Hi Everyone! In this article, you’ll find 100+ Free Machine Learning and Deep Learning Books. We have researched a lot on the internet and collected a huge list of free deep learning and machine learning books. This eBooks will be either in PDF or in the HTML Format. This book collection is updated in 2023 and the books are suitable for beginners, intermediate and experts.
Note: We’ve added affiliate links of all the books for you if you want to buy the book. As an Amazon Associate, We may get a small amount of commission for referring you.
List is very big. So, We recommend you to check the Table Of Content first and go through all the book titles.
Table Of Contents 👉
- Some Of The Best Free Books On Machine Learning Are
- Top 6 Free Machine Learning And Deep Learning Books For Beginners And Advanced ML Enthusiasts
- 100 Books On Deep Learning, Machine Learning With Python, Mathematics, AI, NLP, TensorFlow, Etc, Are As Follows
- Probabilistic Machine Learning: An Introduction PDF
- Deep Learning for Coders with FastAI and PyTorch: AI Applications Without a PhD
- Approaching Almost Any Machine Learning Problem PDF
- Probabilistic Machine Learning: Advanced Topics PDF
- Machine Learning Engineering
- Natural Language Processing with Python
- Feature Engineering and Selection: A Practical Approach for Predictive Models
- Dive Into Deep Learning PDF
- Deep Learning (Adaptive Computation and Machine Learning series)
- Understanding Machine Learning: From Theory to Algorithms PDF
- Hands-On Machine Learning with R
- The Hundred-Page Machine Learning Book
- Mathematics for Machine Learning PDF
- Machine Learning Yearning
- Machine Learning for Humans
- Deep Learning On Graphs PDF
- Pattern Recognition and Machine Learning PDF
- Reinforcement Learning: An Introduction, 2nd Edition PDF
- Mining of Massive Datasets PDF
- Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
- Bayesian Reasoning and Machine Learning
- Data-Intensive Text Processing with MapReduce PDF
- Patterns, Predictions, And Actions: A Story About Machine Learning PDF
- Math Basics for Computer Science and Machine Learning PDF
- Model-Based Machine Learning
- Introduction to Machine Learning Interviews Book
- Deep Learning Interviews: Hundreds Of Fully Solved Job Interview Questions From A Wide Range Of Key Topics In AI PDF
- Explanatory Model Analysis : Explore, Explain, and Examine Predictive Models. With examples in R and Python
- Physics-based Deep Learning PDF
- Python Machine Learning Projects
- The Elements of Statistical Learning, 2nd Edition PDF
- An Introduction to Machine Learning Interpretability PDF
- The AI Ladder – Demystifying AI Challenges PDF
- Deep Learning: A Technical Introduction PDF
- Deep Learning with Pytorch PDF
- Artificial Intelligence by Patrick Henry Winston PDF
- Gaussian Processes for Machine Learning PDF
- Computer Vision: Algorithms and Applications PDF
- Tidy Modeling with R
- Fundamentals Of Computer Vision – A Gentle, Accessible Introduction To Foundational Concepts In Computer Vision And Computational Perception
- Practical Machine Learning in R
- Understanding Deep Learning – Application in Rare Event Prediction
- Human and Machine Consciousness PDF
- Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations PDF
- The Boundaries of Humanity: Humans, Animals, Machines
- Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics PDF
- Planning Algorithms PDF
- An Introduction to Statistical Learning with Applications in R PDF
- A Course in Machine Learning PDF
- Theory and Applications for Advanced Text Mining
- Foundations of Machine Learning PDF
- Statistical Learning and Sequential Prediction PDF
- Interpretable Machine Learning: Black Box Models Explainable
- Boosting: Foundations and Algorithms
- A Brief Introduction to Machine Learning for Engineers PDF
- The LION Way: Machine Learning Plus Intelligent Optimization PDF
- Speech and Language Processing PDF
- Machine Translation: An Introductory Guide
- Neural Networks and Deep Learning PDF
- Computer Vision: Models, Learning, and Inference PDF
- Information Theory, Inference, and Learning Algorithms PDF
- AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java PDF
- Paradigms of Artificial Intelligence Programming
- Computers and Thought: A practical Introduction to Artificial Intelligence
- Machine Learning For Dummies
- The Quest for Artificial Intelligence: A History of Ideas and Achievements PDF
- Machine Learning from Scratch
- A Comprehensive Guide to Machine Learning PDF
- Automated Machine Learning: Methods, Systems, Challenges PDF
- Algorithms for Reinforcement Learning PDF
- AI based Robot Safe Learning and Control
- Learning Deep Architectures for AI PDF
- Applied Artificial Neural Networks
- Artificial Neural Networks – Methodological Advances and Biomedical Applications
- Programming Computer Vision with Python PDF
- Artificial Intelligence: Foundations of Computational Agents
- Ethical Artificial Intelligence PDF
- The Essential AI Handbook for Leaders PDF
- Artificial Intelligence through Prolog
- Clever Algorithms – Nature-Inspired Programming Recipes PDF
- Convex Optimization PDF
- Introduction to Autonomous Robots PDF
- The World and Mind of Computation and Complexity
- An Introduction to Machine Learning
- Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
- Neural Networks and Deep Learning
- Projection-Based Clustering through Self-Organization and Swarm Intelligence
- Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks
- Advanced Applications for Artificial Neural Networks
- Neural Network Design PDF
- Memristor and Memristive Neural Networks
- Keras Succinctly
- C++ Neural Networks and Fuzzy Logic PDF
- Models of Learning and Optimization for Data Scientists – A Python hands-on approach
- A Brief Introduction to Neural Networks using Java and SNIPE PDF
- TensorFlow 2 Tutorial
- Seven Steps to Success: Machine Learning in Practice
- Algorithmic Aspects of Machine Learning PDF
- Machine Learning & Big Data
- Building Machine Learning Systems with Python PDF
- Linear Algebra for Machine Learning PDF
- Computer Age Statistical Inference PDF
- A Probabilistic Theory of Pattern Recognition PDF
- Introduction to Information Retrieval PDF
- Foundations of Statistical Natural Language Processing PDF
- From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science PDF
- Introduction to Probability For Machine Learning PDF
- Introduction to Probability and Statistics Using R PDF
- Supervised Sequence Labelling with Recurrent Neural Networks PDF
- Forecasting: Principles and Practice
- Data Mining – Practical Machine Learning Tools and Techniques
- Support Vector Machines Succinctly
- TensorFlow Roadmap
- Frequently Asked Questions:
Some Of The Best Free Books On Machine Learning Are
- Probabilistic Machine Learning: An Introduction
- Deep Learning for Coders with FastAI and PyTorch: AI Applications Without a PhD
- The Hundred-Page Machine Learning Book
- Machine Learning Engineering
- Introduction to Machine Learning Interviews Book
- Feature Engineering and Selection: A Practical Approach for Predictive Models
- Mathematics for Machine Learning
Top 6 Free Machine Learning And Deep Learning Books For Beginners And Advanced ML Enthusiasts
|Machine Learning Books For Beginners And Experts||Suitable For||Author||Ratings||Read Or Download Link|
|1. The Hundred Page Machine Learning Book||Beginners||Andriy Burkov||4.6/5 Star By 830 Readers||Read|
|2. Machine Learning For Humans||Beginners||Vishal Maini And Samer Sabri||4.1/5 Star By 75 Readers||Download|
|3. Mathematics For Machine Learning||Beginners And Advanced||Marc Peter Deisenroth, A. Aldo Faisal, And Cheng Soon Ong||4.7/5 Star By 575 Readers||Download|
|4. Approaching (Almost) Any Machine Learning Problem||Advanced||Abhisek Thakur||4.5/5 Star By 675 Readers||Download|
|5. Reinforcement Learning: An Introduction||Beginners||Richard S. Sutton And Andrew G. Barto||4.6/5 Star By 109 Readers||Download|
|6. Deep Learning (Adaptive Computation and Machine Learning series)||Beginners And Advanced||Ian Goodfellow, Yoshua Bengio, And Aaron Courville||4.3/5 Star By 1892 Readers||Read|
100 Books On Deep Learning, Machine Learning With Python, Mathematics, AI, NLP, TensorFlow, Etc, Are As Follows
Author: Kevin P Murphy
About Probabilistic Machine Learning: An Introduction PDF:
This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. Learn more about this machine learning book by visiting the below given link.
Authors: Jeremy Howard And Sylvain Gugger
About Deep Learning for Coders with Fastai and PyTorch PDF:
This hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. Learn more about this deep learning learning book by visiting the below given link.
Author: Abhisek Thakur
About Approaching (Almost) Any Machine Learning Problem Book:
This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn’t explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems.
Author: Kevin P Murphy
About Probabilistic Machine Learning: Advanced Topics Book:
In this book, we expand the scope of ML to encompass more challenging
problems. We assume the reader has some prior exposure to (supervised) ML and other relevant mathematical topics (e.g., probability, statistics, linear algebra, optimization). To learn more about this machine learning ebook, visit the below given link.
Author: Andriy Burkov
What experts says about Machine Learning Engineering PDF:
The most comprehensive book on the engineering aspects of building reliable AI systems. “If you intend to use machine learning to solve business problems at scale, I’m delighted you got your hands on this book.” – Cassie Kozyrkov, Chief Decision Scientist at Google
Author: Steven Bird, Ewan Klein, Edward Loper
About Natural Language Processing with Python PDF:
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.
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Authors: Max Kuhn and Kjell Johnson
About Feature Engineering and Selection PDF:
The goals of Feature Engineering and Selection are to provide tools for re-representing predictors, to place these tools in the context of a good predictive modeling framework, and to convey our experience of utilizing these tools in practice.
Author: Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola
About Dive Into Deep Learning Book:
An interactive deep learning book with code, math, and discussions. Table of Content of this eBook includes
- The Preliminaries: A Crashcourse
- Linear Neural Networks
- Multilayer Perceptrons
- Deep Learning Computation
- Convolutional Neural Networks
- Modern Convolutional Networks
- And More
Author: Aaron Courville, Ian Goodfellow, and Yoshua Bengio
About Deep Learning (Adaptive Computation and Machine Learning series) PDF:
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. Learn more about this deep learning book by visiting the below given link.
Author: Shai Shalev-Shwartz and Shai Ben-David
About Understanding Machine Learning: From Theory to Algorithms Book:
This book gives a structured introduction to machine learning. It looks at the fundamental theories of machine learning and the mathematical derivations that transform these concepts into practical algorithms. Following that, it covers a list of ML algorithms, including (but not limited to), stochastic gradient descent, neural networks, and structured output learning.
Authors: Bradley Boehmke & Brandon Greenwell
About Hands-On Machine Learning with R PDF:
In this eBook, You will learn how to build and tune these various models with R packages that have been tested and approved due to their ability to scale well. This book is not meant to be an introduction to R or to programming in general; as we assume the reader has familiarity with the R language to include defining functions, managing R objects, controlling the flow of a program, and other basic tasks.
Author: Andriy Burkov
About The Hundred-Page Machine Learning Book PDF:
Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. Learn more about this machine learning book by visiting the below given link.
Author: A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth
About Mathematics for Machine Learning Book:
If you ever need a place to start learning about the maths behind machine learning, then this a highly recommended book. This book provides great coverage of all the basic mathematical concepts for machine learning. Learn more about this mathematical for machine learning book by visiting the below given link.
Machine Learning Yearning
Author: Andrew Ng
About Machine Learning Yearning PDF:
After reading Machine Learning Yearning, you will be able to:
- Prioritize the most promising directions for an AI project
- Diagnose errors in a machine learning system
- Build ML in complex settings, such as mismatched training/test sets
- Set up an ML project to compare to and/or surpass human-level performance
- Know when and how to apply end-to-end learning, transfer learning, and multi-task learning.
Machine Learning for Humans
Authors: Vishal Maini and Samer Sabri
About Machine Learning for Humans PDF:
- Technical people who want to get up to speed on machine learning quickly
- Non-technical people who want a primer on machine learning and are willing to engage with technical concepts
- Anyone who is curious about how machines think
Authors: Yao Ma and Jiliang Tang
About Deep Learning on Graphs Book:
This book covers comprehensive contents in developing deep learning techniques for graph structured data with a specific focus on Graph Neural Networks (GNNs). The foundation of the GNN models are introduced in detail including the two main building operations: graph filtering and pooling operations.
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Author: Christopher M. Bishop
About Pattern Recognition and Machine Learning Book:
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.
Author: Richard S. Sutton, Andrew G. Barto
About Reinforcement Learning: An Introduction Book:
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. Learn more about this reinforcement learning book by visiting the below given link.
Author: by Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
About Mining of Massive Datasets Book:
This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically.
Author: Cameron Davidson
About Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference PDF:
Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.
Author: David Barber
About Bayesian Reasoning and Machine Learning PDF:
This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master’s students with limited background in linear algebra and calculus. Learn more about this best book on Bayesian machine learning from the below given link.
Author: by Jimmy Lin & Chris Dyer
About Data-Intensive Text Processing with MapReduce Book:
This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. Learn more about this text processing book by visiting the below given link.
Authors: Moritz Hardt and Benjamin Recht
About Patterns, Predictions, And Actions: A Story About Machine Learning Book:
This graduate textbook on machine learning tells a story of how patterns in data support predictions and consequential actions. Starting with the foundations of decision making, we cover representation, optimization, and generalization as the constituents of supervised learning.
Math Basics for Computer Science and Machine Learning PDF
Author: Jean Gallier and Jocelyn Quaintance
About Math Basics for Computer Science and Machine Learning Book:
A great book regarding math for machine learning with around 2,000 pages.
Model-Based Machine Learning
Authors: Christopher Bishop And John Winn
About Model-Based Machine Learning PDF:
This book is unusual for a machine learning text book in that the authors do not review dozens of different algorithms. Instead they introduce all of the key ideas through a series of case studies involving real-world applications. Learn more about this machine learning book by visiting the below given link.
Introduction to Machine Learning Interviews Book
Author: Hueyen Chip
About This eBook:
The book consists of two parts. The first part provides an overview of the machine learning interview process, what types of machine learning roles are available, what skills each role requires, what kinds of questions are often asked, and how to prepare for them. The second part consists of over 200 knowledge questions, each noted with its level of difficulty.
Deep Learning Interviews: Hundreds Of Fully Solved Job Interview Questions From A Wide Range Of Key Topics In AI PDF
Authors: Shlomo Kashani And Amir Ivry
About Deep Learning Interviews PDF:
The second edition of Deep Learning Interviews is home to hundreds of fully-solved problems, from a wide range of key topics in AI. Learn more about this deep learning book by visiting the below given link.
Explanatory Model Analysis : Explore, Explain, and Examine Predictive Models. With examples in R and Python
Author: Przemyslaw Biecek and Tomasz Burzykowski
About Explanatory Model Analysis PDF:
This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.
Physics-based Deep Learning PDF
Authors: Nils Thuerey, Philipp Holl and More
About Physics-based Deep Learning Book:
This document contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. Learn more about this deep learning book by visiting the below given link.
Python Machine Learning Projects
Author: Lisa Tagliaferri & Brian Boucheron
About Python Machine Learning Projects PDF:
This book will set you up with a Python programming environment if you don’t have one aly, then provide you with a conceptual understanding of machine learning in the chapter “An Introduction to Machine Learning.” What follows next are three Python machine learning projects. Learn more about this best python machine learning book from the below given link.
Author: Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
About The Elements of Statistical Learning, 2nd Edition Book:
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Learn more about this statistical learning book pdf by visiting the below given link.
An Introduction to Machine Learning Interpretability PDF
Author: Patrick Hall & Navdeep Gill
About An Introduction to Machine Learning Interpretability Book:
This book is recommended reading for all practitioners wanting to adopt recent and disruptive breakthroughs in debugging, explainability, fairness, and interpretability techniques for machine learning.
The AI Ladder – Demystifying AI Challenges PDF
Author: Rob Thomas
About The AI Ladder – Demystifying AI Challenges Book:
This mini book introduces a roadmap that will help companies without the benefit of years of advanced AI research and hundreds of deep learning PhDs to take advantage of one of the next big steps forward in computing.
Deep Learning: A Technical Introduction PDF
Author: Thomas Epelbaum
About Deep Learning: A Technical Introduction Book:
This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent.
Author: Eli Stevens, Luca Antiga, & Thomas Viehmann
About Deep Learning with Pytorch Book:
Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch. Learn more about this deep learning with pytorch book by visiting the below given link.
Author: Patrick Henry Winston
About Artificial Intelligence Book:
This book explains how it is possible for computers to reason and perceive, thus introducing the field called artificial intelligence. From the book, you learn why the field is important, both as a branch of engineering and as a science.
Author: Carl Edward Rasmussen & Christopher K. I. Williams
About Gaussian Processes for Machine Learning Book:
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Learn more about this gaussian process book by visiting the below given link.
Author: Richard Szeliski
About Computer Vision: Algorithms and Applications Book:
Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. Learn more about this computer vision book by visiting the below given link.
Author: Max Kuhn and Julia Silge
About Tidy Modeling with R PDF:
First and foremost, this book provides an introduction to how to use our software to create models. We focus on a dialect of R called the tidyverse that is designed to be a better interface for common tasks using R. Learn more about this R programming book by visiting the below given link.
Fundamentals Of Computer Vision – A Gentle, Accessible Introduction To Foundational Concepts In Computer Vision And Computational Perception
Author: George K
About Fundamentals of Computer Vision PDF:
Have you ever been curious about how your phone unlocks when it sees your face, how a camera can track people and objects in a video, how humans see depth, or how computers can differentiate dogs from cats? This book will start from the basics of image manipulation and build up to cover all of these topics, and more!
Practical Machine Learning in R
Author: Kyriakos Chatzidimitriou, Themistoklis Diamantopoulos, Michail Papamichail, and Andreas Symeonidis
About Practical Machine Learning in R PDF:
The book is about quickly entering the world of creating machine learning models in R. The theory is kept to minimum and there are examples for each of the major algorithms for classification, clustering, features engineering and association rules. Learn more about this machine learning with R book by visiting the below given link.
Author: Chitta Ranjan
About Understanding Deep Learning PDF:
Think of deep learning as an art of cooking. One way to cook is to follow a recipe. But when we learn how the food, the spices, and the fire behave, we make our creation. And an understanding of the “how” transcends the creation.
Author: David Gamez
About Human and Machine Consciousness PDF:
Gamez explains difficult concepts in a clear way that closely engages with scientific research. His punchy, concise prose is packed with vivid examples, making it suitable for the educated general er as well as philosophers and scientists.
Author: Yoav Shoham & Kevin Leyton-Brown
About Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations Book:
This exciting and pioneering new overview of multiagent systems, which are online systems composed of multiple interacting intelligent agents, i.e., online trading, offers a newly seen computer science perspective on multiagent systems, while integrating ideas from operations research, game theory, etc.
Author: James J. Sheehan & Morton Sosna
About The Boundaries of Humanity: Humans, Animals, Machines:
To the age-old debate over what it means to be human, the relatively new fields of sociobiology and artificial intelligence bring new, if not necessarily compatible, insights. What have these two fields in common? Have they affected the way we define humanity? These and other timely questions are addressed with colorful individuality by the authors of The Boundaries of Humanity.
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics PDF
Author: Justin Solomon
About Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics:
The book covers a wide range of topics—from numerical linear algebra to optimization and differential equations—focusing on real-world motivation and unifying themes.
Planning Algorithms PDF
Author: Steven M. LaValle
About Planning Algorithms Book:
Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding.
Author: Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani
About An Introduction to Statistical Learning with Applications in R Book:
This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.
A Course in Machine Learning PDF
Author: Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin
About A Course in Machine Learning Book:
If you’re looking to get started with the key concepts of Machine Learning, then you’ll love this book: easy to follow, simple, and clean. It’s probably the best resource after the Andrew Ng courses to get started!
Author: Shigeaki Sakurai
About Theory and Applications for Advanced Text Mining Book:
This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields.
Author: Mehryar Mohri, Afshin Rostamizadeh & Ameet Talwalkar
About Foundations of Machine Learning Mohri Book:
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application.
Statistical Learning and Sequential Prediction PDF
Author: Alexander Rakhlin & Karthik Sridharan
About Statistical Learning and Sequential Prediction:
This book focuses on theoretical aspects of Statistical Learning and Sequential Prediction. Until recently, these two subjects have been treated separately within the learning community. Learn more about this statistical learning book by visiting the below given link.
Author: Christoph Molnar
About Interpretable Machine Learning PDF:
The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. reading the book is recommended for machine learning practitioners, data scientists, statisticians, etc.
Author: by Robert E. Schapire & Yoav Freund
About Boosting: Foundations and Algorithms PDF:
An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones.
Author: Osvaldo Simeone
About A Brief Introduction to Machine Learning for Engineers Book:
There is a wealth of literature and books available to engineers starting to understand what machine learning is and how it can be used in their everyday work. Learn more about this machine learning book by visiting the below given link.
The LION Way: Machine Learning Plus Intelligent Optimization PDF
Author: Roberto Battiti & Mauro Brunato
About The LION Way: Machine Learning Plus Intelligent Optimization Book:
The LION way is about increasing the automation level and connecting data directly to decisions and actions. More power is directly in the hands of decision makers in a self-service manner, without resorting to intermediate layers of data scientists.
Author: Daniel Jurafsky & James Martin
About Speech and Language Processing eBook:
This book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. Learn more about this Speech and Language Processing book by visiting the below given link.
Machine Translation: An Introductory Guide
Author: D. Arnold
About Machine Translation: An Introductory Guide PDF:
This introductory book looks at all aspects of Machine Translation: covering questions of what it is like to use a modern Machine Translation system, through questions about how it is done, to questions of evaluating systems, and what developments can be foreseen in the near to medium future.
Neural Networks and Deep Learning PDF
Author: Michael Nielsen
About Neural Networks and Deep Learning Book:
The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning. After working through the book you will have written code that uses neural networks and deep learning to solve complex pattern recognition problems. Learn more about this neural network book by visiting the below given link.
Author: Simon J. D. Prince
About Computer Vision: Models, Learning, and Inference Book:
With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the er can implement and modify to build useful vision systems. This book is primarily meant for advanced undergraduate and graduate students.
Author: David J. C. MacKay
About Information Theory, Inference, and Learning Algorithms Book:
This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction.
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AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java PDF
Author: George F. Luger and William A. Stubblefield
About AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java Book:
This book illustrates how to program AI algorithms in Lisp, Prolog, and Java. The book basically cover each topic 3 times in each language. Topics include: simple production-like system based on logic, logic-based learning, and natural language parsing.
Author: Peter Norvig
About Paradigms of Artificial Intelligence Programming PDF:
Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems. The book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts.
Author: Mike Sharples & David Hogg
About Computers and Thought: A practical Introduction to Artificial Intelligence PDF:
This textbook is a unified, self-contained introduction to artificial intelligence with little or no computing background. The text presents original AI programming projects throughout to illustrate the material covered and to show how AI actually works.
Author: Judith Hurwitz and Daniel Kirsch
About Machine Learning For Dummies PDF:
Machine Learning For Dummies, IBM Limited Edition, gives you insights into what machine learning is all about and how it can impact the way you can weaponize data to gain unimaginable insights. In this book, you discover types of machine learning techniques, models, and algorithms that can help achieve results for your company.
Author: Nils J. Nilsson
About The Quest for Artificial Intelligence: A History of Ideas and Achievements Book:
This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today’s AI engineers. AI is becoming more and more a part of everyone’s life.
Machine Learning from Scratch
About Machine Learning from Scratch PDF:
This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Each chapter in this book corresponds to a single machine learning method or group of methods.
A Comprehensive Guide to Machine Learning PDF
Author: Soroush Nasiriany
About A Comprehensive Guide to Machine Learning Book:
You’ll Learn about
- Dimensionality Reduction
- Beyond Least Squares: Optimization and Neural Networks
- Decision Tree Learning
- Deep Learning
Author: Frank Hutter, Joaquin Vanschoren, and Lars Kotthoff
About Automated Machine Learning: Methods, Systems, Challenges Book:
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems.
Author: Csaba Szepesvari, Ronald Brachman & Thomas Dietterich
About Algorithms for Reinforcement Learning Book:
In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
AI based Robot Safe Learning and Control
Author: Xuefeng Zhou, Hongmin Wu, Zhihao Xu, Xiaojing Lv, Taobo Cheng, Shuai Li
About AI based Robot Safe Learning and Control PDF:
The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc.
Learning Deep Architectures for AI PDF
Author: Yoshua Bengio
About Learning Deep Architectures for AI Book:
Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one would need deep architectures.
Applied Artificial Neural Networks
Author: Christian Dawson
About Applied Artificial Neural Networks PDF:
This Special Issue focuses on the second of these two research themes, that of the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.
Artificial Neural Networks – Methodological Advances and Biomedical Applications
Author: Kenji Suzuki
About Artificial Neural Networks PDF:
The purpose of this book is to provide recent advances of artificial neural networks in biomedical applications. The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization.
Author: Jan Erik Solem
About Programming Computer Vision with Python Book:
If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python.
Author: David L. Poole & Alan K. Mackworth
About Artificial Intelligence: Foundations of Computational Agents PDF:
Artificial Intelligence: Foundations of Computational Agents is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents.
Ethical Artificial Intelligence PDF
Author: Bill Hibbard
About Ethical Artificial Intelligence Book:
This book-length article combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence (AI). This articles makes the case for utility-maximizing agents and for avoiding infinite sets in agent definitions.
The Essential AI Handbook for Leaders PDF
Author: Luka Crnkovic-Friis, Sebastien Plassard, Kye Andersson & Marcus Wallenberg
About The Essential AI Handbook for Leaders Book:
Artificial Intelligence has the power to advance humankind more than fire and electricity. Everywhere. We believe it is of greatest importance that AI knowledge and technology is available, usable and affordable for all – not only the big and powerful.
Artificial Intelligence through Prolog
Author: Neil C. Rowe
About Artificial Intelligence through Prolog PDF:
You’ll Learn About
- Variables and queries
- Arithmetic and lists in Prolog
- Control structures for rule-based systems
- Implementation of rule-based systems
- Representing uncertainty in rule-based systems
- And more
Clever Algorithms – Nature-Inspired Programming Recipes PDF
Author: Jason Brownlee
About Clever Algorithms – Nature-Inspired Programming Recipes Book:
This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete, consistent, and centralized manner. Learn more about this algorithms book by visiting the below given link.
Author: Lieven Vandenberghe, Stephen Boyd, and Stephen P. Boyd
About Convex Optimization Book:
“The focus of the book is on recognizing and formulating convex optimization problems, and then solving them efficiently. It contains many worked examples and homework exercises and will appeal to students, researchers, and practitioners in fields such as engineering, computer science, mathematics, finance, and economics.”
Author: Nikolaus Correll
About Introduction to Autonomous Robots Book:
This book introduces concepts in mobile, autonomous robotics to 3rd-4th year students in Computer Science or a related discipline. The book covers principles of robot motion, forward and inverse kinematics of robotic arms and simple wheeled platforms, perception, error propagation, localization and simultaneous localization and mapping.
The World and Mind of Computation and Complexity
Author: Gregg Schaffter
About The World and Mind of Computation and Complexity PDF:
With the increase in development of technology, there is research going into the development of human-like artificial intelligence that can be self-aware and act just like humans. This book explores the possibilities of artificial intelligence and how we may be close to developing a true artificially intelligent being.
An Introduction to Machine Learning
Author: Miroslav Kubat
About An Introduction to Machine Learning PDF:
This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, etc.
Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
Author: Mariette Awad and Rahul Khanna
About Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers PDF:
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models.
Author: Charu C. Aggarwal
About Neural Networks and Deep Learning PDF:
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications.
Projection-Based Clustering through Self-Organization and Swarm Intelligence
Author: Michael Christoph Thrun
About Projection-Based Clustering through Self-Organization and Swarm Intelligence PDF:
This open access book covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS).
Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks
Author: Erik R. Ranschaert, Sergey Morozov & Paul R. Algra
About Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks PDF:
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling ers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging.
Advanced Applications for Artificial Neural Networks
Author: Adel El-Shahat
About Advanced Applications for Artificial Neural Networks PDF:
In this book, highly qualified multidisciplinary scientists grasp their recent researches motivated by the importance of artificial neural networks. Learn more about this neural network book by visiting the below given link.
Neural Network Design PDF
Author: Martin T. Hagan
About Neural Network Design Book:
This book provides a clear and detailed survey of basic neural network architectures and learning rules. In it, the authors emphasize mathematical analysis of networks, methods for training networks, and application of networks to practical engineering problems in pattern recognition, signal processing, and control systems.
Memristor and Memristive Neural Networks
Author: Alex James
About Memristor and Memristive Neural Networks:
This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: (1) Devices, (2) Models and (3) Applications.
Author: James McCaffrey
About Keras Succinctly PDF:
Neural networks are a powerful tool for developers, but harnessing them can be a challenge. With Keras Succinctly, author James McCaffrey introduces Keras, an open-source, neural network library designed specifically to make working with backend neural network tools easier.
Author: Valluru Rao
About C++ Neural Networks and Fuzzy Logic PDF:
Provides a logical and easy-to-follow presentation of introductory and advanced topics in Neural Network technology. The authors provide numerous examples in C++ for use with most C++ compilers, including Borland and Microsoft.
Models of Learning and Optimization for Data Scientists – A Python hands-on approach
Author: Sergio Rojas
About Models of Learning and Optimization for Data Scientists PDF:
This book has been designed to introduce newcomers to the essentials of Data Science using a hands-on approach rather than a theoretical perspective. For this aim, it addresses two of its most important branches: Machine Learning and Metaheuristics.
A Brief Introduction to Neural Networks using Java and SNIPE PDF
Author: David Kriesel
About A Brief Introduction to Neural Networks using Java and SNIPE Book:
This book introduces the Java programmer to the world of Neural Networks and Artificial Intelligence using SNIPE. SNIPE is a well-documented JAVA library that implements a framework for neural networks in a speedy, feature-rich and usable way.
TensorFlow 2 Tutorial
Author: Ren Zhang
About Tensorflow 2 Tutorial PDF:
This book is a somewhat intermediate-level introduction to Tensorflow 2. We will eventually cover everything tf.keras, but no so fast until we implemented them with raw tffirst.
Seven Steps to Success: Machine Learning in Practice
Author: Daoud Clarke
About Seven Steps to Success: Machine Learning in Practice PDF:
Non Technical product managers and non-machine Learning software engineers entering the field should not miss this tutorial. Very well written (Slightly old and doesn’t cover Deep Learning, but works for all practical purposes).
Algorithmic Aspects of Machine Learning PDF
Author: Ankur Moitra
About Algorithmic Aspects of Machine Learning Book:
This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy.
Machine Learning & Big Data
Author: Kareem Alkaseer
About Machine Learning & Big Data PDF:
This is a work in progress, which I add to as time allows. The purpose behind it is to have a balance between theory and implementation for the software engineer to implement machine learning models comfortably without relying too much on libraries.
Author: Luis Pedro Coelho and Willi Richert
About Building Machine Learning Systems with Python Book:
Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling, basket analysis, etc, are covered.
Linear Algebra for Machine Learning PDF
Author: Jim Hefferon
About Linear Algebra Book:
This text covers a standard first course : Gauss’s method, vector spaces, linear maps and matrices, determinants, and eigenvalues and eigenvectors. In addition, each chapter ends with some topics such as brief applications. Learn more about this linear algebra for machine learning book by visiting the below given link.
Author: Bradley Efron and Trevor Hastie
About Computer Age Statistical Inference Book:
This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories – Bayesian, frequentist, Fisherian – individual chapters take up a series of influential topics: survival analysis, logistic regression, etc.
A Probabilistic Theory of Pattern Recognition PDF
Author: Luc Devroye
About A Probabilistic Theory of Pattern Recognition Book:
Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast – moving field.
Author: Christopher D. Manning, Hinrich Schütze, and Prabhakar Raghavan
About Introduction to Information Retrieval Book:
Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.
Author: Christopher D. Manning and Hinrich Schütze
About Foundations of Statistical Natural Language Processing Book:
This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools.
From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science PDF
Author: Norm Matloff
About From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science Book:
This is a textbook for a course in mathematical probability and statistics for computer science students. Computer science examples are used throughout, in areas such as: computer networks, data and text mining, computer security, remote sensing, etc..
Introduction to Probability For Machine Learning PDF
Author: Joseph K. Blitzstein & Jessica Hwang
About Introduction to Probability Book:
The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject, as well as the fundamental concepts and methods of statistical inference, both Bayesian and classical.
Introduction to Probability and Statistics Using R PDF
Author: G. Jay Kerns
About Introduction to Probability and Statistics Using R Book:
This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Learn more about this statistics and probability for machine learning book by visiting the below given link.
Supervised Sequence Labelling with Recurrent Neural Networks PDF
Author: Alex Graves
About Supervised Sequence Labelling with Recurrent Neural Networks Book:
The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realize this goal.
Author: James McCaffrey
James McCaffrey leads you through the fundamental concepts of neural networks, including their architecture, input-output, tanh and SoftMax activation, back-propagation, error and accuracy, normalization and encoding, and model interpretation.
Author: George Athanasopoulos and Rob J. Hyndman
About Forecasting: Principles and Practice:
This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors’ own consulting experience.
Author: by Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal
About Data Mining – Practical Machine Learning Tools and Techniques:
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real world data mining situations.
Support Vector Machines Succinctly
Author: Alexandre Kowalczyk
About Support Vector Machines Succinctly:
In Support Vector Machines Succinctly, author Alexandre Kowalczyk guides readers through the building blocks of SVMs, from basic concepts to crucial problem-solving algorithms. By the end of the book, SVMs should be an important tool in the reader’s machine-learning toolbox.
Author: Amirsina Torfi
About TensorFlow Roadmap PDF:
A deep learning is of great interest these days, the crucial necessity for rapid and optimized implementation of the algorithms and designing architectures is the software environment.
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Frequently Asked Questions:
Which Book Is Best For Machine Learning For Beginners?
Machine Learning for Humans By Vishal Maini and Samer Sabri. This book is for all i.e. For Technical people who want to get up to speed on machine learning quickly, Non-technical people who want a primer on machine learning and anyone who is curious about how machines think.
What Are The Best Deep Learning Books For Beginners?
Best Deep Learning Books For Beginners, Intermediate And Experts Are 1. Deep Learning for Coders with Fastai and PyTorch, 2. Approaching Almost Any Machine Learning Problem, 3. Dive Into Deep Learning, 4. Deep Learning (Adaptive Computation and Machine Learning series), 5. Neural Networks and Deep Learning.
What Is The Best Book For Deep Learning With Python?
The two best books for those who wants to learn deep learning with python are 1. Deep Learning for Coders with Fastai and PyTorch by Jeremy Howard And 2. Deep Learning with Python by François Chollet. These books are suitable for dummies as well as advanced ML enthusiasts.
What Is The Best Book For Machine Learning With Python?
There are more than one best Machine Learning python books. Three Best Machine Learning With Python Books For Newbies, Intermediate And Advanced ML Enthusiasts Are 1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2. Deep Learning with Python And 3. 100 Page Machine-Learning Book.
What Are The Best Math Books For Machine Learning?
The four best books for those who wants to learn mathematics for machine learning are 1. Mathematics for Machine Learning Book, 2. Linear Algebra by Jim Hefferon, 3. Probabilistic Machine Learning: Advanced Topics, Probability and 4. Statistical Inference by Hogg And Probabilistic Machine Learning: Beginners Topics.