5 Best Books for Machine Learning

We all know that these days, machine learning technology has emerged very fast. Many businesses, scientists, and industries have shown their interest in the field of machine learning. That’s why today I will tell you about some best books for machine learning that an ML beginner or an engineer must read.

Machine learning is one possible application of artificial intelligence. It uses specific algorithms to teach machines how to learn, automatically improving performance and delivery.

This idea has proven to give humans incredible power. With machine learning, tasks can be run automatically, thus making life more comfortable.

Book Name & Author Image Rating Price
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by John D. Kelleher, Brian Mac Namee, Aoife D’Arcy 9.8 View on Amazon
The Hundred-Page Machine Learning Book by Andriy Burkov 9.3 View on Amazon
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron 9.3 View on Amazon
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) by Kevin P. Murphy 9.0 View on Amazon
Machine Learning For Absolute Beginners: A Plain English Introduction (AI, Data Science, Python & Statistics for Beginners)
by Oliver Theobald
9.2 View on Amazon

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

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 behavior, and document classification.

This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory work examples, and case studies illustrate the application of these models in the broader business context.

This edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge of core concepts, and giving them a solid basis for exploring the field on their own.

Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to the implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.

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2. The Hundred-Page Machine Learning Book

The Hundred-Page Machine Learning Book by Andriy Burkov will help you to easily learn machine learning through self-study within a few days.

The great thing about this book is that you don’t need to have any prior knowledge of the subject. As a novice, the first five chapters will guide you through learning the fundamentals, followed by chapters that teach you more advanced concepts in an easy-to-understand manner.

It is a great tool in the hands of students of data science. The book will also do those seeking in-depth knowledge about machine learning some good. If you have some basic knowledge of statistics, math, and probability, then you’ll be soaring through this book easily.

Realistically, you wouldn’t learn everything about machine learning from this book. However, you will all learn all that you need to know. Each chapter is written in such a way that the knowledge is broken down for easy understanding.

Here’s a simple tip. The counterfeit of this book is available and if you are not careful, you might just order it. To order the original, make sure it ships from Amazon directly.

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3. Hands-On Machine Learning with Scikit-Learn and TensorFlow

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

The idea is to help programmers who have no previous experience with the technology create their own programs by presenting them with simple yet efficient tools in the most practical manner.

The author helps the reader gain an intuitive understanding of tools and concepts used in developing these intelligent systems by employing minimal theory, concrete examples, and a dual, production-ready Python framework.

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4. Machine Learning: A Probabilistic Perspective

In search of a textbook that teaches probabilistic methods along with inference? Machine Learning: A Probabilistic Perspective is one of your best options, combining inference with probabilistic methods to comprehensively introduce machine learning.

Machine learning is useful for determining future data as it can detect current data automatically. This textbook covers a wide range of topics relating to the subject by going in-depth into each topic.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as a discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way.

Almost all the models described have been implemented in a MATLAB software package PMTK (probabilistic modeling toolkit) that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

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5. Machine Learning For Absolute Beginners: A Plain English Introduction

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

As the name implies, Machine Learning for Absolute Beginners is perfect for the complete novice. The author explains key concepts in simple, easy-to-understand language for those without any prior experience in coding.

Visual examples and understandable explanations are used to present core algorithms so the novice can follow along with ease. It covers a wide range of topics, including:

Downloading free datasets.

  • Data scrubbing techniques.
  • Preparation of data for analysis.
  • Regression analysis.
  • Clustering includes k-nearest and k-means.
  • Neural Networks.
  • Bias/Variance, which is instrumental to the improvement of machine learning models.
  • Decision Trees for the decoding of classification.
  • Using Python to build a Machine Learning Model.

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