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10 Machine Learning Books To Read For Budding Data Scientists

Machine learning and artificial intelligence are exciting fields, and we’ve been writing about these topics for a couple of years now. While a lot of what we talk about on our blog is advanced implementations of machine learning and can be overwhelming to beginners, the core concepts of machine learning are actually pretty easy to grasp. There are many resources and cheat sheets available online, but we believe the old-fashioned way of learning is sometimes the best: with a good book. Few resources can match the in-depth, comprehensive detail of a good book.

In this blog, we list some of the most popular books for machine learning beginners or for anyone curious about machine learning.

But while these books will give you a good overview of the topics and theories, you also can’t beat practice. Check out our blog on the courses available online or in South Africa to add some practical experience and coursework to your machine learning curve.

Machine learning books for beginners

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition

Author(s): Trevor Hastie, Robert Tibshirani and Jerome Friedman

This book describes the critical ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting – the first comprehensive treatment of this topic in any book.

4.0 / 5 stars on Amazon (at the time of writing).

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Machine Learning for Absolute Beginners: A Plain English Introduction

Author(s): Oliver Theobald

Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners. This means understandable English explanations and no coding experience is required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.

4.5 / 5 stars on Amazon (at the time of writing).

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Machine Learning: The Absolute Beginner’s Guide to Learn and Understand Machine Learning Effectively

Author(s): Hein Smith

Just about anyone with the slightest bit of interest in modern technology is looking to learn more about Machine Learning. This innovative new form of computer programming is the primary tool that makes it possible for a machine to perform a wide range of tasks for you that could range from recommending an excellent movie to driving you to work every day.

No doubt, it is the tech of the future. But it is also a subject that can literally boggle the mind. If you’re not already deep into the terminology and techniques of this wildly exciting new industry, finding information on it written in basic layman’s terms can be tough.

Most of the books on the topic assume that you have at least a fundamental knowledge of the subject. If you’re interested in getting a better grasp at just how this new technology works and what it means for the masses then this is the book for you.

All of it is in very basic simple English so you won’t need a coding degree to understand it. Here, we discuss all the essential entry-level topics required for the absolute amateur so you can start to make sense of this highly innovative technological advancement.

Machine Learning is becoming an increasingly powerful tool that will have an impact on every aspect of our lives in the future. So, whether you need to find good product recommendations to meet your needs or you want to go all out and live in your own smart home, machine learning will be at the core of it. This book will make it easier to grasp the concepts behind it and get you started on a path that leads to a very bright future.

4.5 / 5 stars on Amazon (at the time of writing).

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Understanding Machine Learning: From Theory to Algorithms

Author(s): Shai Shalev-Shwartz

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This textbook aims to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; major algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

4.4 / 5 stars on Amazon (at the time of writing).

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Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Author(s): John D. Kelleher, Brain Mac Namee and Aoife D’Arcy

A comprehensive introduction to the most essential machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behaviour, and document classification. This introductory textbook offers a detailed and focused treatment of the most crucial machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. The technical and mathematical material is augmented with illustrative worked examples, and case studies illustrate the use of these models in the broader business context.

After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a non-technical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors’ many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

4.5 / 5 stars on Amazon (at the time of writing).

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Introduction to Machine Learning with Python: A Guide for Data Scientists

Author(s); Andreas C. Muller and Sarah Guido

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

4.1 /5 stars on Amazon (at the time of writing).

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Machine Learning with R – Second Edition: Expert techniques for predictive modelling to solve all your data analysis problems

Author(s): Brett Lantz

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of R’s cross-platform, zero-cost statistical programming environment, there has never been a better time to start applying machine learning to your data. Machine learning with R offers a robust set of methods to quickly and easily gain insight from your data for both, veterans and beginners in data analytics.

Want to turn your data into actionable knowledge, predict outcomes that make a real impact, and have continued developing insights? R gives you access to all the power you need to master exceptional machine learning techniques.

The second edition of Machine Learning with R provides you with an introduction to the essential skills required in data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience.

With this book, you’ll discover all the analytical tools you need to gain insights from complex data and learn to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you’ll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R.

4.5 / 5 stars on Amazon (at the time of writing).

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More advanced machine learning books to discover

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake our World

Author(s): Pedro Domingos

In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner–the Master Algorithm–and discusses what it will mean for business, science, and society. If data-ism is today’s philosophy, this book is its bible.

4.2 / 5 stars on Amazon (at the time of writing).

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Advances in Financial Machine Learning

Author(s): Marcos Lopez de Prado

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular settings. Written by a recognised expert and portfolio manager, this book will equip investment professionals with the ground-breaking tools needed to succeed in modern finance.

4.5 / 5 stars on Amazon (at the time of writing).

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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

Author(s): Nikhil Buduma and Nicholas Locascio

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.

Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.

3.7 / 5 stars on Amazon (at the time of writing).

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