{"title":"Machine Learning \u0026 AI Books","description":"\u003ch2\u003eDeep Learning, Neural Networks \u0026amp; Data Science References\u003c\/h2\u003e\n\u003cp\u003eBrowse our selection of machine learning and artificial intelligence books written by recognized authors and researchers in the field. This collection includes titles on deep learning, neural networks, data science, and applied AI - covering both foundational theory and hands-on implementation.\u003c\/p\u003e\n\u003cp\u003eAll orders ship free within the US via standard shipping.\u003c\/p\u003e","products":[{"product_id":"ace-the-data-science-interview-201-real-question-answers","title":"Ace the Data Science Interview: 201 Real Interview Questions (9780578973838)","description":"\u003ch2\u003eAce the Data Science Interview by Nick Singh \u0026amp; Kevin Huo\u003c\/h2\u003e\n\u003cp\u003e\u003cem\u003eAce the Data Science Interview\u003c\/em\u003e (ISBN-13: 9780578973838) by Nick Singh and Kevin Huo is a focused, interview-oriented reference for data science candidates preparing for technical hiring processes at technology companies, financial firms, and data-driven startups. The book compiles 201 real interview questions drawn from FAANG companies, tech startups, and Wall Street quantitative firms, with worked solutions that walk through reasoning rather than just final answers.\u003c\/p\u003e\n\u003ch2\u003eAbout the Book\u003c\/h2\u003e\n\u003cp\u003eWritten by former data scientists at major tech and finance companies, this paperback organizes its content around the six areas that appear consistently in data science technical interviews: statistics and probability, SQL, Python and coding, machine learning, product sense, and case and business questions. Each section presents actual questions from real interviews along with explanations of how to approach them, giving candidates a structured way to prepare across the full scope of what interviewers test.\u003c\/p\u003e\n\u003ch2\u003eKey Topics Covered\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eStatistics \u0026amp; Probability\u003c\/strong\u003e - Distributions, hypothesis testing, A\/B testing, and applied statistical reasoning\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSQL\u003c\/strong\u003e - Query writing, joins, aggregations, window functions, and schema design questions\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePython \u0026amp; Coding\u003c\/strong\u003e - Data manipulation, algorithmic questions, and technical screen challenges\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMachine Learning\u003c\/strong\u003e - Model selection, evaluation metrics, bias-variance tradeoff, and ML problem framing\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eProduct Sense\u003c\/strong\u003e - Metrics definition, experiment design, and product impact analysis\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCase \u0026amp; Business Questions\u003c\/strong\u003e - Translating business problems into data science problems and communicating solutions\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eInterview Strategy\u003c\/strong\u003e - How to structure answers, what interviewers look for, and how to approach each question type\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eWho Should Use This Book\u003c\/h2\u003e\n\u003cp\u003eThis book is suited for candidates who have foundational data science knowledge and are actively preparing for job interviews. It is used by candidates targeting FAANG and comparable companies, professionals transitioning into data science from analytics or software engineering roles, and data science graduate students preparing for their first industry job search. It is a preparation guide, not an introductory textbook.\u003c\/p\u003e\n\u003cp\u003eFree standard shipping to US addresses included.\u003c\/p\u003e\n\u003cp\u003eFor support, contact \u003ca href=\"mailto:support@mindabooks.com\"\u003esupport@mindabooks.com\u003c\/a\u003e.\u003c\/p\u003e","brand":"Mindabooks","offers":[{"title":"Default Title","offer_id":43199993839691,"sku":"0578973839","price":45.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0687\/9769\/4027\/files\/Acethedatescienceinterview.jpg?v=1789982320"},{"product_id":"hands-on-machine-learning-scikit-learn-tensorflow-3rd-edition","title":"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd Edition (9781098125974)","description":"\u003cp\u003e\u003cem\u003eHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition\u003c\/em\u003e (ISBN-13: 9781098125974) by Aurélien Géron is a code-first machine learning reference published by O'Reilly Media in November 2022. It covers both classical machine learning and deep learning in a single 861-page volume, using working Python examples throughout.\u003c\/p\u003e\n\n\u003ch2\u003eAbout This Book\u003c\/h2\u003e\n\u003cp\u003eWritten by Aurélien Géron, a former Google engineer and ML educator, this book is structured around the principle that the most effective way to learn machine learning is to implement it. Each concept is introduced alongside a working code example so readers can observe behavior directly rather than relying on abstract description alone.\u003c\/p\u003e\n\u003cp\u003eThe 3rd Edition has been updated for TensorFlow 2 and the modern Keras API. It adds new chapters on transformer architectures and diffusion models, reflecting developments in the field since earlier editions. It is published by O'Reilly Media, a technical publisher specializing in software and AI topics.\u003c\/p\u003e\n\n\u003ch2\u003eKey Topics Covered\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eEnd-to-end ML project workflow: data collection, preprocessing, training, evaluation, and deployment\u003c\/li\u003e\n\u003cli\u003eClassical ML with Scikit-Learn: linear regression, decision trees, random forests, SVMs, and ensemble methods\u003c\/li\u003e\n\u003cli\u003eFeature engineering and data preparation for real-world datasets\u003c\/li\u003e\n\u003cli\u003eNeural network fundamentals using the Keras API\u003c\/li\u003e\n\u003cli\u003eConvolutional neural networks for computer vision\u003c\/li\u003e\n\u003cli\u003eRecurrent neural networks, LSTMs, and GRUs for sequence and time-series data\u003c\/li\u003e\n\u003cli\u003eTransformer architectures and attention mechanisms\u003c\/li\u003e\n\u003cli\u003eGenerative models: autoencoders, GANs, and diffusion models\u003c\/li\u003e\n\u003cli\u003eReinforcement learning: policy gradients, Q-learning, and actor-critic methods\u003c\/li\u003e\n\u003cli\u003eModel saving, loading, and serving in production environments\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eWho Should Use This Book\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSoftware engineers with Python experience who want to build machine learning skills without a prior statistics background\u003c\/li\u003e\n\u003cli\u003eData scientists looking for a single reference that spans both classical ML and deep learning\u003c\/li\u003e\n\u003cli\u003eResearch engineers who need to get up to speed on the TensorFlow 2 and Keras ecosystem\u003c\/li\u003e\n\u003cli\u003eStudents in applied machine learning courses seeking a practice-oriented supplement to academic materials\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003cp\u003eFor support, contact \u003ca href=\"mailto:support@mindabooks.com\"\u003esupport@mindabooks.com\u003c\/a\u003e.\u003c\/p\u003e","brand":"Mindabooks","offers":[{"title":"Default Title","offer_id":43199994069067,"sku":"1098125975","price":46.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0687\/9769\/4027\/files\/handsonmachinelearning.jpg?v=1789982322"},{"product_id":"deep-learning-with-python-2nd-edition","title":"Deep Learning with Python, 2nd Edition (9781617296864)","description":"\u003ch2\u003eDeep Learning with Python, 2nd Edition by François Chollet\u003c\/h2\u003e\n\n\u003cp\u003e\u003cstrong\u003eDeep Learning with Python, 2nd Edition\u003c\/strong\u003e (ISBN-13: 9781617296864) is written by François Chollet, the creator of Keras. Published by Manning Publications in December 2021, this 504-page book teaches deep learning through a practical, code-first approach using Python, Keras, and TensorFlow 2.\u003c\/p\u003e\n\n\u003ch2\u003eAbout This Book\u003c\/h2\u003e\n\n\u003cp\u003eThis edition is updated to cover TensorFlow 2 and the integrated Keras API, along with newer architectures including transformers and diffusion models. Chollet's approach puts working models in the reader's hands early and builds understanding through code examples before layering in theory - a method that many practitioners find more accessible than traditional textbook-style instruction.\u003c\/p\u003e\n\n\u003ch2\u003eKey Topics Covered\u003c\/h2\u003e\n\n\u003cul\u003e\n  \u003cli\u003eNeural network fundamentals: tensors, operations, and backpropagation\u003c\/li\u003e\n  \u003cli\u003eBuilding and training models with Keras and TensorFlow 2\u003c\/li\u003e\n  \u003cli\u003eConvolutional neural networks for image classification and computer vision\u003c\/li\u003e\n  \u003cli\u003eSequence models: RNNs, LSTMs, and text processing for NLP\u003c\/li\u003e\n  \u003cli\u003eTransformer architectures and self-attention mechanisms\u003c\/li\u003e\n  \u003cli\u003eGenerative deep learning: variational autoencoders, GANs, and diffusion models\u003c\/li\u003e\n  \u003cli\u003eAdvanced Keras patterns: custom layers, training loops, and multi-input\/output models\u003c\/li\u003e\n  \u003cli\u003eBest practices: regularization, hyperparameter tuning, callbacks, and debugging\u003c\/li\u003e\n  \u003cli\u003eModel deployment: saving, exporting, and serving trained models\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eWho Should Use This Book\u003c\/h2\u003e\n\n\u003cp\u003eThis book is suited for Python developers who want to start building neural networks, software engineers adding AI features to their projects, data scientists looking to deepen their understanding of model internals, and computer science students seeking applied deep learning experience. Basic Python knowledge is required; no prior deep learning experience is needed.\u003c\/p\u003e\n\n\u003cp\u003eFree standard shipping on orders shipped to US addresses. For questions, contact \u003ca href=\"mailto:support@mindabooks.com\"\u003esupport@mindabooks.com\u003c\/a\u003e.\u003c\/p\u003e","brand":"Mindabooks","offers":[{"title":"Default Title","offer_id":43199995969611,"sku":"9781617296864","price":48.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0687\/9769\/4027\/files\/deep-learning-with-python-2nd-edition-cover_f0ffb99f-25b9-45cd-af30-ce7f6a8c14dc.jpg?v=1789982329"},{"product_id":"deep-learning-ian-goodfellow","title":"Deep Learning (Adaptive Computation and Machine Learning series) (9780262035613)","description":"\u003ch2\u003eDeep Learning by Ian Goodfellow - MIT Press\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDeep Learning\u003c\/strong\u003e (Adaptive Computation and Machine Learning series) by \u003cstrong\u003eIan Goodfellow\u003c\/strong\u003e, \u003cstrong\u003eYoshua Bengio\u003c\/strong\u003e, and \u003cstrong\u003eAaron Courville\u003c\/strong\u003e is a comprehensive academic textbook covering the mathematical foundations and practical techniques of modern deep learning. Published by \u003cstrong\u003eMIT Press\u003c\/strong\u003e (ISBN-13: 9780262035613), it is part of the Adaptive Computation and Machine Learning series and is used in graduate programs in machine learning, computer science, and artificial intelligence.\u003c\/p\u003e\n\u003ch2\u003eAbout This Book\u003c\/h2\u003e\n\u003cp\u003eUnlike practitioner guides focused on specific frameworks, this textbook builds understanding from the ground up - starting with the linear algebra, probability theory, and numerical computation that underlie deep learning systems, and progressing through core models, optimization methods, and modern architectures. At 800 pages in hardcover, it is designed for sustained, serious engagement with the subject rather than quick-start use.\u003c\/p\u003e\n\u003cp\u003eThe authors - Ian Goodfellow, Yoshua Bengio, and Aaron Courville - are researchers who have made documented contributions to the field, including the development of generative adversarial networks and foundational work on neural network training. Their explanations reflect that research background.\u003c\/p\u003e\n\u003ch2\u003eKey Topics Covered\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eMathematical prerequisites: linear algebra, probability, information theory, and numerical methods\u003c\/li\u003e\n\u003cli\u003eMachine learning fundamentals: capacity, overfitting, underfitting, and generalization\u003c\/li\u003e\n\u003cli\u003eDeep feedforward networks: structure and training\u003c\/li\u003e\n\u003cli\u003eRegularization techniques: dropout, batch normalization, and data augmentation\u003c\/li\u003e\n\u003cli\u003eOptimization methods: SGD, momentum, adaptive learning rates, and second-order methods\u003c\/li\u003e\n\u003cli\u003eConvolutional neural networks: architecture, pooling, and computer vision applications\u003c\/li\u003e\n\u003cli\u003eRecurrent neural networks and LSTMs: sequence modeling and temporal dependencies\u003c\/li\u003e\n\u003cli\u003eDeep generative models: autoencoders and generative adversarial networks\u003c\/li\u003e\n\u003cli\u003eResearch topics: representation learning, structured probabilistic models, and Monte Carlo methods\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eWho Should Read This Book\u003c\/h2\u003e\n\u003cp\u003eThis textbook is suited for graduate students in machine learning, computer science, and AI programs where neural networks are covered at a theoretical level. Researchers building foundational knowledge to engage with academic literature will find the mathematical depth useful. Software engineers and data scientists who want to understand how deep learning models function - beyond framework documentation - will also find it relevant. The early chapters include a mathematical review, though readers with a background in linear algebra, probability, and calculus will get the most from the material. It is not intended as an introductory or beginner-level text.\u003c\/p\u003e\n\u003cp\u003eFor questions, contact \u003ca href=\"mailto:support@mindabooks.com\"\u003esupport@mindabooks.com\u003c\/a\u003e.\u003c\/p\u003e","brand":"Mindabooks","offers":[{"title":"Default Title","offer_id":43199996559435,"sku":"9780262035613","price":54.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0687\/9769\/4027\/files\/DeepLearning_dd51a702-4c06-4394-8882-541242e1df6b.jpg?v=1789982331"}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0687\/9769\/4027\/collections\/Machine_Learning_AI_Books.webp?v=1789558527","url":"https:\/\/www.mindabooks.com\/collections\/machine-learning-ai-books.oembed","provider":"Mindabooks","version":"1.0","type":"link"}