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学生对 密歇根大学 提供的 Introduction to Machine Learning in Sports Analytics 的评价和反馈

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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....



Dec 4, 2022

Outstanding course! Really interesting and tutor was really enthusiastic which kept the videos and assessments easy to work through.


Oct 24, 2022

Very hands-on course, I could understand all techniques available to model sports.


1 - Introduction to Machine Learning in Sports Analytics 的 5 个评论(共 5 个)

创建者 Leonardo A

Sep 14, 2021

创建者 Nathan M

Dec 5, 2022

创建者 Leonardo P d R

Oct 25, 2022

创建者 Lam C V D

Dec 18, 2021

创建者 Artúr P S

Nov 6, 2021