showcases filtering techniques, methods to detect groups or patterns, search engine algorithms, ways to make predictions, and more. Each chapter includes exercises to display the lessons in the application.

4. "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies" by John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy
Authors: John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy
This book covers all the fundamentals of machine learning, diving into the theory of the subject and using practical applications, working examples, and case studies to drive the knowledge home. "Fundamentals" is best read by people with some analytics knowledge.

It presents the different learning approaches with machine learning and accompanies each learning concept with algorithms and models, along with working examples to show the concepts in practice.

5. "Machine Learning for Hackers" by Drew Conway and John Myles White
Authors: Drew Conway and John Myles White
Here, the word ??hackers' is used in the more technical sense: programmers who hack together code for specific goals and practical projects. For those who aren't well versed in mathematics, but are experienced with programming and coding languages, "Machine Learning for Hackers" comes in. Machine learning is usually based on a lot of math, due to the algorithms needed for it to parse data, but a lot of experienced coders don't always develop those math skills.

The book uses hands-on case studies to present the material in real-world practical applications rather than going heavy on mathematical theory. It presents typical problems in machine learning and how to solve them with the R programming language. From comparing U.S. Senators based on their voting records to building a recommendation system for who to follow on Twitter, to detecting spam emails based on the email text, machine learning applications are endless.

Source: HOB