Welcome!

Course Learning Outcomes

By the end of the course, students are expected to be able to:

• Describe supervised learning and identify what kind of tasks it is suitable for.
• Explain common machine learning concepts such as classification and regression, training and testing, overfitting, parameters and hyperparameters, and the golden rule.
• Identify when and why to apply data pre-processing techniques such as scaling and one-hot encoding.
• Describe at a high level how common machine learning algorithms work, including decision trees, and 𝑘-nearest neighbours.
• Use Python and the scikit-learn package to develop an end-to-end supervised machine learning pipeline.

Prerequisites

Before we proceed to Module 1, it is important to make sure you have a solid foundation of coding in Python.

Have you taken Programming in Python for Data Science?

Make sure you are familiar with basic Python programming concepts as they are essential for this course.