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
- Decision Tree
- Ensemble Learning
- Classification Algorithms
- Supervised Learning
- Machine Learning (ML) Algorithms
Syllabus - What you will learn from this course
K Nearest Neighbors
Support Vector Machines
- 5 stars89.18%
- 4 stars9%
- 3 stars0.90%
- 1 star0.90%
TOP REVIEWS FROM SUPERVISED MACHINE LEARNING: CLASSIFICATION
It's a greate course. I learned a lot, from deeper understanding basic algorithms to more advanced technique such as bagging and model explanability.
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
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.
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