Applied Machine Learning for Data Science Practitioners

Applied Machine Learning for Data Science Practitioners is a comprehensive,practical guide to building end-to-end ML solutions for real-world business challenges,empowering data science practitioners to make informed decisions and select the right techniques for any use case.
Applied Machine Learning for Data Science Practitioners is a comprehensive,practical guide to building end-to-end ML solutions for real-world business challenges,empowering data science practitioners to make informed decisions and select the right techniques for any use case. It covers essential topics such as Data Science Fundamentals, Data Preparation, ML Problem Solving, Model Optimization, ML Ethics, and Model Deployment and Monitoring, with code snippets and practical examples.

Format: Hardback
Length: 656 pages
Publication date: 27 March 2025
Publisher: John Wiley & Sons Inc

This book provides a comprehensive and practical guide to data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). It offers a holistic approach, covering foundational topics in data science and machine learning, and demonstrates how they fit together in a workflow. The book is highly recommended for anyone who wants to master the fundamentals of building and analyzing ML models. Dr Anoop Sinha, Research Director at Google, praises the book for its practicality and expert insights. Applied Machine Learning for Data Science Practitioners provides a step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach, balancing theoretical concepts with practical examples. It equips you with the knowledge to evaluate a range of techniques and algorithms, interpret results, and demonstrate applications. The book includes code snippets, reviewing output, and guidance on interpreting results.

Weight: 1089g
Dimension: 259 x 185 x 41 (mm)
ISBN-13: 9781394155378