10 Best Natural Language Processing Books

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.

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.

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

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.

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

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