Mohlo by sa vám tiež páčiť

Informovať o naskladnení knihy

Viac informácií o knihe Grokking Machine Learning, Second Edition

• Parametre knihy
• Anotácia kniha
• Obľúbené z iného súdka

Nákupom získate 134 bodov

Anotácia knihy

Machine learning can feel confusing at first, but it can be easier to understand with the right approach. This book introduces the main ideas in a simple way that you can follow using only high school mathematics. It explains concepts with clear examples and practical exercises that help you apply what you learn. It also introduces recent developments like large language models and generative AI, so you can see how these ideas are used today. 

• Learn with clear code examples and fun illustrations. 

• Understand supervised and unsupervised machine learning. 

• Explore neural networks, regression and probabilistic models. 

• Discover methods for evaluating and improving your models. 

• Learn about data pre-processing, feature engineering, transformers and generative AI. 

Grokking Machine Learning is designed to help you learn machine learning in a clear and simple way. It explains ideas step by step so you can follow along easily. Each chapter introduces an important concept, from neural networks to data preparation and feature selection. You also get examples and exercises that help you practise and apply what you learn. 

After reading this book, you will be able to understand and build key machine learning algorithms. You will gain the skills to work with modern developments in the field. This book is for readers who know basic Python. No prior knowledge of machine learning is needed

Parametre knihy

Zaradenie knihy Knihy po nemecky Naturwissenschaften, Medizin, Informatik, Technik Informatik, EDV Informatik

• Celý názov: Grokking Machine Learning, Second Edition
• Autor: Luis G. Serrano
• Jazyk: Angličtina
• Väzba: Brožovaná
• Počet strán: 525
• EAN: 9781633434547
• ISBN: 1633434540
• ID: 50899871
• Nakladateľ: Manning Publications
• Hmotnosť: 626 g
• Dátum vydania: 30. June 2026

Obľúbené z iného súdka

---

---

---