Explore the exciting universe of Machine Learning with our collection of free PDF books.
Machine Learning is a branch of artificial intelligence that enables machines to learn and improve automatically from data.
This discipline is revolutionizing industries with applications ranging from data prediction to recognizing complex patterns.
Browse through our collection of free books, which cover everything from basic introductions to advanced approaches in algorithms, neural networks, and big data.
Take advantage of the free access to these books and break the barriers that often limit learning. Expand your horizons and delve into this constantly evolving technology.
Download your free PDF Machine Learning books and take the first step towards mastering this powerful tool of the future.
Machine Learning Books
#1 Foundations of Machine Learning
Extension: PDF | 505 pages
Foundations of Machine Learning, this second edition serves as a comprehensive introduction to machine learning, covering fundamental topics, theoretical frameworks, and practical applications.
#2 Machine Learning – Supervised Techniques
Extension: PDF | 256 pages
Machine Learning - Supervised Techniques, provides a comprehensive overview of supervised machine learning methods, emphasizing applications in bioinformatics.
#3 Interpretable Machine Learning
Extension: PDF | 251 pages
Interpretable Machine Learning, this book serves as a comprehensive guide to making complex machine learning models interpretable. It discusses various interpretability methods, their importance, and practical applications, making it crucial for practitioners and researchers seeking to improve model transparency and trustworthiness in AI.
#4 Introduction to machine learning
Extension: PDF | 188 pages
Introduction to machine learning, this paper serves as an initial draft of a textbook proposal on machine learning. Covers fundamental concepts, various types of learning and methods.
#5 Machine Learning
Extension: PDF | 154 pages
Machine Learning, this document presents a comprehensive overview of recent advancements in machine learning, particularly in applications such as finance, healthcare, and automation.
#6 Undergraduate Fundamentals of Machine Learning
Extension: PDF | 143 pages
Undergraduate Fundamentals of Machine Learning is a comprehensive resource designed to provide students with a foundational understanding of machine learning.
#7 Machine learning – The power and promise of computers that learn by example
Extension: PDF | 128 pages
Machine learning - The power and promise of computers that learn, this document provides a comprehensive overview of machine learning, highlighting its advancements, applications, and implications for society.
#8 Machine Learning
Extension: PDF | 120 pages
Machine Learning, this document serves as a comprehensive set of lecture notes on machine learning, highlighting key concepts such as supervised and unsupervised learning, reinforcement learning, ensemble methods, and genetic algorithms.
#9 The Foundation for Best Practices in Machine Learning
Extension: PDF | 88 pages
The Foundation for Best Practices in Machine Learning, this document outlines ethical and responsible practices for machine learning, providing guidelines for data scientists and organizations to ensure fairness, transparency, and accountability in machine learning projects.
#10 Machine Learning Tutorial
Extension: PDF | 56 pages
Machine Learning Tutorial, this document offers a comprehensive overview of machine learning, including definitions, basic concepts, supervised and unsupervised learning techniques, and practical applications.
#11 The Little Book of Deep Learning
Extension: PDF | 163 pages
The Little Book of Deep Learning, this concise guide offers foundational insights into deep learning and machine learning, covering essential concepts, model architectures, and applications.
#12 Neural Networks and Deep Learning
Extension: PDF | 224 pages
Neural Networks and Deep Learning, this document serves as a comprehensive introduction to neural networks and deep learning, exploring their architecture, learning algorithms, and applications in recognizing handwritten digits.
#13 Handbook Of Artificial Intelligence And Big Data Applications In Investments
Extension: PDF | 164 pages
Handbook Of Artificial Intelligence And Big Data Applications In Investments, this document explores various applications of artificial intelligence (AI) and big data in the investment sector, focusing on machine learning (ML) techniques, natural language processing, and their implications for asset management.
#14 Machine Learning with Big Data – Challenges and Approaches
Extension: PDF | 22 pages
Machine Learning with Big Data - Challenges and Approaches, this document explores the challenges and approaches of applying machine learning techniques to Big Data, highlighting how traditional algorithms struggle with the characteristics of Big Data, such as volume, velocity, variety, and veracity.
#15 Natural Language Processing
Extension: PDF | 587 pages
Natural Language Processing, is a foundational document covering a wide range of techniques and approaches in natural language processing, with a significant focus on machine learning.
#16 Natural Language Processing
Extension: PDF | 80 pages
Natural Language Processing, this document outlines a comprehensive course on natural language processing (NLP), introducing fundamental techniques, current research issues, and applications.
Algorithms in Machine Learning Books and Materials
#1 Mathematical Analysis of Machine Learning Algorithms
Extension: PDF | 479 pages
Mathematical Analysis of Machine Learning Algorithms, is a comprehensive examination of the mathematical foundations underlying machine learning algorithms.
#2 Types of Machine Learning Algorithms
Extension: PDF | 32 pages
Types of Machine Learning Algorithms, the document provides a comprehensive overview of various machine learning algorithms, categorizing them into supervised, unsupervised, semi-supervised, reinforcement learning, and others.
#3 Clustering Algorithms: A Comparative Approach
Extension: PDF | 31 pages
Clustering Algorithms: A Comparative Approach, this document presents a systematic comparison of seven clustering methods using the R language, addressing the effectiveness of each in different scenarios of artificial data.
#4 K-Means Clustering and Related Algorithms
Extension: PDF | 18 pages
K-Means Clustering and Related Algorithms, this document provides a comprehensive overview of K-Means clustering, a fundamental algorithm in machine learning for grouping similar data points.
#5 k-Nearest Neighbour Classifiers
Extension: PDF | 18 pages
k-Nearest Neighbour Classifiers, this document provides an in-depth overview of k-Nearest Neighbour (k-NN) classification, discussing its mechanisms, distance metrics, computational complexities, and techniques for dimensionality reduction.
#6 Online gradient descent learning algorithm
Extension: PDF | 41 pages
Online gradient descent learning algorithm, this paper discusses an online gradient descent algorithm in the context of reproducing kernel Hilbert spaces (RKHS), focusing on deriving error bounds and convergence results without explicit regularization.
#7 A review of Machine Learning (ML) algorithms used for modeling travel mode choice
Extension: PDF | 23 pages
A review of Machine Learning (ML) algorithms used for modeling travel mode choice this paper provides a comprehensive review of various Machine Learning algorithms applied to travel mode choice modeling.
#8 Hierarchical Clustering
Extension: PDF | 11 pages
Hierarchical Clustering, this document discusses hierarchical clustering as an alternative to K-Means clustering, addressing its limitations by exploring its two main approaches: agglomerative and divisive clustering.
#9 Classic machine learning algorithms
Extension: PDF | 61 pages
Classic machine learning algorithms, is a chapter that presents the main classical machine learning algorithms, focusing on supervised learning methods for classification and regression, as well as strategies to mitigate overfitting.
Supervised Learning Books
#1 Supervised Learning – An Introduction
Extension: PDF | 119 pages
Supervised Learning - An Introduction, this paper provides a comprehensive overview of supervised learning, focusing on the classification tasks and the underlying algorithms.
#2 Supervised Machine Learning
Extension: PDF | 112 pages
Supervised Machine Learning, this document serves as lecture notes for a course on Statistical Machine Learning, outlining foundational concepts in supervised machine learning, including regression and classification.
#3 Supervised Machine Learning: A Brief Introduction
Extension: PDF | 13 pages
Supervised Machine Learning: A Brief Introduction, this document offers an overview of supervised machine learning, discussing various classifiers and their applications, particularly in psychological research.
Unsupervised Learning Materials
#1 Unsupervised Learning
Extension: PDF | 20 pages
Unsupervised Learning, this document provides a comprehensive overview of unsupervised learning, detailing its definition, advantages, common algorithms, and applications.
#2 Unsupervised Feature Learning and Deep Learning – A Review and New Perspectives
Extension: PDF | 30 pages
Unsupervised Feature Learning and Deep Learning - A Review and New Perspectives, this document reviews significant advancements in unsupervised feature learning and deep learning, exploring how various representation-learning algorithms enhance machine learning performance.
#3 Unsupervised learning – a systematic literature review
Extension: PDF | 22 pages
Unsupervised learning - a systematic literature review, this document provides a comprehensive examination of supervised learning within the machine learning field, detailing various algorithms, their applications, and performance metrics.
#4 Unsupervised learning
Extension: PDF | 10 pages
Unsupervised learning, this document provides an overview of unsupervised learning, highlighting its definitions, distinctions from supervised and semi-supervised learning, clustering techniques, and association rules, making it a valuable resource for understanding key concepts in machine learning.
Neural Networks Books
#1 An introduction to Neural Networks
Extension: PDF | 135 pages
An introduction to Neural Networks, this document serves as a comprehensive introduction to neural networks, covering fundamentals, theories, and practical applications.
#2 Deep Learning in Neural Networks – An Overview
Extension: PDF | 75 pages
Deep Learning in Neural Networks - An Overview, this document provides a comprehensive overview of deep learning techniques in neural networks, tracing historical developments and key concepts such as supervised and unsupervised learning, reinforcement learning, and the credit assignment problem.
#3 A Brief Introduction to Neural Networks
Extension: PDF | 284 pages
A Brief Introduction to Neural Networks, is a document that offers a comprehensive view of neural networks, ranging from their history and motivations to their components and learning paradigms.
#4 The Shallow and the Deep
Extension: PDF | 294 pages
The Shallow and the Deep, this document is a collection of lecture notes that provides a biased introduction to neural networks and traditional machine learning techniques, focusing on classical methods like classification and regression.
Machine Learning for Programmers Books
#1 Machine Learning with Python Tutorial
Extension: PDF | 453 pages
Machine Learning with Python Tutorial, this book provides a complete guide to machine learning using Python, covering essential concepts, data visualization, and various algorithms such as k-nearest neighbors and neural networks.
#2 Machine Learning with Python
Extension: PDF | 167 pages
Machine Learning with Python, is a comprehensive tutorial that introduces the fundamental concepts of machine learning, its applications, and practical implementation using Python.
#3 Python Machine Learning Projects
Extension: PDF | 135 pages
Python Machine Learning Projects, this book provides a collection of practical Python projects geared towards machine learners, ranging from setting up the programming environment to building classifiers and neural networks.
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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