Free Books Available for Download

• Pattern Recognition and Machine Learning [PDF]

  • This book serves as a comprehensive introduction to pattern recognition and machine learning, reflecting the significant developments over the past decade, including the mainstreaming of Bayesian methods and the emergence of graphical models as a general framework for probabilistic models. Aimed at advanced undergraduates, first-year PhD students, researchers, and practitioners, it requires no prior knowledge in the fields but does require an understanding of multivariate calculus and basic linear algebra, with a self-contained introduction to basic probability theory included for those not familiar with probabilities.

• Foundations of Data Science [PDF]

  • This book offers a comprehensive exploration of the evolution of computer science, with a particular focus on the application of mathematical and algorithmic theory to address the challenges of modern data analysis. It traces the journey from the discipline's inception in the 1960s, through its development to incorporate the study of algorithms in the 1970s, to its current emphasis on extracting actionable insights from vast datasets.

• A Machine Learning Primer [PDF]

  • This book serves as a comprehensive guide to the foundational and advanced techniques in machine learning and data analysis. It begins with an exploration of predictive modeling through linear and logistic regression before delving into classification methods such as Naive Bayes, Support Vector Machines, Decision Trees, and K-Nearest Neighbors. This book is tailored for individuals seeking to deepen their understanding of machine learning algorithms and their application in real-world data science problems.

• Mathematics for Machine Learning [PDF]

  • "Mathematics for Machine Learning" is crafted to bridge the daunting gap between high school mathematics and the level required to delve into the realms of machine learning, aiming to make the fascinating world of machine learning accessible to a broader audience. The book positions itself as a vital primer, preparing readers to tackle more advanced materials and encouraging them to engage with machine learning as astute listeners, experienced artists, or fledgling composers in the domain.

• Deep Learning with PyTorch [PDF]

  • "Deep Learning with PyTorch" is penned by a trio who ventured into the realms of computing and programming during the dawn of personal computers in the 1980s. Through their individual journeys in medical image analysis and neural networks, Eli, Luca, and Thomas witnessed the transformative power of deep learning, particularly as it began revolutionizing computer vision and medical image analysis in the early 2010s. Targeting readers with little to no prior knowledge of the subject, the authors aspire to offer a book that they would have found invaluable during their own early encounters with deep learning.

• Machine Learning Yearning [Email Address Required]

  • "Machine Learning Yearning," penned by Andrew Ng, serves as a practical guide for teams embarking on machine learning projects. The book’s premise revolves around optimizing the decision-making process in the development of machine learning applications. The ultimate goal is to empower the reader to act as a pivotal force within their team, significantly boosting productivity and project success by applying the book’s lessons on technical direction and project management in machine learning.

• The Elements of Statistical Learning [PDF]

  • The second edition of "The Elements of Statistical Learning" responds to the field's rapid research advancements and the book's initial success by incorporating four new chapters and updating existing content, it ntroduces chapters on Prototype Methods and Nearest-Neighbors, Unsupervised Learning, Random Forests, and Ensemble Learning, enriching the text with current methodologies like the LAR algorithm, lasso path for logistic regression, spectral clustering, and approaches to high-dimensional problems ("p ≫ N"). The book continues to serve as a comprehensive resource on statistical learning, further enhanced to meet contemporary challenges in the field.

• Computer Age Statistical Inference [PDF]

  • This book offers a panoramic view of the evolution of statistical inference over the past sixty years, tracing its journey from the era preceding electronic computation to the present-day computational intensity of machine learning. It dissects a wide array of influential topics like generalized linear models, survival analysis, and neural networks, showcasing the significant methodological advances and their theoretical underpinnings. Central to the narrative is the role of computation in transforming statistical applications, highlighting both computer-independent developments and those born from the advent of accessible computing power.

• An Introduction to Machine Learning Interpretability [PDF]

  • This book addresses the critical need for understanding and trusting models in various scientific and practical domains, challenging the traditional trade-off between model accuracy and interpretability. It brings to light recent advancements in debugging, explainability, fairness, and interpretability techniques in machine learning, providing a comprehensive guide for practitioners. Furthermore, it presents an applied taxonomy for these techniques alongside a review of the available software tools, addresses general limitations and testing approaches, and includes a collection of open-source code examples, aiming to equip readers with the knowledge to build sophisticated, transparent, and trustworthy modeling systems.

• Think Stats [PDF]

  • "Think Stats" is an introduction to exploratory data analysis that emphasizes a practical, computational approach over traditional mathematical methods. Author Allen B. Downey guides readers through a process that mirrors his own workflow with datasets: importing and cleaning data, exploring variables individually and in pairs, conducting multivariate analysis, and engaging in estimation and hypothesis testing. A significant portion of the book is dedicated to visualization as both a tool for exploration and a means of communicating results. Distinctively, "Think Stats" teaches statistical concepts through Python code rather than mathematical notation, aiming to enhance readability and interactivity.

• Think Bayes [PDF]

  • This book, part of the Think X series, is designed to leverage programming skills to explore Bayesian statistics, making the subject more accessible by using Python code instead of traditional mathematical notation. Through progressive examples—from dice rolls to the historical German tank problem—the book emphasizes model building and approximation. The book suggests that an approximate solution to a well-thought-out model is preferable to an exact solution to a flawed one and recommends starting with clear, demonstrable code, optimizing only when necessary, and using the initial model as a benchmark for testing optimized solutions. 

• Think Python [PDF]

  • "How to Think Like a Computer Scientist," now retitled "Think Python," is an instructional book by Allen B. Downey, crafted out of a desire to improve the teaching and understanding of programming. The book, which has been updated to Python 3 in its second edition, includes new sections on debugging, exercises ranging from simple tests to more elaborate projects, case studies, and a discussion on program development plans and design patterns. Released under the GNU Free Documentation License, "Think Python" encourages modification and distribution, reflecting Downey's commitment to accessible, practical education in programming.

• Machine Learning for Humans [PDF]

  • "Machine Learning for Humans" is a comprehensive introduction to machine learning, designed to demystify the field for both technical and non-technical readers. The authors, Vishal Maini and Samer Sabri, address the recent surge in artificial intelligence (AI) applications, from conversational agents and advanced game-playing algorithms to real-world applications like medical diagnosis and autonomous vehicles. Beyond technical explanations, the series aims to provoke thought on the broader implications of machine learning and AI, from perpetuating biases to shaping the future of work. Written during a productive journey across the United Kingdom, "Machine Learning for Humans" is an invitation to grasp the principles that will define our technological future.

• Machine Learning for Brain Disorders [PDF]

  • This comprehensive Springer Link Open Access book serves as a modern and extensive guide to the application and methodological nuances of machine learning (ML) in the context of brain disorders. Tailored for a diverse audience, from students venturing into this field to seasoned researchers seeking to expand their knowledge, this book bridges the gap across disciplines such as engineering, computer science, neurology, psychiatry, radiology, and neuroscience, offering detailed advice and insights from experts to achieve successful outcomes in laboratory settings. The full list of Springer Link Open Access books on artificial intelligence is available here.

• Dive Into Deep Learning [PDF][Companion Website]

  • This book chronicles the rise of deep learning from an academic interest to a pivotal technology driving advances across multiple fields, including computer vision, natural language processing, and more. This transformation has not only fueled practical innovations but also spurred theoretical advancements in machine learning and statistics. The authors aim to capture this transformation, highlighting deep learning's broad impact on industry and society, revolutionizing filmmaking, disease diagnosis, and even contributing to fundamental sciences such as astrophysics and biomedicine.

• Intermediate Python [PDF]

  • This book serves as a guide for those who have mastered the basics of Python and are looking to delve deeper into the language's less explored aspects. It is designed to cater to a wide range of programming skill levels, from beginners to advanced users, offering snippets of information on intriguing Python topics for further exploration. This work is presented as an ongoing project, inviting readers to contribute improvements and suggestions through pull requests, reflecting the author's commitment to collaborative learning and the dynamic nature of programming knowledge.

• Ethical Artificial Intelligence [PDF]

  • This book explores the complex task of designing ethical artificial intelligence (AI) systems, offering mathematical strategies to predefine AI behaviors and avoid harmful consequences. It tackles both the technical challenges and ethical considerations in AI development, advocating for design techniques rooted in utilitarian ethics and the restriction to finite sets to minimize risks associated with sophisticated AI. The author's approach, described as collectivist and intrusive, is presented as a necessary shift from libertarian views to ensure the safety and ethical soundness of powerful AI systems.

• A Brief Introduction to Neural Networks [PDF]

  • This book introduces the foundational concepts and methodologies of neural networks, emphasizing the potential for computers to learn from interactions without pre-programmed instructions, akin to biological learning mechanisms. Through exploring the history, decline, and resurgence of neural network approaches to problem-solving, the text delves into why neural networks are essential for addressing complex issues not solvable by traditional algorithms. The book aims to elucidate how neural network paradigms, capable of generalizing and associating data, can be applied to various problem classes, offering an introduction to different neural network models and practical remarks for their application, underscoring the technology's fault tolerance and self-organizing features.

• Mining of Massive Datasets [PDF]

  • "Data Mining" sets the stage for understanding the multifaceted nature of data mining, touching upon its evolution, underlying principles, and the various disciplines it encompasses. Beginning with a candid discussion about the essence of data mining, the text demystifies the term by presenting it as the discovery of models from data, exploring statistical modeling, machine learning, and computational approaches as key facets. 

• Reinforcement Learning [PDF]

  • This book delves into reinforcement learning (RL), a computational approach to learning from interaction, focusing on the development of machines that learn to make decisions through trial and error to maximize rewards. Distinct from supervised and unsupervised learning, RL is presented as a unique paradigm within machine learning, emphasizing the balance between exploration of new actions and exploitation of known actions to achieve goals. T. Reinforcement learning is depicted not just as a field of study but as a confluence of problem-solving methods, the challenges of exploration vs. exploitation, and the quest for general principles in AI, promising insights into both the engineering applications and the biological parallels of learning from interaction.

• Python Machine Learning Projects [PDF]

  • This book aims to democratize the field of machine learning by providing Python projects that enhance both the conceptual understanding and practical skills of developers. Recognizing the growing reliance on machine learning for pattern recognition, analysis, and decision-making, the book begins with setting up a Python programming environment and an introductory chapter on machine learning fundamentals. It then progresses to hands-on projects, including developing a machine learning classifier, recognizing handwritten digits through a neural network, and exploring deep reinforcement learning by building a bot for Atari games. Further resources and learning materials on Python and other programming languages and DevOps engineering can be accessed through DigitalOcean’s extensive tutorial library.