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.
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About this Course
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- Decision Tree
- Ensemble Learning
- Classification Algorithms
- Supervised Learning
- Machine Learning (ML) Algorithms
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Syllabus - What you will learn from this course
Logistic Regression
K Nearest Neighbors
Support Vector Machines
Decision Trees
Reviews
- 5 stars89.14%
- 4 stars9.04%
- 3 stars0.90%
- 1 star0.90%
TOP REVIEWS FROM SUPERVISED MACHINE LEARNING: CLASSIFICATION
Great! Helps me build my career path in Data Science
I would like to give especial thanks to the instructor (the one in the videos) for his great job. It would be nice to know who is is.
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.
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
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