Overview

Struggling to grasp machine learning concepts or unsure how to apply them in the real world? This book aims to change that by using the world's most popular game—soccer—to illuminate key concepts in predictive modeling and data science. Whether you're a complete beginner or you're interested in entering the burgeoning field of sports analytics, you'll develop a solid foundation in machine learning through engaging examples that bridge academic principles with practical applications. Written by experts in both machine learning and sports analytics, this practical Python-focused guide introduces fundamental data science techniques using real soccer data. Ideal for students, analysts, and soccer fans alike, it offers instructions on models and techniques such as logistic regression, random forests, deep learning, simulations, and feature engineering. But instead of memorizing algorithms, you'll learn by building predictive models to analyze match outcomes, test betting strategies, run simulated game scenarios, and more. Understand machine learning concepts by working with real sports data Develop, refine, and evaluate machine learning models, using Python for data analysis Carry out detailed analyses and research on soccer game predictions and betting strategies to surface valuable insights Apply the skills you learn to predictive modeling scenarios in other industries

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9781098181116

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Dr. Haipeng Gao is a machine learning expert with extensive industry experience, having held machine learning roles at multiple leading tech companies spanning several core business domains, including Ads, Trust, Risk, and CRM. In addition, Dr. Gao has taught Statistics and Probability at the University of North Carolina at Chapel Hill and San Jose State University. This combination of industry experience and academic services gives Dr. Gao unique insights into both the theoretical foundations and practical applications of machine learning and data science. Dr. Gao holds a Ph.D. in Statistics and Operations Research from the University of North Carolina at Chapel Hill. Ari Joury is the founder of Wangari Global, a financial analytics firm. As a former particle physicist in academia as well as a climate risk scientist in the insurance industry, he has racked up a lot of experience in statistical modeling. His blog entries and online learning materials have been viewed over two million times. Dr. Weining Shen is an associate professor of statistics at the University of California, Irvine. His research interests include sports analytics, machine learning, Bayesian inference, and large language models. He has published over 60 papers at major statistics journals and machine learning venues. Dr. Shen holds a PhD in Statistics from North Carolina State University. Dr. Guanyu Hu is an assistant professor at The University of Texas Health Science Center at Houston. Specializing in spatial statistics, Bayesian computation, and sports analytics, Dr. Hu has led numerous NSF-funded projects and published extensively in top statistical journals. He also serves as an associate editor for several prominent journals, chair of Statistics in Sports Section of American Statistical Association and is an organizer of the American Soccer Insights Summit. Dr. Hu holds a PhD in Statistics from Florida State University.

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