This module builds on Module 9 to teach practical ML techniques that can be applied to real world datasets in multiple domains. The module covers a range of topics from linear classification and perceptrons to decision trees and random forests. It teaches the students to correctly apply ML, interpret results and refine the models. The highlight of the module is a real-world classification project on tabular data that allows students to apply the concepts they have learned in a practical setting.

Values:

• Machine learning and data analysis are high-in-demand fields of expertise.

• Students will be able to analyze real world datasets with sophisticated ML algorithms and make better decisions.

• Students will have the necessary skill set to find an ML solution to a business problem and create their own startup based on it.