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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Intermediate Level
Approx. 25 hours to complete
English
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Try Coursera for BusinessSkills you will gain
- Decision Tree
- Ensemble Learning
- Classification Algorithms
- Supervised Learning
- Machine Learning (ML) Algorithms
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 25 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
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Reviews
- 5 stars89.15%
- 4 stars8.96%
- 3 stars0.94%
- 1 star0.94%
TOP REVIEWS FROM SUPERVISED MACHINE LEARNING: CLASSIFICATION
by YSJun 30, 2022
Great! Helps me build my career path in Data Science
by RPApr 12, 2021
I recommend this course to everyone who wants to excel in Machine Learning. This is a Great Course!
by NRFeb 21, 2022
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.
by KUDec 23, 2020
This course is has a detailed explanation on each and every aspect of classification.
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