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學生對 密歇根大学 提供的 Introduction to Machine Learning in Sports Analytics 的評價和反饋

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11 個評分

課程概述

In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. Building on the previous courses in the specialization, students will apply methods such as support vector machines (SVM), decision trees, random forest, linear and logistic regression, and ensembles of learners to examine data from professional sports leagues such as the NHL and MLB as well as wearable devices such as the Apple Watch and inertial measurement units (IMUs). By the end of the course students will have a broad understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events....

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1 - Introduction to Machine Learning in Sports Analytics 的 4 個評論(共 4 個)

創建者 Leonardo A

2021年9月14日

創建者 Leonardo P d R

2022年10月25日

創建者 Lam C V D

2021年12月18日

創建者 Artúr P S

2021年11月6日