Machine Learning: Classification
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About this course
This course covers key supervised machine learning (ML) and classification techniques, including logistic regression, decision trees, ensemble methods, and handling unbalanced datasets. Build and evaluate classification models using real-world data.
What you'll learn
- understand supervised machine learning principles
- apply logistic regression for classification tasks
- build decision tree models
- implement ensemble methods
- manage unbalanced datasets in machine learning
Course objectives
- enable students to construct classification models
- teach methods for evaluating model performance
- provide insights into handling real-world data challenges
Skills you'll gain
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