Master machine learning through clarity, not complexity―in a book engineered to teach with exceptional conciseness.
Translated into 11 languages and used in thousands of universities worldwide, this book takes a unique approach: it assumes that your time is valuable. Instead of drowning you in theory or skimming the surface, it delivers a complete education in modern machine learning, focusing on what matters in practice. From fundamental algorithms that form the backbone of many applications, to cutting-edge deep learning and neural networks, you'll understand how these tools work and how to use them.
What sets this book apart is its careful progression through key concepts. You'll start with essential mathematical concepts and gradually progress through the most practically important machine learning algorithms. You'll learn practical skills like feature engineering, regularization, handling imbalanced datasets, ensembles, and model evaluation that help turn theory into working systems.
The book covers not just supervised learning, but also clustering, topic modeling, metric learning, learning to rank, and recommendation systems, giving you a complete toolkit for solving modern machine learning challenges.
This isn't just another theoretical textbook. Every chapter reflects the author's real-world experience, focusing on techniques that work in practice. Whether you're building a recommendation system, analyzing customer data, or working with images and text, you'll find practical guidance here.
This isn't a high-level overview either. The book explores each concept with precisely the right level of technical detail-enough to create those crucial "a-ha!" moments of understanding, but not so much that you get overwhelmed by mathematical notation or theoretical abstractions. It hits that sweet spot where complex ideas click into place naturally, making it valuable for both newcomers looking to build a strong foundation and experienced practitioners seeking to expand their toolkit.
What's Inside
About the Reader
The book assumes a basic foundation in college-level mathematics. However, it's entirely self-contained, introducing all necessary mathematical concepts through intuitive explanations. This approach ensures that readers with basic mathematical knowledge can follow along without getting lost in complex equations.
Endorsed by Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world, Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, and other industry leaders.
Read endorsements on themlbook.com
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Andriy Burkov is the author of "The Hundred-Page Machine Learning Book" and "Machine Learning Engineering," both of which became #1 Best Sellers on Amazon. He holds a Ph.D. in Artificial Intelligence and is a recognized expert in machine learning and natural language processing.As a machine learning expert and leader, Andriy has successfully led dozens of production-grade AI projects in different business domains at Fujitsu and Gartner. His previous books have been translated into more than a dozen languages and are used as textbooks in many universities worldwide. His work has impacted millions of machine learning practitioners and researchers worldwide.Currently, Andriy is the Head of Machine Learning at TalentNeuron, where he develops AI solutions for talent marketplace analytics. He uses language models and other machine learning tools to analyze billions of job postings across 30+ languages in near real time.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
EUR 10,21 per la spedizione da U.S.A. a Italia
Destinazione, tempi e costiEUR 7,67 per la spedizione da U.S.A. a Italia
Destinazione, tempi e costiDa: -OnTimeBooks-, Phoenix, AZ, U.S.A.
Condizione: very_good. Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if youâre not satisfied with purchase please return item for full refund. Ships USPS Media Mail. Codice articolo OTV.1999579518.VG
Quantità: 2 disponibili
Da: Better World Books, Mishawaka, IN, U.S.A.
Condizione: As New. Used book that is in almost brand-new condition. Codice articolo 40840381-6
Quantità: 3 disponibili
Da: California Books, Miami, FL, U.S.A.
Condizione: New. Codice articolo I-9781999579517
Quantità: Più di 20 disponibili
Da: BargainBookStores, Grand Rapids, MI, U.S.A.
Hardback or Cased Book. Condizione: New. The Hundred-Page Machine Learning Book 1.3. Book. Codice articolo BBS-9781999579517
Quantità: 5 disponibili
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New. Codice articolo 36644947-n
Quantità: Più di 20 disponibili
Da: Rarewaves USA, OSWEGO, IL, U.S.A.
Hardback. Condizione: New. Hard Cover ed. Codice articolo LU-9781999579517
Quantità: Più di 20 disponibili
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition. Codice articolo 36644947
Quantità: Più di 20 disponibili
Da: Speedyhen, London, Regno Unito
Condizione: NEW. Codice articolo NW9781999579517
Quantità: 2 disponibili
Da: PBShop.store US, Wood Dale, IL, U.S.A.
HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L1-9781999579517
Quantità: Più di 20 disponibili
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L1-9781999579517
Quantità: Più di 20 disponibili