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.

The Introduction to Machine Learning in Sports Analytics course is an exceptional offering for anyone interested in blending data science with sports. This course provides a thorough and practical introduction to supervised machine learning techniques, using the Python scikit-learn toolkit, and is designed to build on prior knowledge from earlier courses in the specialization. One of the standout features of this course is its focus on real-world athletic data, including data from professional sports leagues like the NHL and MLB, as well as from wearable devices such as the Apple Watch and inertial measurement units (IMUs). This makes the course not only highly relevant but also engaging, as students get hands-on experience applying machine learning algorithms to actual sports data. Throughout the course, students are introduced to a variety of core machine learning methods including support vector machines (SVM), decision trees, random forests, linear and logistic regression, and ensemble methods. The course provides a balanced mix of theory and practical application, allowing students to not only understand the algorithms but also learn how to apply them effectively to predict athletic outcomes. The structure of the course is well-paced, and the content is presented in a clear and accessible way, even for those with limited experience in machine learning. The practical coding assignments are particularly valuable, as they help solidify concepts and enhance learning through direct application. By the end of the course, students will have a solid foundation in both classification and regression techniques, and will understand how these methods can be applied to real-world sports analytics. Whether you are a beginner in machine learning or looking to further develop your skills, this course offers an excellent opportunity to gain expertise in a rapidly growing field. Overall, I highly recommend the Introduction to Machine Learning in Sports Analytics course for anyone interested in the intersection of data science and sports. It offers a comprehensive, hands-on, and engaging learning experience that will equip you with valuable skills for tackling real-world sports analytics challenges.

Well-structured notebook, resourceful, applicable to real-world projects, clear and entertaining teaching. Highly satisfied. One of the best modules in the entire specialization.

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!

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

What an awesome course, interesting, challenging, gives new perspective and useful insights

Very good. Requires some experience in python. Content is up to date and practical.

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

Provide solid foundation for beginning supervised ML

The labs need more clarity in instructions