Grokking Machine Learning, Second Edition

Inbunden, Engelska, 2026

771 kr

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Beskrivning

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

Produktinformation

Utgivningsdatum:2026-11-30
Vikt:626 g
Format:Inbunden
Språk:Engelska
Antal sidor:525
Upplaga:2
Förlag:Manning Publications
ISBN:9781633434547

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Mer om författaren

Luis G. Serrano is a research scientist in quantum artificial intelligence, known for making complex topics accessible. With experience as a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple, Luis brings practical industry knowledge to every page. He translates complex machine learning concepts into clear guidance that helps developers build their skills.

Innehållsförteckning

1 WHAT IS MACHINE LEARNING? IT IS COMMON SENSE, EXCEPT DONE BY A COMPUTER 2 TYPES OF MACHINE LEARNING 3 DRAWING A LINE CLOSE TO OUR POINTS: LINEAR REGRESSION 4 OPTIMIZING THE TRAINING PROCESS: UNDERFITTING, OVERFITTING, TESTING, AND REGULARIZATION 5 USING LINES TO SPLIT OUR POINTS: THE PERCEPTRON ALGORITHM 6 A CONTINUOUS APPROACH TO SPLITTING POINTS: LOGISTIC CLASSIFIERS 7 HOW DO YOU MEASURE CLASSIFICATION MODELS? ACCURACY AND ITS FRIENDS 8 USING PROBABILITY TO ITS MAXIMUM: THE NAIVE BAYES MODEL 9 SPLITTING DATA BY ASKING QUESTIONS: DECISION TREES 10 COMBINING BUILDING BLOCKS TO GAIN MORE POWER: NEURAL NETWORKS 11 FINDING BOUNDARIES WITH STYLE: SUPPORT VECTOR MACHINES AND THE KERNEL METHOD 12 COMBINING MODELS TO MAXIMIZE RESULTS: ENSEMBLE LEARNING 13 PUTTING IT ALL IN PRACTICE: A REAL-LIFE EXAMPLE OF DATA ENGINEERING AND MACHINE LEARNING 14 AI FOR LANGUAGE AND IMAGE GENERATION APPENDICES APPENDIX A: SOLUTIONS TO THE EXERCISES APPENDIX B: THE MATH BEHIND GRADIENT DESCENT APPENDIX C: REFERENCES