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 stars89.35%
- 4 stars8.79%
- 3 stars0.92%
- 1 star0.92%
TOP REVIEWS FROM SUPERVISED MACHINE LEARNING: CLASSIFICATION
this course taught me a lot even after being a practioner for 10+ years!
Well-structured learning path. If you dont have previous python experience you can catch up after a couple of weeks as the workflow is similar regardless of the algorithmn you are using
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.
Amazing. Full of content, activities. A lot to learn
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