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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


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....

1 - Introduction to Machine Learning in Sports Analytics 的 3 个评论(共 3 个)

创建者 Leonardo A

Sep 14, 2021

I've learned very interesting things about how to obtain, clean and preprocesse data. Also the Machine Learning tecniques although are very simple but very powerful. Thank you!

创建者 Lam C V D

Dec 18, 2021

T​he labs need more clarity in instructions

创建者 Artúr P S

Nov 6, 2021

Entirely different difficulty than the other courses. It seems like a whole another level, starts from a very high complexity. The quizzes ask questions which are much more deep level than the videos or the commentary.