About the Book
A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML).
This book provides an excellent, practical compendium of the foundational topics in data science and machine learning, from a true expert. This book shows how Data Science and Machine Learning fit together in a workflow --- and learning that workflow is an essential foundation for building ML systems. I highly recommend this book for anyone who wants to master the fundamentals of building and analyzing ML models. Dr Anoop Sinha (Research Director, Google) Applied Machine Learning for Data Science Practitioners offers a practical, 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. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results.
This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed.
Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including:
Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML.
Data Preparation covers the process of framing ML problems and preparing data and features for modeling.
ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection.
Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model.
ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics.
Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift.
About the Author :
Vidya Subramanian is a passionate Data Science and Analytics leader, with experience leading teams at Google, Apple, and Intuit. Forbes recognized her as one of the "8 Female Analytics Experts From The Fortune 500." She authored Adobe Analytics with SiteCatalyst (Adobe Press) and McGraw-Hill's PMP Certification Mathematics (McGraw Hill). Vidya holds Master's degrees from Virginia Tech and Somaiya Institute of Management (India) and currently leads Data Science and Analytics for Google Play.
Review :
This book provides an excellent, practical compendium of the foundational topics in data science and machine learning, from a true expert. This book shows how Data Science and Machine Learning fit together in a workflow --- and learning that workflow is an essential foundation for building ML systems. I highly recommend this book for anyone who wants to master the fundamentals of building and analyzing ML models. Dr Anoop Sinha (Research Director, Google)
An extraordinarily well-structured guide for anyone on a journey to learn Data Science. While there are many books in this space, this book stands out for its clear and comprehensive path through the entire problem-solving process, as well as the author's enthusiastic, encouraging tone that showcases her extensive industry experience. The content is particularly strong in problem framing, data preparation and feature selection, and interpretation of results, and it includes a breadth of solution strategies not often seen in similar books, making it an ideal companion for those just starting out or those looking to solidify their foundational knowledge. This is a valuable resource that will significantly benefit students and practitioners at all levels. Dr Barbara Hoopes (Associate Dean of the Graduate School, Virginia Tech)
In the breakneck pace of modern tech, a solid foundation isn't just helpful--it's your most critical asset. This book builds that foundation, masterfully balancing the core theory of machine learning with the practical code needed to bring it to life. It's an essential guide for anyone on the data science journey, from framing the right questions to deploying a solution with care. Lauren Taralli (Director, Gemini Data Science, Google DeepMind)
In a field evolving as rapidly as data science and machine learning, the risk of obsolescence looms large. Yet this book stands out by striking the right balance between enduring fundamentals and real-world applications. Applied Machine Learning for Data Science offers a lucid, well-structured exposition of core concepts, reinforced by practical examples that bring theory to life. A valuable resource both for students and practitioners seeking to master this dynamic domain. Dr Raman Ramachandran (Dean, Somaiya Institute of Management Studies, Mumbai, India)
Data Science is an ever expanding discipline that can help us know the unknown and data science students would benefit from a guide to follow on their process of discovery. I can highly recommend this book as a well laid out guide for anyone wanting clarity on the end to end process of creating, scaling, and deploying Machine Learning models. Few resources combine all the important aspects of data science into one compendium like this book does. I can unequivocally endorse this book for anyone looking for a holistic guide into the world of data science. This is a valuable resource that will significantly benefit anyone looking to have a successful career in Data Science. Dr Daniel Eilen (Director - MS Data Analytics and Artificial Intelligence Program, University of Central Florida)
For anyone serious about building an industry career in data science, this book is your blueprint. It goes beyond the academic and into the practical, providing a structured framework for understanding how Machine Learning based models can be built and deployed in practice. From foundational ideas to advanced application, production and ethical considerations, this comprehensive guide doesn't just teach you what to do--it teaches you how to think like a data scientist, making it a valuable asset for aspiring and current practitioners alike. Harikesh Nair (Sr. Director, Google Ads Data Science, Google)
This book is a wonderful practical and everyday guide on how to take the theory behind Machine Learning and Data Science and fit it into a workflow with practical applications to solve real industry problems. The book covers the entire gamut from theory to workflow to deployment and ethics. Love the tone of the author throughout the book! It is an extremely valuable reference for folks at all levels across the spectrum of ML and Data Science. Revathi Subramanian( Global MD, Center for Advanced AI, Accenture Inc, author of "Bank Fraud: Using Technology to Combat Losses")
In today's product-driven world, understanding data science isn't just a nice-to-have for product managers, it's table stakes. This book bridges the gap between the theoretical aspects of data science and its practical application. For product managers, it offers invaluable clarity on the entire ML workflow, from problem framing and data preparation to deployment and ethical considerations. This comprehensive guide will not only empower PMs to speak the language of data science but also significantly enhance their collaboration with data science teams, leading to more effective and impactful product development. A must-read for any product leader looking to truly master their craft. Amit Fulay (Vice President of Product, Uber & Board Member, Nike Strength)
A comprehensive guide for the modern data scientist. It balances core theory with practical code, covering the entire journey from problem framing to deployment and ethics. It's an essential resource for any student learning the fundamentals of Data Science and anyone building ML applications. Dr Julian McAuley (University of California, San Diego & Author, Personalized Machine Learning)
