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Bewertung und Feedback des Lernenden für Introduction to Machine Learning in Sports Analytics von University of Michigan
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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.
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1 - 3 von 3 Bewertungen für Introduction to Machine Learning in Sports Analytics
von Leonardo A
•14. Sep. 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!
von Lam C V D
•18. Dez. 2021
The labs need more clarity in instructions
von Artúr P S
•6. Nov. 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.
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