Soccer Analytics with Machine Learning (Final) by Haipeng Gao(.ePUB)+
File Size: 10 MB
Soccer Analytics with Machine Learning: Learning Predictive Modeling Techniques with Sports Data (Final Release) by Haipeng Gao, Ari Joury, Guanyu Hu, Weining Shen
Requirements: .ePUB, .PDF reader, 10 MB
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. Python is that environment for the rest of the book. It gives us a readable language, a mature ecosystem of data tools, and a practical workspace for moving from raw data to analytical output. Python is well suited to soccer analytics for several practical reasons. It is readable, expressive, and approachable, even for those with limited programming experience. Its syntax is designed to be clear and concise, allowing complex ideas to be implemented with relatively little code. This emphasis on clarity—often described as being Pythonic—reduces friction between thinking and implementation. Beyond its accessibility, Python has become one of the dominant languages in Data Science and Machine Learning. Its widespread adoption has led to a mature ecosystem of libraries, tools, and contributors across many domains. Sports analytics is no exception.
Genre: Non-Fiction > Tech & Devices
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