Supervised Learning

edX MOOC / Non-credit USD 249

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Supervised Learning

About this course

Learn how to build supervised learning models using Python and Sklearn (Sci-Learn). This course includes the most popular supervised learning models, including K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Regression, Random Forest and Decision Trees. With Sklearn and Python all of these models can be quickly created using just a few lines of code.

What you'll learn

understand how to implement K-Nearest Neighbor models
create Support Vector Machines
apply Regression techniques
develop Random Forest models
build Decision Trees using Python and Sklearn

Course objectives

to equip students with the skills to build various supervised learning models
to enhance proficiency in using Python and Sklearn for machine learning tasks

Skills you'll gain

python regression machine learning data science supervised learning decision trees random forest svm KNN sklearn

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