Description for ML in Sports Analytics An Introduction
Supervised Machine Learning Techniques: Acquire practical expertise with supervised machine learning techniques, encompassing support vector machines, decision trees, random forests, and both linear and logistic regression.
Implementation of Python's Scikit-Learn Toolkit: Master the implementation of machine learning algorithms with scikit-learn, a robust Python toolkit, through the application of real-world data for pragmatic insights.
Application to Real-World Athletic Data: Examine data from sources such as professional sports leagues (NHL and MLB) and wearable devices, such the Apple Watch and IMUs, to formulate significant forecasts.
Broad Exploration of Classification and Regression: Comprehend how classification and regression methodologies can generate insights in sports analytics across diverse athletic activities and events.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by University of Michigan
Duration: 3 weeks at 4 hours a week
Schedule: Flexible
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