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.
Offered By


About this Course
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessSkills you will gain
- Decision Tree
- Ensemble Learning
- Classification Algorithms
- Supervised Learning
- Machine Learning (ML) Algorithms
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Logistic Regression
K Nearest Neighbors
Support Vector Machines
Decision Trees
Reviews
- 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.
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.