Supervised Learning

The Supervised Learning course covers how supervised learning models work and how to quickly and efficiently code them using the Sklearn libraries in Python. The most popular and commonly used supervised learning models are taught including K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Regression, Random Forest and Decision Trees. The course covers how each algorithm works and types of problems that the algorithm is good for solving. But students will not need to spend large amounts of time coding the algorithms, because Sklearn normally reduces the creation and training of the algorithm down to just a few lines of code. The course ends with a capstone project that allows students to demonstrate their knowledge

• Understand how the K-Nearest Neighbor algorithm works and how to code it in Python
• Understand how the Support Vector Machine algorithm works and how to code it in Python
• Understand how different types of Regression algorithm work and how to code them in Python
• Understand how the Random Forest algorithm works and how to code it in Python
• Understand how the Decision Trees algorithm works and how to code it in Python
• Evaluate different supervised learning algorithms for a given problem

• Students should have a basic level of coding knowledge in Python.