Isbn: 9781098181116 - soccer analytics with machine learning: learning predictive modeling techniques with sports data (10 Ergebnisse)

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Brand New. 300 pages. 9.19x7.00x9.19 inches. In Stock.

Zustand: New.

Zustand: NEW.

Zustand: New. 2026. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Neuware - 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. 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.…

Zustand: New.

Taschenbuch. Zustand: Neu. Soccer Analytics with Machine Learning | Learning Predictive Modeling Techniques with Sports Data | Haipeng Gao (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2026 | O'Reilly Media | EAN 9781098181116 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

Taschenbuch. Zustand: Neu. Neuware -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. 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 industriesLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 300 pp. Englisch.…

Taschenbuch. Zustand: Neu. Neuware -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. 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 300 pp. Englisch.…