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The 10 Best Natural Language Processing Books list have been recommended not only by normal readers but also by experts.
You’ll also find that these are top-ranking books on the US Amazon Best Sellers book list for the Natural Language Processing category of books.
If any of the titles interest you, I’d recommend checking them out by clicking the “Check Price” button. It’ll take you to the authorized retailer website, where you’ll be able to see reviews and buy it.
Let’s take a look at the list of 10 Best Natural Language Processing Books.
10 Best Natural Language Processing Books
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques...
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The Hundred-Page Machine Learning Book
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques...
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Machine Learning Engineering
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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and...
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Introduction to Machine Learning with Python: A Guide for Data Scientists
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CRYSTAL BALL PHOTOGRAPHY FOR BEGINNERS: Master the Art of Crystal Ball Photography, Tips and Tricks...
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Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems
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An Elementary Introduction to the Wolfram Language - Second Edition
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You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the...
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Now, let’s dive right into the list of 10 Best Natural Language Processing Books, where we’ll provide a quick outline for each book.
1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron Review Summary
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks–Scikit-Learn and TensorFlow–author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. * Explore the machine learning landscape, particularly neural nets * Use Scikit-Learn to track an example machine-learning project end-to-end * Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods * Use the TensorFlow library to build and train neural nets * Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning * Learn techniques for training and scaling deep neural nets.
2. The Hundred-Page Machine Learning Book by Andriy Burkov Review Summary
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The Hundred-Page Machine Learning Book
Peter Norvig , Research Director at Google, co-author of AIMA , the most popular AI textbook in the world: “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.” Aur elien Geron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow : “The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn’t hesitate to go into the math equations: that’s one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field.” Karolis Urbonas , Head of Data Science at Amazon : “A great introduction to machine learning from a world-class practitioner.” Chao Han , VP, Head of R&D at Lucidworks : “I wish such a book existed when I was a statistics graduate student trying to learn about machine learning.” Sujeet Varakhedi , Head of Engineering at eBay : “Andriy’s book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.” Deepak Agarwal , VP of Artificial Intelligence at LinkedIn : “A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.” Vincent Pollet , Head of Research at Nuance : “The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.” Gareth James , Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R : “This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base.” Everything you really need to know in Machine Learning in a hundred pages.
3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron Review Summary
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks–Scikit-Learn and TensorFlow–author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. * Explore the machine learning landscape, particularly neural nets * Use Scikit-Learn to track an example machine-learning project end-to-end * Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods * Use the TensorFlow library to build and train neural nets * Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning * Learn techniques for training and scaling deep neural nets
4. Machine Learning Engineering by Andriy Burkov Review Summary
Machine Learning Engineering
From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book , this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI and is the leader of a machine learning team at Gartner. This book is based on Andriy’s own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders. Here’s what Cassie Kozyrkov, Chief Decision Scientist at Google tells about the book in the Foreword: “You’re looking at one of the few true Applied Machine Learning books out there. That’s right, you found one! A real applied needle in the haystack of research-oriented stuff. Excellent job, dear reader… unless what you were actually looking for is a book to help you learn the skills to design general- purpose algorithms, in which case I hope the author won’t be too upset with me for telling you to flee now and go pick up pretty much any other machine learning book. This one is different.” […] “So, what’s in […] the book? The machine learning equivalent of a bumper guide to innovating in recipes to make food at scale. Since you haven’t read the book yet, I’ll put it in culinary terms: you’ll need to figure out what’s worth cooking / what the objectives are ( decision-making and product management ), understand the suppliers and the customers ( domain expertise and business acumen ), how to process ingredients at scale ( data engineering and analysis ), how to try many different ingredient- appliance combinations quickly to generate potential recipes ( prototype phase ML engineering ), how to check that the quality of the recipe is good enough to serve ( statistics ), how to turn a potential recipe into millions of dishes served efficiently ( production phase ML engineering ), and how to ensure that your dishes stay top-notch even if the delivery truck brings you a ton of potatoes instead of the rice you ordered ( reliability engineering ). This book is one of the few to offer perspectives on each step of the end-to-end process.” […] “One of my favorite things about this book is how fully it embraces the most important thing you need to know about machine learning: mistakes are possible… and sometimes they hurt. As my colleagues in site reliability engineering love to say, “Hope is not a strategy.” Hoping that there will be no mistakes is the worst approach you can take. This book does so much better. It promptly shatters any false sense of security you were tempted to have about building an AI system that is more “intelligent” than you are. (Um, no. Just no.) Then it diligently takes you through a survey of all kinds of things that can go wrong in practice and how to prevent/detect/handle them. This book does a great job of outlining the importance of monitoring, how to approach model maintenance, what to do when things go wrong, how to think about fallback strategies for the kinds of mistakes you can’t anticipate, how to deal with adversaries who try to exploit your system, and how to manage the expectations of your human users (there’s also a section on what to do when your, er, users are machines). These are hugely important topics in practical machine learning, but they’re so often neglected in other books. Not here.” “If you intend to use machine learning to solve business problems at scale, I’m delighted you got your hands on this book. Enjoy!”
5. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition by Sebastian Raschka Review Summary
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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. #### Key Features * Third edition of the bestselling, widely acclaimed Python machine learning book * Clear and intuitive explanations take you deep into the theory and practice of Python machine learning * Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices #### Book Description Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you’ll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It’s also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you’re a Python developer new to machine learning or want to deepen your knowledge of the latest developments. #### What you will learn * Master the frameworks, models, and techniques that enable machines to ‘learn’ from data * Use scikit-learn for machine learning and TensorFlow for deep learning * Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more * Build and train neural networks, GANs, and other models * Discover best practices for evaluating and tuning models * Predict continuous target outcomes using regression analysis * Dig deeper into textual and social media data using sentiment analysis #### Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data. #### Table of Contents 1. Giving Computers the Ability to Learn from Data 2. Training Simple ML Algorithms for Classification 3. ML Classifiers Using scikit-learn 4. Building Good Training Datasets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying ML to Sentiment Analysis 9. Embedding a ML Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Implementing Multilayer Artificial Neural Networks 13. Parallelizing Neural Network Training with TensorFlow 14. TensorFlow Mechanics 15. Classifying Images with Deep Convolutional Neural Networks 16. Modeling Sequential Data Using Recurrent Neural Networks 17. GANs for Synthesizing New Data 18. RL for Decision Making in Complex Environments
6. Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller Review Summary
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Introduction to Machine Learning with Python: A Guide for Data Scientists
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: * Fundamental concepts and applications of machine learning * Advantages and shortcomings of widely used machine learning algorithms * How to represent data processed by machine learning, including which data aspects to focus on * Advanced methods for model evaluation and parameter tuning * The concept of pipelines for chaining models and encapsulating your workflow * Methods for working with text data, including text-specific processing techniques * Suggestions for improving your machine learning and data science skills.
7. CRYSTAL BALL PHOTOGRAPHY FOR BEGINNERS: Master the Art of Crystal Ball Photography, Tips and Tricks For Every Beginner by Johnny Cott Review Summary
CRYSTAL BALL PHOTOGRAPHY FOR BEGINNERS: Master the Art of Crystal Ball Photography, Tips and Tricks For Every Beginner
2020 UPDATED ILLUSTRATED GUIDE CRYSTAL BALL PHOTOGRAPHY FOR BEGINNERS: Step by Step Guide With Pictures If you find yourself in a photographic rut with the need to break new creative ground, look for different photography mediums. One way you can achieve this is by gazing into the future with Crystal ball photography. This is an amazing piece of equipment any photographer can have. It works purely like an external lens and most importantly, it is very versatile. In this guide, you’ll learn all about refraction photography and how to take great crystal ball photography. Below is a preview of what to expect: -> The meaning of Crystal Ball Photography -> How refraction in Glass Ball Photography works-> How to get a good subject -> How to place your crystal ball-> How to overcome technical issues-> How to select the best crystal ball-> Tips and tricks-> And many more! What are you waiting for? Even if you’re a newbie in the world of photography, you’ll definitely gain mastery of this Crystal ball Photography. Hit the BUY button now!
8. Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems by Sowmya Vajjala Review Summary
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Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: * Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP * Implement and evaluate different NLP applications using machine learning and deep learning methods * Fine-tune your NLP solution based on your business problem and industry vertical * Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages * Produce software solutions following best practices around release, deployment, and DevOps for NLP systems * Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
9. An Elementary Introduction to the Wolfram Language – Second Edition by Stephen Wolfram Review Summary
An Elementary Introduction to the Wolfram Language - Second Edition
The Wolfram Language represents a major advance in programming languages that makes leading-edge computation accessible to everyone. Unique in its approach of building in vast knowledge and automation, the Wolfram Language scales from a single line of easy-to-understand interactive code to million-line production systems. This book provides an elementary introduction to the Wolfram Language and modern computational thinking. It assumes no prior knowledge of programming, and is suitable for both technical and non-technical college and high-school students, as well as anyone with an interest in the latest technology and its practical application.
10. You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place by Janelle Shane Review Summary
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You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place
As heard on NPR’s “Science Friday,” discover the book recommended by Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant: an “accessible, informative, and hilarious” introduction to the weird and wonderful world of artificial intelligence (Ryan North). “You look like a thing and I love you” is one of the best pickup lines ever . . . according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog AI Weirdness. She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans–all to understand the technology that governs so much of our daily lives. We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really… and how does it solve problems, understand humans, and even drive self-driving cars? Shane delivers the answers to every AI question you’ve ever asked, and some you definitely haven’t. Like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world’s best Halloween costume really “Vampire Hog Bride”? In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt–and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking. “I can’t think of a better way to learn about artificial intelligence, and I’ve never had so much fun along the way.” –Adam Grant, New York Times bestselling author of Originals