Artificial Intelligence Ebook Free Download Pdf
Hi Everyone! Today we're gonna list out 100+ Free Machine Learning Books and Free Artificial Intelligence Books. We have researched a lot on the internet and collected a huge list of free machine learning books pdf for you guys. This all books will be either in PDF or in the Web Format. You can download the this list of best machine learning books for beginners and experts from the below given links.
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List of 100+ free as well as some of the Best Machine Learning and Artificial Intelligence Books are
Vishal Maini and Samer Sabri
Who should read this? - 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 This guide is intended to be accessible to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn't necessary to have prior knowledge of them to gain value from this series. In short, this book contains simple, plain-English explanations accompanied by math, code, and real-world examples.
Author: Kevin P Murphy
About this Special eBook:
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. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.
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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. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.
👉 Fundamentals of Computer Vision - A gentle, accessible introduction to foundational concepts in computer vision and computational perception
About this special eBook: 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 Kyriakos Chatzidimitriou, Themistoklis Diamantopoulos, Michail Papamichail, and Andreas Symeonidis About this Special eBook: 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. The book is a compilation of the leaflets the authors give to their students during the practice labs, in the courses of Pattern Recognition and Data Mining, in the Electrical and Computer Engineering Department of the Aristotle University of Thessaloniki
👉 Understanding Deep Learning - Application in Rare Event Prediction About this Special eBook: 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. Likewise, an understanding of the "how" transcends deep learning. In this spirit, this book presents the deep learning constructs, their fundamentals, and how they behave. Baseline models are developed alongside, and concepts to improve them are exemplified.
Author: Andriy Burkov
What experts says about this book:
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 "Foundational work about the reality of building machine learning models in production." - Karolis Urbonas, Head of Machine Learning and Science at Amazon.
Author: Steven Bird, Ewan Klein, Edward Loper
About This Special eBook:
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. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.
Author: Mark Needham & Amy E. Hodler About This Special eBook: Whether you are building dynamic network models or forecasting real-world behavior, this book illustrates how graph algorithms deliver value: from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions.
👉 An Introduction to Machine Learning Interpretability PDF
Author: Patrick Hall & Navdeep Gill
About This 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 This Special eBook: 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.
Author: Aaron Courville, Ian Goodfellow, and Yoshua Bengio About This Special eBook: The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Author:Shai Shalev-Shwartz and Shai Ben-David About This Special eBook: 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.
Author: Andriy Burkov
Why this Book is Special?
Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."
Author: Jakub Langr and Vladimir Bok About This Special eBook: GANs in Action teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. In this book, you'll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator networks.
Author: Aditya Bhargava About This Special eBook:
Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.
Author: Francois Chollet
About This Special Book:
This book is widely considered to be the Bible of Deep Learning. Written by three experts, including one of the godfathers of the field, this is the most comprehensive book you can find on the subject. The book is extremely technical & full of math, but the authors do a great job at explaining everything. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology, etc.
Author: A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth About This Special eBook: 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.
👉 Machine Learning Yearning PDF
Author: Andrew Ng
What You'll Learn after reading this book:
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.
Author: Christopher M. Bishop About This Special eBook: 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. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Author: Richard S. Sutton, Andrew G. Barto
About This eBook:
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. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
Author: by Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman About This Special eBook: 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. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent item sets and clustering.
Author: Cameron Davidson About This Special eBook: 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 This Special eBook: 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. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Author: by Jimmy Lin & Chris Dyer About This Special eBook: This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks 👉 Python Machine Learning Projects (Digital Ocean) Author: Lisa Tagliaferri & Brian Boucheron About This Special eBook: This book will set you up with a Python programming environment if you don't have one already, 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. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari. Author: Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie About This Special eBook: 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. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. Author:Eli Stevens, Luca Antiga, & Thomas Viehmann About This Special eBook: 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: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. Author: Patrick Henry Winston About This Special eBook: 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. If you are a computer scientist or an engineer, you will enjoy the book, because it provides a cornucopia of new ideas for representing knowledge, using knowledge, and building practical systems. If you are a psychologist, biologist, linguist, or philosopher, you will enjoy the book because it provides an exciting computational perspective on the mystery of intelligence. Author: Carl Edward Rasmussen & Christopher K. I. Williams About This Special eBook: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. Author: Richard Szeliski About This Special eBook: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. Author: David Gamez About This Special eBook: Human and Machine Consciousness also provides original insights into unusual conscious experiences, such as hallucinations, religious experiences and out-of-body states, and demonstrates how 'designer' states of consciousness could be created in the future. 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 reader as well as philosophers and scientists. Yoav Shoham & Kevin Leyton-Brown About This Special eBook: 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, economics, logic, and even philosophy and linguistics. The authors emphasize foundations to create a broad and rigorous treatment of their subject, with thorough presentations of distributed problem solving, game theory, multiagent communication and learning, social choice, mechanism design, auctions, cooperative game theory, and modal logics of knowledge and belief 👉 The Boundaries of Humanity: Humans, Animals, Machines James J. Sheehan & Morton Sosna About This Special eBook: 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 About This Special eBook: 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. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students' intuition while introducing extensions of the basic material. The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background. Steven M. LaValle About This Special eBook: 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. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. Difficult mathematical material is explained through hundreds of examples and illustrations. Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani About This Special eBook: 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. Color graphics and real-world examples are used to illustrate the methods presented. 👉 A Course in Machine Learning Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin About This eBook: 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! This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data. 👉 Theory and Applications for Advanced Text Mining Author:Shigeaki Sakurai About This Special eBook: 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 This Special eBook: 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. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. 👉 Statistical Learning and Sequential Prediction Alexander Rakhlin & Karthik Sridharan About This Special eBook: 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. It follows a unified approach to analyzing learning in both scenarios. To make this happen, it brings together ideas from probability and statistics, game theory, algorithms, and optimization. It is this blend of ideas that makes the subject interesting for us, and authors hope to convey the excitement. Author: Nishant Shukla About This Special eBook: Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. 👉 Interpretable Machine Learning: Black Box Models Explainable Focus of this eBook: 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, and anyone else interested in making machine learning models interpretable. Author:by Robert E. Schapire & Yoav Freund About This Special eBook: 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. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. 👉 A Brief Introduction to Machine Learning for Engineers Author:Osvaldo Simeone About This Special eBook: 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. A Brief Introduction to Machine Learning for Engineers is the entry point to machine learning for students, practitioners, and researchers with an engineering background in probability and linear algebra. Author: Roberto Battiti & Mauro Brunato About This Special eBook: 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. LION is a complex array of mechanisms, like the engine in an automobile, but the user (driver) does not need to know the inner workings of the engine in order to realize its tremendous benefits. Author: Daniel Jurafsky & James Martin About This Special eBook: An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology – at all levels and with all modern technologies – this book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations 👉 Machine Translation: An Introductory Guide About This Special eBook: 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 What's the purpose of this 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. And you will have a foundation to use neural networks and deep learning to attack problems of your own devising 👉 Computer Vision: Models, Learning, and Inference Author:Simon J. D. Prince About This Special eBook: With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. 👉 Information Theory, Inference, and Learning Algorithms David J. C. MacKay About This Special eBook: 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. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. 👉 AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java Author: George F. Luger and William A. Stubblefield About This Special eBook: 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: Luis Serrano About This Special eBook: In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. Practical examples illustrate each new concept to ensure you're grokking as you go. You'll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert.
Author: Cole Howard, Hannes Hapke, and Hobson Lane About This Special eBook: Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.
Author: Carl S. Gold About This Special eBook: Fighting Churn with Data teaches developers and data scientists proven techniques for stopping churn before it happens. Packed with real-world use cases and examples, this book teaches you to convert raw data into measurable behavior metrics, calculate customer lifetime value, and improve churn forecasting with demographic data. By following Zuora Chief Data Scientist Carl Gold's methods, you'll reap the benefits of high customer retention.
Author: Peter Norvig About This Special eBook: Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems. By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. 👉 Computers and Thought: A practical Introduction to Artificial Intelligence
Author: Mike Sharples & David Hogg About This Special eBook: This textbook is a unified, self-contained introduction to artificial intelligence for readers 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.
👉 Machine Learning For Dummies (IBM Edition)
Author: Judith Hurwitz and Daniel Kirsch About This Special eBook: 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. Your data is only as good as what you do with it and how you manage it. In this book, you discover types of machine learning techniques, models, and algorithms that can help achieve results for your company. This information helps both business and technical leaders learn how to apply machine learning to anticipate and predict the future.
Author: Andrew W. Trask
What this book Teaches? Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks.
Author:Tommaso Teofili About This Special eBook: Deep Learning for Search teaches you how to improve the effectiveness of your search by implementing neural network-based techniques. By the time you're finished with the book, you'll be ready to build amazing search engines that deliver the results your users need and that get better as time goes on!
Author: Nils J. Nilsson About This Special eBook: 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. The technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots, among other applications.
Author:Oliver Durr, Beate Sick, Elvis Murina About This Special eBook: Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications.
Author: Stuart J. Russell & Peter Norvig About This Special eBook: A highly accessible, up-to-date professional reference for programmers, software engineers, system administrators, or technical managers, this book integrates state-of-the-art AI techniques into intelligent agent designs using examples and exercises to lead the reader from simple reactive agents to full knowledge-based agents with natural language capabilities. 👉 Machine Learning from Scratch
About This Special eBook: 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. Those entering the field of machine learning should feel comfortable with this toolbox so they have the right tool for a variety of tasks. Each chapter in this book corresponds to a single machine learning method or group of methods. In other words, each chapter focuses on a single tool within the ML toolbox.
👉 A Comprehensive Guide to Machine Learning
Author: Soroush Nasiriany
About This Special eBook: Table of Content Includes.. - Regression - Dimensionality Reduction - Beyond Least Squares: Optimization and Neural Networks - Classification - Clustering - Decision Tree Learning - Deep Learning
👉 Automated Machine Learning: Methods, Systems, Challenges
Author: Frank Hutter, Joaquin Vanschoren, and Lars Kotthoff
About This Special eBook: 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. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge.
👉 Algorithms for Reinforcement Learning by Csaba
Author: Csaba Szepesvari, Ronald Brachman & Thomas Dietterich
About This Special eBook: 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.
👉 The Handbook of Artificial Intelligence; Computers and Thought
Author: Edward Feigenbaum About This Special eBook: The scope of this handbook is broad: over 200 short articles covering all of the important ideas, techniques, and systems developed during 25 years of research in the AI field. The articles are written for people with no background in AI. Some articles serve as overviews, discussing the various approaches within a subfield, the issues, and the problems. The handbook is a reference work, a textbook, a guide to programming techniques and to the extensive literature of the field, and a book for intellectual browsing.
👉 AI based Robot Safe Learning and Control
Author: Xuefeng Zhou, Hongmin Wu, Zhihao Xu, Xiaojing Lv, Taobo Cheng, Shuai Li
About This Special eBook: 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. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.
👉 Learning Deep Architectures for AI
Author: Yoshua Bengio About This Special eBook: 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. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers, graphical models with many levels of latent variables, or in complicated propositional formulae re-using many sub-formulae.
👉 Applied Artificial Neural Networks
Author: Christian Dawson
About This Special eBook: 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 This Special eBook: 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. Advanced architectures for biomedical applications, which offer improved performance and desirable properties, follow. 
About This Special eBook: 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. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills. 👉 Artificial Intelligence: Foundations of Computational Agents
Author: David L. Poole & Alan K. Mackworth
About This Special eBook: 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. By showing how basic approaches fit into a multidimensional design space, readers can learn the fundamentals without losing sight of the bigger picture
👉 Ethical Artificial Intelligence
Author: Bill Hibbard
About This Special eBook: 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. It shows how to avoid agent self-delusion using model-based utility functions and how to avoid agents that corrupt their reward generators (sometimes called "perverse instantiation") using utility functions that evaluate outcomes at one point in time from the perspective of humans at a different point in time. It argues that agents can avoid unintended instrumental actions (sometimes called "basic AI drives" or "instrumental goals") by accurately learning human values.
👉 The Essential AI Handbook for Leaders by Peltarion
Author: Luka Crnkovic-Friis, Sebastien Plassard, Kye Andersson & Marcus Wallenberg About This Special eBook: 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. Our ambition is to contribute to this by trying to make the topic more understandable. 👉 Artificial Intelligence through Prolog 
About This Special eBook: Table of Content includes.. - Introduction - Representing facts - Variables and queries - Definitions and inferences - Arithmetic and lists in Prolog - Control structures for rule-based systems - Implementation of rule-based systems - Representing uncertainty in rule-based systems - And more..
Author: Jason Brownlee
About This Special eBook: 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. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described in this book were originally inspired by biological and natural systems, such as the adaptive capabilities of genetic evolution and the acquired immune system, and the foraging behaviors of birds, bees, ants and bacteria. An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in the Ruby Programming Language.
Author: Lieven Vandenberghe, Stephen Boyd, and Stephen P. Boyd
About This Special eBook: "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 This Special eBook: 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 cover picture shows a wind-up toy that is smart enough to not fall off a table just using intelligent mechanism design and illustrate the importance of the mechanism in designing intelligent, autonomous systems. 👉 The World and Mind of Computation and Complexity
Author: Gregg Schaffter About This Special eBook: 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 This Special eBook: 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, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of "boosting," how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.
Author: Mariette Awad and Rahul Khanna
About This Special eBook: 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. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques.
Author: Charu C. Aggarwal About This Special eBook: 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. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? 👉 Projection-Based Clustering through Self-Organization and Swarm Intelligence
Author: Michael Christoph Thrun
About This Special eBook: 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). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.
👉 Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks
Author: Erik R. Ranschaert, Sergey Morozov & Paul R. Algra About This Special eBook: This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI.
Author: Eric Nielsen, Shanqing Cai, and Stan Bileschi
About This Special eBook: In Deep Learning with JavaScript, you'll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you'll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation.
Author: Kevin Ferguson and Max Pumperla
About This Special eBook: Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game.
👉 Advanced Applications for Artificial Neural Networks
Author: Adel El-Shahat
About This Special eBook: In this book, highly qualified multidisciplinary scientists grasp their recent researches motivated by the importance of artificial neural networks. It addresses advanced applications and innovative case studies for the next-generation optical networks based on modulation recognition using artificial neural networks, hardware ANN for gait generation of multi-legged robots, production of high-resolution soil property ANN maps, ANN and dynamic factor models to combine forecasts, and more.
👉 Neural Network Design
Author: Martin T. Hagan
About This Special eBook: 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 This Special eBook: 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. The resistive switching property is an important aspect of the memristors, and there are several designs of this discussed in this book, such as in metal oxide/organic semiconductor nonvolatile memories, nanoscale switching and degradation of resistive random access memory and graphene oxide-based memristor.
👉 Keras Succinctly
Author: James McCaffrey
About This Special eBook: 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.
👉 C++ Neural Networks and Fuzzy Logic
Author: Valluru Rao About This Special eBook: 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. Examples show how to implement neural networks and fuzzy logic for further applications and experimentation.
👉 Models of Learning and Optimization for Data Scientists - A Python hands-on approach
Author: Sergio Rojas
About This Special eBook: 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. The book presents many introductory examples as well as an assortment of challenges with varying levels of difficulty, for readers to solve them using the Python programming language, the current tool–of–choice adopted by the Data Science community. These challenges (nearly 90 programming exercises) will help readers to acquire skills that hopefully will foster their academic or industry interests involving data analysis for knowledge discovery. 👉 A Brief Introduction to Neural Networks using Java and SNIPE About This Special eBook: 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 
About This Special eBook:
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 This Special eBook: 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).
Author: Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola About This Special eBook: An interactive deep learning book with code, math, and discussions. Table of Content of this eBook includes - Introduction - The Preliminaries: A Crashcourse - Linear Neural Networks - Multilayer Perceptrons - Deep Learning Computation - Convolutional Neural Networks - Modern Convolutional Networks - Recurrent Neural Networks - Attention Mechanism - Optimization Algorithms - Computational Performance - Computer Vision - Natural Language Processing - Generative Adversarial Networks - And More.. 👉 Algorithmic Aspects of Machine Learning
Author: Ankur Moitra
About This Special eBook: 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. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations.
👉 Machine Learning & Big Data
Author: Kareem Alkaseer About This Special eBook: 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. Most of the time the concept behind a model or a technique is simple or intutive but it gets lost in details or jargon. Also, most of the time existing libraries would solve the problem at hand but they are treated as black boxes and more often than not they have their own abstractions and architectures that hide the underlying concepts. This book's attempt is to make the underlying concepts clear. 👉 Building Machine Learning Systems with Python Luis Pedro Coelho and Willi Richert
About This Special eBook: 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 (finding a text's most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.
Author: Jim Hefferon
About This Special eBook: 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. What sets it apart is careful motivation, many examples, and extensive exercise sets. Together these help each student master the material of this course, and also help an instructor develop that student's level of mathematical maturity. This book has been available online for many years and is widely used, both in classrooms and for self-study. It is supported by worked answers for all exercises, beamer slides for classroom use, and a lab manual of computer work
Author: Bradley Efron and Trevor Hastie
About This Special eBook: 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, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. 👉 A Probabilistic Theory of Pattern Recognition
Author: Luc Devroye
About This Special eBook: A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. 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 This Special eBook: 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. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science.
Author: Alexey Grigorev
About This Special eBook: In Machine Learning Bookcamp you'll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you'll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You'll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more.
Author: Christopher D. Manning and Hinrich Schütze
About This Special eBook: 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. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications. 👉 From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science
Author: Norm Matloff
About This Special eBook: 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; computer performance evaluation; software engineering; data management; etc.
Author: Joseph K. Blitzstein & Jessica Hwang
About This Special eBook: 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. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, a fairly detailed introduction to Bernoulli, Poisson, and Markov processes. 👉 Introduction to Probability and Statistics Using R
Author: G. Jay Kerns
About This Special eBook: 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. Students attending the class include mathematics, engineering, and computer science majors.
👉 Supervised Sequence Labelling with Recurrent Neural Networks
Author: Alex Graves
About This Special eBook: 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 realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation.
👉 Neural Networks with JavaScript Succinctly
Author: James McCaffrey
About This Special eBook: 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. Although most concepts are relatively simple, there are many of them, and they interact with each other in unobvious ways, which is a major challenge of neural networks. But you can learn all important neural network concepts by running and examining the code in Neural Networks with JavaScript Succinctly, with complete example programs for the three major types of neural network problems.
Author: George Athanasopoulos and Rob J. Hyndman
About This Special eBook: 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. by Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal
About This Special eBook: 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. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Author: Masato Hagiwara About This Special eBook: Real-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq—without getting bogged down in complex language theory and the mathematics of deep learning. 👉 Support Vector Machines Succinctly
Author: Alexandre Kowalczyk
About This Special eBook: Support Vector Machines (SVMs) are some of the most performant off-the-shelf, supervised machine-learning algorithms. In Support Vector Machines Succinctly, author Alexandre Kowalczyk guides readers through the building blocks of SVMs, from basic concepts to crucial problem-solving algorithms. He also includes numerous code examples and a lengthy bibliography for further study. By the end of the book, SVMs should be an important tool in the reader's machine-learning toolbox.
👉 TensorFlow Roadmap
Author: Amirsina Torfi
About This Special eBook: 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. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility is designing highly modular model which also can be a disadvantage too for beginners since lots of the pieces must be considered together for creating the model. This issue has been facilitated as well by developing high-level APIs such as Keras and Slim which gather lots of the design puzzle pieces. The interesting point about TensorFlow is that its trace can be found anywhere these days.
Author: Alessandro Nego
About This Special eBook:
Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You'll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you'll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls.
Kenji Lefevre, Clément Stenac, Mark Treveil, and more About This Special eBook: This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. Do you like this huge list of free machine learning books and free deep learning books? If yes, then please share this bunch of best machine learning books with others. Download artificial intelligence: a modern approach 3rd edition pdf, practical machine learning pdf, artificial intelligence with python pdf, grokking deep learning pdf, introduction to data science pdf, practical deep learning pdf, introduction to machine learning pdf and other open source ebooks and start your journey. 
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