Best Machine Learning Books for Beginners

by | Feb 19, 2024 | Data Science, Machine Learning

Stepping into the world of machine learning is a pivotal moment for any beginner filled with curiosity and a drive to unlock the mysteries of artificial intelligence. Just as selecting the right course can set you on a path of discovery and mastery, finding the perfect book is equally crucial in your journey.

In this exploration, I’ve meticulously curated a list of the best machine learning books for beginners, designed specifically for those new to machine learning. These volumes come from the pens of leading experts in the field, offering a blend of foundational knowledge, practical insight, and hands-on projects.

Whether you’re completely new to the concept or looking to solidify your understanding of machine learning principles, these books promise to be your faithful guides, transforming complex theories into accessible knowledge step-by-step, and equipping you with the tools needed to navigate the fascinating world of AI.

In these machine learning books for beginners, you’ll find everything from basic machine learning concepts like linear regression, supervised learning, unsupervised learning, decision trees, and support vector machines, to advanced topics in machine learning.

Let’s dive in.


Machine Learning Books for Beginners

1. The Hundred-Page Machine Learning Book by Andriy Burkov

Embark on a journey into the realm of machine learning with “The Hundred-Page Machine Learning Book,” a compact yet comprehensive guide designed for beginners. Authored by Andriy Burkov, a seasoned machine learning expert with a Ph.D. in AI, this book distills the essence of machine learning into an accessible format.

Covering key topics such as supervised and unsupervised learning, deep learning, and the crucial mathematics behind these concepts, Burkov employs a clear, engaging writing style complemented by practical Python examples.

This book promises a solid foundation in machine learning, making complex AI systems understandable and preparing readers for machine learning interviews.

What sets this book apart is its ability to make the vast field of machine learning approachable within just over 100 pages. Through a blend of theory and practice, readers gain insights into fundamental algorithms, neural networks, and essential math topics, all tailored for immediate application.

This guide is not only a resource for acquiring foundational knowledge but also serves as a launchpad for further exploration in the field. Whether you’re a data professional expanding your skill set or a beginner eager to delve into machine learning, “The Hundred-Page Machine Learning Book” offers a concise, yet thorough, entry point into the subject.

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Publisher: ‎ Andriy Burkov (January 13, 2019)

Book Edition: First Edition

The hundred page machine learning book


2. Machine Learning For Absolute Beginners by Oliver Theobald

Machine Learning for Absolute Beginners” by Oliver Theobald is a highly accessible introduction to the field of machine learning, designed specifically for readers with no prior knowledge of coding or mathematics.

This comprehensive guide breaks down complex concepts into understandable language, covering key topics such as the fundamentals of machine learning, data analysis, data preprocessing, and the use of major machine learning libraries, such us Scikit-learn, NumPy, Pandas, Matplotlib, Seaborn, and TensorFlow.

With an emphasis on practical application, the book offers an introduction to Python programming, ensuring that beginners can easily follow along and implement the concepts discussed.

Enhanced with visuals, examples, and clear explanations of machine learning algorithms, plus free downloadable code exercises and video demonstrations, Theobald’s book is an indispensable resource for anyone looking to start their journey in machine learning.

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Publisher: ‎ Independently published (April 3, 2017)

Book Edition: Third Edition


3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron Aurelien

Aurélien Géron‘s Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is an intermediate-level guide that bridges the gap between basic coding knowledge and the development of intelligent systems.

Designed for readers with Python experience, this book delves into the construction and training of deep neural networks, deep reinforcement learning, and the fundamentals of linear and logistic regression. It stands out for its practical approach, offering a plethora of well-crafted exercises that not only reinforce learning but also prepare readers for real-world machine-learning projects.

While it navigates through a wide range of topics including performance measurement, ensemble learning methods, and deep neural networks, it remains accessible to programmers without oversimplification.

The latest edition updates include cutting-edge code from the TensorFlow and Scikit-Learn libraries, covering everything from basic regression to advanced neural networks, CNNs, RNNs, and GANs.

Géron’s work is ideal for readers seeking to solidify their understanding of machine learning concepts through practical application, making it a go-to guide for building end-to-end machine learning systems with Python.

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Publisher: ‎ O’Reilly Media;  (October 15, 2019)

Book Edition: 2nd Edition

Machine Learning with Python


4. Introduction to Machine Learning with Python

Introduction to Machine Learning with Python” by Andreas C. Müller and Sara Guido offers a hands-on approach to learning machine learning for those with a foundational knowledge of Python.

Tailored for beginners, this guide emphasizes the practical application of machine learning algorithms using Python and the Scikit-learn library, steering clear of complex mathematical explanations.

It covers a broad spectrum of topics, from the basics of machine learning, and supervised and unsupervised learning models, to data representation techniques and natural language processing, making it an ideal starting point for aspiring data scientists.

This book is distinguished by its focus on the Scikit-learn library, a staple in Python-based machine learning, and its clear presentation of foundational concepts and algorithms. It guides readers through the machine learning workflow, offering insights into best practices for data cleaning, feature engineering, model evaluation, and optimization.

The authors, both acclaimed data scientists, leverage their expertise to provide a comprehensive introduction to the field, enriched with examples that demonstrate the use of Scikit-learn. For those looking to delve into machine learning with Python, this book serves as both a practical guide and a valuable resource for developing effective machine-learning applications, offering insights into hyperparameter tuning, model evaluation, and the complete workflow of a machine learning project to meet business demands.

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Publisher: ‎ O’Reilly Media;

Book Edition: 1st edition

Book Authors: Andreas C. Müller & Sarah Guido


5. Python Machine Learning By Example

Python Machine Learning By Example, Third Edition,” by Yuxi (Hayden) Liu, is a vital resource for aspiring machine learning enthusiasts keen on applying Python to solve real-world problems. This book begins with the fundamentals of machine learning and Python programming, making it accessible to beginners while also being rich in content for more advanced learners.

Liu adopts a hands-on approach, emphasizing the practical application of machine learning algorithms through a series of projects that cover data preprocessing, analysis, visualization, and various machine learning techniques including clustering, classification, and regression.

What distinguishes this edition are the six new chapters that align with the latest machine learning advancements, ensuring learners are up-to-date with the field’s current demands. Readers are guided through the creation of sophisticated models for applications like movie recommendations, face recognition, stock price forecasting, and more, utilizing techniques ranging from Naive Bayes to Reinforcement Learning.

Liu’s industry insights from his experience as a machine learning engineer at Google enrich the book, making it an engaging guide filled with real-world examples, like spam detection and ad click-through prediction. This third edition not only bolsters the learner‘s understanding of the machine learning landscape using Python but also equips them with the skills to develop intelligent applications confidently.

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Publisher: ‎ Packt Publishing (May 31, 2017)

Book Edition: third edition

Book Author: Yuxi (Hayden) Liu


6. Machine Learning Yearning – Andrew Ng

Andrew Ng’s book on machine learning offers a comprehensive exploration of strategic aspects crucial for building effective machine-learning systems in real-world scenarios. Renowned for his clear and accessible style, Ng provides a valuable resource suitable for both novices and experts in the field.

The book covers the entire data science process, from initial development setup to collaborative strategy formulation within team settings, emphasizing practical insights into project management, data collection, feature engineering, and model evaluation.

Exclusive topics such as introduction to machine learning problems, learning curves, debugging algorithms, bias and variance, and error analysis furnish readers with a robust foundation in designing impactful machine learning systems.

Ng’s consideration of the strategic business implications, including industry-specific applications like marketing and retail, empowers readers to effectively apply machine learning techniques across diverse business landscapes.

Praised for its clarity and practicality, the book is hailed as a timeless guide, with Ng’s adept teaching style ensuring that readers not only grasp complex concepts but also understand how to implement strategies in contemporary machine-learning projects.

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Book Author: Andrew Ng


7. Machine Learning For Dummies

“Machine Learning For Dummies” by John Paul Mueller and Luca Massaron offers an accessible introduction to the intricate world of machine learning (ML) for readers seeking to understand its fundamental concepts and practical applications.

Tailored for novices in the field, the book aims to empower readers with the knowledge and skills needed to construct and deploy machine learning models effectively. By blending historical context with hands-on tutorials in Python, R, and TensorFlow, the authors ensure readers not only grasp theoretical foundations but also gain hands-on experience with contemporary datasets and methodologies.

A standout feature of the book is its comprehensive coverage of the machine learning process, encompassing data cleaning, exploration, preprocessing, and an exploration of unsupervised, supervised, and deep learning techniques. Through guidance on model evaluation metrics and best practices in feature and model selection, the authors equip readers with essential tools to navigate the complexities of machine learning, mitigating common challenges such as overfitting.

“Machine Learning For Dummies” simplifies daunting machine learning concepts, illustrating their relevance in everyday technologies like web search algorithms and spam filtering. With practical coding tutorials in R and Python, the book serves as a holistic primer for beginners keen on understanding how to interact with computers and leverage machine learning to discern patterns and analyze data effectively.

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Publisher: ‎ For Dummies

Book Edition: 1st edition

Book Author:  John Paul Mueller and Luca Massaron


8. Machine Learning in Action by Peter Harrington

“Machine Learning in Action” by Peter Harrington is a pivotal guide that marries the foundational theories of machine learning with the hands-on practice of crafting analytical tools for everyday use.

Tailored for IT professionals and developers eager to dive into machine learning, this book stands out for its clear, jargon-free approach, taking readers directly to the practical techniques needed in their daily work. Harrington, a seasoned data scientist, fills the pages with Python-based examples that not only illustrate core machine learning algorithms but also span across a wide range of tasks including data preprocessing, data analysis, and data visualization.

This book is designed for readers with no prior experience in machine learning, though a basic familiarity with Python is advantageous, making it an ideal starting point for beginners. Through its comprehensive coverage, the book delves into classification, regression forecasting, unsupervised learning, and advanced machine learning tools, offering a holistic view of the field.

Each chapter is structured to introduce fundamental concepts and algorithms, followed by practical examples to apply these techniques in real-world scenarios.

What sets “Machine Learning in Action” apart is its focus on making machine learning accessible to developers, demystifying complex algorithms, and providing a toolkit for building sophisticated data analysis programs.

Whether you’re looking to understand the basics of classification, forecast numerical values, dive into unsupervised learning, or explore advanced machine learning tools, Harrington’s guide is an indispensable resource. It stands as a testament to the blend of theoretical knowledge and practical application, making it a must-read for anyone looking to harness the power of machine learning in their projects.

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Publisher: Manning; First Edition

Book Edition: 1st Edition

Book Author: Peter Harrington


Enhancing Your Machine Learning Journey: Essential Reads by Soledad Galli:

Once you have mastered the basics of machine learning through these machine learning books for beginners, you’ll be challenged with the complexities of feature engineering and selection. These are two critical aspects that significantly impact the performance and efficiency of machine learning models.

The books by Soledad Galli, “Python Feature Engineering Cookbook” and “Feature Selection in Machine Learning ” serve as your gateway to exploring these advanced topics.


Unleashing the Power of Data with “Python Feature Engineering Cookbook”

In the realm of machine learning, ensuring the quality of input data through predictive data analytics and statistical learning is paramount. “Python Feature Engineering Cookbook, 2nd Edition” by Soledad Galli stands as an indispensable resource for those aspiring to elevate their machine learning models.

This book offers over 70 Python recipes that cover the transformation and creation of features from tabular, text, time series and transactional data. This book thereby ensures your models are built on a foundation of robust and meaningful data.

From addressing challenges like missing data and categorical variables to harnessing advanced techniques to extract features from transactions, time series or text data, Galli’s approach focuses on leveraging the power of open-source Python libraries, to simplify the creation of features and streamline your machine learning workflows.

This book is a comprehensive guide for data scientists and machine learning engineers who seek to apply the principles of statistical learning and predictive analytics to enhance model accuracy and performance.

Python Feature Engineering Cookbook book cover


Mastering Feature Selection with “Feature Selection in Machine Learning”

The complexity and depth of machine learning models are often mirrored in the data they learn from. “Feature Selection in Machine Learning” by Soledad Galli is a critical read for those focused on predictive modeling and the efficiency of machine learning algorithms.

Galli expertly navigates the process of feature selection, an essential process for developing simpler, faster, and more performant models that are foundational to predictive data analytics. This book thoroughly explores filter, wrapper, and embedded methods, alongside advanced strategies for feature selection, illustrating how to significantly reduce model complexity without sacrificing predictive power.

Galli’s work emphasizes the importance of statistical learning in selecting features that contribute most significantly to the model’s predictive capabilities. By providing readers with a pathway to eliminate redundancy and enhance machine learning interpretability, this book stands as a beacon for developing real-world machine learning frameworks.

The accompanying Github repository extends an invitation to practical application, making it a vital tool for anyone aiming to harness the full potential of their data through informed feature selection.

Feature Selection in Machine Learning with Python, book cover


Final Thoughts: Navigating Towards Mastery in Machine Learning

The books we’ve explored are more than just instructional materials; they are gateways to the vast and intricate world of machine learning, designed to empower you at the very start of your journey.

From the gentle introductions and basic concepts provided by “Machine Learning for Absolute Beginners” to the deeper dives into algorithms and practical applications found in “Machine Learning in Action by Peter Harrington” each book has been carefully selected to not only introduce you to the fundamentals but also to inspire you to apply what you learn in innovative ways.

If you are interested in stepping into the field of machine learning, but reading is not your thing, check out our machine learning course recommendations instead.

Once you’ve mastered the essentials, you can decide which path to take for further learning or specialization, be it Computer Vision, data mining, Artificial Neural networks or Computer science itself.

Embrace the journey, engage with the challenges, and be part of shaping the technological landscape. Happy reading and learning!