This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
About this Course
Skills you will gain
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- 5 stars87.87%
- 4 stars10.90%
- 3 stars0.60%
- 1 star0.60%
TOP REVIEWS FROM SUPERVISED MACHINE LEARNING: CLASSIFICATION
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
Great course, well structured. The presentation of the different methods is very clear and well separated to understand the differences. A good understanding of classifiers is gained from this course.
The course is very well structured, and the explanations very clear. I would only suggest enhancing the peer-review community since it takes a long time to get a review sometimes.
Great! Helps me build my career path in Data Science
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