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
- 5 stars88.23%
- 4 stars10.69%
- 3 stars0.53%
- 1 star0.53%
TOP REVIEWS FROM SUPERVISED MACHINE LEARNING: CLASSIFICATION
The course content is very great in the coding area and it is very helping. but a shortage that is clear is the theory behind every algorithm, the handling of it wasn't that much perfect.
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
Thank you Coursera.
Thank you IBM
Thank you to all instructors.
Great! Helps me build my career path in Data Science
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Certificate?
More questions? Visit the Learner Help Center.