15+ Machine Learning Books for Free! [PDF]

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Artificial intelligence has been growing exponentially since its emergence, and with the idea that you learn a little more about this topic, we prepared a varied collection of free machine learning books in PDF format.

Machine Learning is a branch of Artificial Intelligence, which through algorithms provides computers with the ability to detect and identify patterns within massive data, to generate forecasts or predictive analysis.

This AI discipline is considered a scientific field and works with information in the form of words, numbers, statistics, and images, among many others. 

For the development of these algorithms, various programming languages are used such as Python, C++, R, Java, JavaScript, C#, Julia, TypeScript, Shell, and Scala.

You can immerse yourself in this subject of great interest and current affairs, reading any of our more than 70 materials, including books and free articles on machine learning in PDF format.

Machine Learning Books

Introduction to machine learning

Nils J. Nilsson

Machine Learning

Jaydip Sen

Undergraduate Fundamentals of Machine Learning

William J. Deuschle

Machine Learning - Supervised Techniques

Sepp Hochreiter

Machine learning - The power and promise of computers that learn by example

Royal Society

Machine Learning


The Foundation for Best Practices in Machine Learning


Machine Learning Tutorial

Wei-Lun Chao

A non-technical introduction to machine learning

Olivier Colliot

Artificial Intelligence and Machine Learning Approaches in Digital Education - A Systematic Revision

Hussan Munir, Bahtijar Vogel and Andreas Jacobsson

Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions

Raffaele Cioffi, Marta Travaglioni, Giuseppina Piscitelli, Antonella Petrillo and Fabio De Felice

Rules of Machine Learning - Best Practices for ML Engineering

Martin Zinkevich

Best Practices for Machine Learning Applications

Brett Wujek, Patrick Hall, and Funda Gunes

Supervised Machine Learning: A Review of Classification Techniques

S. B. Kotsiantis

The fundamentals of machine learning

Jay Wilpon, David Thomson, Srinivas Bangalore, Patrick Haffner and Michael Johnston

An Introduction to Machine Learning

Solveig Badillo, Balazs Banfai, Fabian Birzele and others

How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different? (Article)

Tanya Tiwari, Tanuj Tiwari and Sanjay Tiwari

Machine Learning in Artificial Intelligence - Towards a Common Understanding (Article)

Niklas Kühl, Marc Goutier, Robin Hirt, Gerhard Satzger

Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature (Article)

Andreas K Triantafyllidis and Athanasios Tsanas

Overview of Machine Learning Tools and Libraries

Daniel Pop and Gabriel Iuhasz

Algorithms in Machine Learning Books

In the world of machine learning, algorithms are an essential part of the machine learning process, and understanding them can be critical to developing innovative solutions in different areas.

Algorithms in machine learning are a series of defined steps that allow machines to learn from data and improve their performance over time.

If you are interested in learning more about this topic, we invite you to explore our selection of free books and articles on machine learning algorithms.

Online gradient descent learning algorithm

Yiming Ying and Massimiliano Pontil

Types of Machine Learning Algorithms

Taiwo Oladipupo Ayodele

Clustering Algorithms: A Comparative Approach

Mayra Z. Rodriguez, Cesar H. Comin, Dalcimar Casanova and others

Dbscan - Fast Density-based Clustering with R

Michael Hahsler, Matthew Piekenbrock and Derek Doran

Methods of Hierarchical Clustering

Fionn Murtagh and Pedro Contreras

Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models

Xinhui Zhang, Xun Shen and Tinghui Ouyang

A review of Machine Learning (ML) algorithms used for modeling travel mode choice

Pineda-Jaramillo and Juan D

A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms

Shahneela Pitafi, Toni Anwar and Zubair Sharif

K-Means Clustering and Related Algorithms

Ryan P. Adams

Selection of K in K-means clustering

D T Pham, S. S. Dimov, and C D Nguyen

k-Nearest Neighbour Classifiers

Pádraig Cunningham and Sarah Jane Delany

DBSCAN: A simple fast DBSCAN algorithm for big data

Shaoyuan Weng, Jin Gou and Zongwen Fan

KNN Classification With One-Step Computation

Shichao Zhang and Jiaye Li

Hierarchical Clustering (Article)

Ryan P. Adams

Implementation of Decision Tree Algorithm to Classify Knowledge Quality in a Knowledge Intensive System

Casper Kaun, N.Z Jhanjhi, Wei Wei Goh and Sanath Sukumaran

Supervised Machine Learning Algorithms - Classification and Comparison (Article)

Osisanwo F.Y, Akinsola J.E.T, Awodele O and others

Random Forest Classifiers :A Survey and Future Research Directions (Article)

Vrushali Y Kulkarni and Pradeep K Sinha

Classification Based on Decision Tree Algorithm for Machine Learning (Article)

Bahzad Taha Jijo and Adnan Mohsin Abdulazeez

Combining Hierarchical Clustering and Machine Learning to Predict High-Level Discourse Structure (Article)

Caroline Sporleder and Alex Lascarides

Supervised Learning Books

Supervised learning is one of the most popular and widely used techniques in the field of Machine Learning. It is a type of learning in which an algorithm is trained using a labeled data set to learn to make accurate predictions or classifications.

Supervised learning is used in a wide variety of Machine Learning applications, such as image classification, email spam detection, fraud detection in financial transactions, and many others.

Learn more about this powerful and versatile technique with the following free supervised learning books and articles in PDF format.

Supervised Learning - An Introduction

Michael Biehl

Supervised Learning Techniques - A comparison of the Random Forest and the Support Vector Machine

Jonni Fidler Dennis and Lukas Arnroth

Supervised Machine Learning Techniques: An Overview with Applications to Banking

Linwei Hu, Jie Chen, Joel Vaughan, Hanyu Yang and others

Supervised Machine Learning: A Brief Introduction

Seemant TIWARI

Supervised Machine Learning

Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten and Thomas B. Schön

Unsupervised Learning Books

Unsupervised learning is another essential technique used in Machine Learning. Unlike supervised learning, where the algorithm is trained using a labeled data set, in unsupervised learning the algorithm is prepared using an unlabeled data set.

In unsupervised learning, the algorithm is responsible for finding patterns in the input data on its own, without being told what to look for. This technique is especially useful in situations where there is no labeled training data set available.

This technique is used in a wide variety of Machine Learning applications, such as customer segmentation, data clustering, anomaly detection, and many others. You can learn a little more with the following unsupervised learning books and articles in PDF format.

Unsupervised learning - a systematic literature review

Salim Dridi

Unsupervised Learning

Wei Wu

Discovery of Course Success Using Unsupervised Machine Learning Algorithms

Emre CAM and Muhammet Esat OZDAG

Unsupervised learning (Article)

Hannah Van Santvliet

Deep Learning of Representations for Unsupervised and Transfer Learning

Yoshua Bengio

Unsupervised Feature Learning and Deep Learning - A Review and New Perspectives

Yoshua Bengio, Aaron Courville, and Pascal Vincent

Deep Learning Books

Deep learning is a machine learning technique that uses artificial neural networks to learn from large data sets and improve their ability to perform complex tasks.

It has become increasingly popular in recent years due to its ability to tackle complex problems in different areas, from medicine to robotics

It has also enabled significant advances in areas such as speech recognition and computer vision. You can learn more about this topic with the following books and articles on deep learning.

The Little Book of Deep Learning

François Fleuret

Neural Networks and Deep Learning

Michael Nielsen

Deep learning in neural networks: An overview

Jürgen Schmidhuber

List of Deep Learning Models

Amir Mosavi, Sina Ardabili, and Annamária R. Várkonyi-Kóczy

Machine learning and deep learning (Article)

Christian Janiesch, Patrick Zschech and Kai Heinrich

Deep Learning Limitations and Flaws (Article)

Bahman Zohuri and Masoud Moghaddam

Deep Learning Techniques: An Overview (Article)

Amitha Mathew, P.Amudha and S.Sivakumari

The Limitations of Deep Learning in Adversarial Settings

Nicolas Papernot, Patrick McDaniel, Somesh Jha and others

Machine Learning and Database Books

Machine learning and databases are two closely related topics. In simple terms, databases are an essential tool for storing and organizing large data sets, while Machine Learning is a technique for analyzing and extracting useful information from that data.

Together, machine learning and databases are essential for processing and analyzing large data sets. For example, machine learning algorithms can be used to analyze data stored in a database and provide useful information to users.

In addition, databases can be used to store and organize the data needed to train machine learning algorithms. Learn more about this interesting relationship with the following books and articles on machine learning and databases.

Data Science and Machine Learning

Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre and Radislav Vaisman

Handbook Of Artificial Intelligence And Big Data Applications In Investments

Larry Cao

On practical machine learning and data analysis

Daniel Gillblad

Machine Learning with Big Data - Challenges and Approaches

Alexandra L’Heureux, Katarina Grolinger, Hany F. ElYamany, Miriam A. M. Capretz

Machine Learning for Database Management Systems

Sai Tanishq N.

A review on the significance of machine learning for data analysis in big data

Vishnu Vandana Kolisetty and Dharmendra Singh Rajput

UDO: Universal Database Optimization using Reinforcement Learning

Junxiong Wang, Immanuel Trummer and Debabrota Basu

Exploration of Approaches for In-Database ML

Steffen Kläbe, Stefan Hagedorn and Kai-Uwe Sattler

Neural Networks Books

Neural networks are computational systems that are inspired by the workings of the human brain and are used to learn from large data sets and perform complex tasks in an automated manner.

They are commonly used in computer vision, natural language processing, and robotics. In addition, deep neural networks have enabled significant advances in the field of deep learning.

If you would like to learn more, we invite you to take a look at the following books and articles on neural networks that we have located for you in PDF format.

Natural Language Processing

Jacob Eisenstein

Natural Language Processing

Ann Copestake

Introduction to natural language processing

R. Kibble

Natural Language Processing


Natural Language Processing Advancements By Deep Learning - A Survey

Amirsina Torf, Rouzbeh A. Shirvani, Yaser Keneshloo, Nader Tavaf, and Edward A

Here ends our selection of free Machine Learning Books in PDF format. We hope you liked it and already have your next book!

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