Best Natural Language Processing Books of 2025

The ChatGPT Millionaire is a comprehensive guide on how to make money online using artificial intelligence. The book provides step-by-step instructions on how to create passive income sources, impress customers with high-quality work, and research and promote engaging content. It also includes access to 150+ powerful "Act as" prompts that'll ensure you can use ChatGPT quickly and easily for whatever you need. The book is an excellent resource for individuals who want to leverage AI to make money online. Overall, this is an excellent book that provides valuable insights into the world of AI and its potential to help people make money online easily and quickly.

Nobody expected ChatGPT, an AI capable of writing convincingly like a human, to be so successful. But how does it work? In this short book, Stephen Wolfram, a prominent scientist, and computation pioneer explain how ChatGPT brings together the latest neural net technology with foundational questions about language and human thought posed by Aristotle more than two thousand years ago. The book is an engaging explanation of ChatGPT's inner workings that draws on the author's decades-long experience at the frontiers of science and technology. Overall, this book provides an excellent overview of ChatGPT's capabilities and is a must-read for anyone interested in natural language processing and AI technology.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a must-read for anyone interested in machine learning. This book provides a comprehensive overview of the concepts and techniques required to build intelligent systems. The author, Aurelien Geron, uses Python frameworks such as scikit-learn, Keras, and TensorFlow to explain everything from simple linear regression to deep neural networks. The book includes numerous code examples and exercises that help readers apply what they've learned. The updated third edition covers new topics such as generative adversarial networks and deep reinforcement learning. Overall, this book is an excellent resource for anyone looking to learn machine learning from scratch or deepen their knowledge in the field.

The "Machine Learning System Design Interview" book is an essential resource for anyone interested in ML system design, whether they are beginners or experienced engineers. It provides a step-by-step framework for tackling a broad range of ML system design questions, including real-world examples to illustrate the systematic approach. With 10 real ML system design interview questions and detailed solutions, this book is specifically written to help readers prepare for an ML interview. It also includes an insider's take on what interviewers are looking for and why, making it a valuable tool for anyone entering the field.

Natural Language Processing with Transformers, Revised Edition is a practical guide for data scientists and coders interested in training and scaling large models using Hugging Face Transformers. The authors, including the creators of the library, provide a hands-on approach to integrating transformers in your applications and solving a variety of NLP tasks. The book covers topics such as cross-lingual transfer learning, distillation, pruning, and quantization. The revised edition is now in full color and offers updated content, making it a valuable resource for both beginners and experienced practitioners.

This comprehensive guide, "Essential Math for AI", is a must-read for anyone interested in the AI field. It provides a comprehensive understanding of the underlying mathematics necessary to build successful AI systems, focusing on real-world applications and state-of-the-art models. The book is written in an immersive and conversational style, making it easy for anyone to learn and understand. It covers topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more. The author has done an exceptional job of making math fun and engaging, making this book stand out from other math textbooks.

The book "Machine Learning with PyTorch and Scikit-Learn" is a comprehensive guide to machine and deep learning using PyTorch's simple-to-code framework. It covers all the essential machine learning techniques in depth, including generative adversarial networks (GANs) and reinforcement learning. The book is a perfect companion for Python developers new to machine learning or those who want to deepen their knowledge of the latest developments. The clear explanations, visualizations, and examples make it easy for readers to understand the essential parts of PyTorch and create models using popular libraries. Highly recommended for anyone interested in machine learning and deep learning.

The ChatGPT Goldrush: Profiting from the AI Revolution Online - Mastering Prompt Engineering is a comprehensive guide that explores the diverse applications of ChatGPT and how to leverage its powerful capabilities to optimize business performance. The book covers prompt engineering, content creation, research assistance, language learning, and much more. It also provides access to an extensive list of AI-powered apps and an invaluable library of advanced prompts. This book is perfect for entrepreneurs, freelancers, and anyone curious about AI's potential. Overall, this book is an excellent resource for those looking to unlock the potential of AI and take advantage of the ChatGPT phenomenon.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a practical guide to building intelligent systems using machine learning. The author, Aurélien Géron, provides a comprehensive overview of the field, from simple linear regression to deep neural networks, using Python frameworks Scikit-Learn and TensorFlow. With exercises in each chapter, readers can apply what they've learned and gain an intuitive understanding of the concepts and tools. Overall, this book is an excellent resource for programmers with some experience who want to delve into machine learning and build intelligent systems.

The second edition of "Transformers for Natural Language Processing" is a comprehensive guide to building, training, and fine-tuning deep neural network architectures for NLP. With Python, PyTorch, TensorFlow, BERT, and GPT-3, the book covers a range of models and platforms while teaching problem-solving skills to tackle model weaknesses. The author also investigates complex language problems, including machine translations, speech-to-text, text-to-speech, and question-answering. The book is an excellent resource for anyone interested in NLP and AI, offering step-by-step guides and techniques to solve hard language problems. Overall, it's a must-read for those looking to stay current with cutting-edge technology.