Introduction to Machine Learning in Sports Analytics(으)로 돌아가기
미시건 대학교의 Introduction to Machine Learning in Sports Analytics 학습자 리뷰 및 피드백
강좌 소개
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.
필터링 기준:
Introduction to Machine Learning in Sports Analytics의 2개 리뷰 중 1~2
교육 기관: Leonardo A
•2021년 9월 14일
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!
교육 기관: Artúr P S
•2021년 11월 6일
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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