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Item #:
267647665
Soccer Analytics with Machine Learning: Learning Predictive Modeling Techniques with Sports Data
Item #:
267647665
AED 231
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This practical Python-focused guide introduces fundamental data science techniques using real soccer data.
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- 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 dataDevelop, refine, and evaluate machine learning models, using Python for data analysisCarry out detailed analyses and research on soccer game predictions and betting strategies to surface valuable insightsApply the skills you learn to predictive modeling scenarios in other industries
| Publisher | O'Reilly Media |
| Publication date | June 15, 2026 |
| Edition | 1st |
| Language | English |
| File size | 4.4 MB |
| Enhanced typesetting | Enabled |
| X-Ray | Not Enabled |
| Word Wise | Not Enabled |
| Print length | 538 pages |
| ISBN-13 | 978-1098181079 |
| Page Flip | Enabled |
| Item Weight | 1.5 lbs (680 grams) |
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Soccer Analytics with Machine Learning: Learning Predictive Modeling Techniques with Sports Data
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AED 231
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Features & Benefits
- Understand machine learning through real-world soccer data.
- Develop predictive models for match outcomes and betting strategies.
- Ideal for students, analysts, and soccer fans.
- Learn fundamental techniques like logistic regression and deep learning.
- Engage with practical applications rather than memorizing algorithms.
- Apply skills learned to predictive modeling in various industries.
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