This course is the first in the program’s Machine Learning series. By the end of the series, students will
be expected to:

1. Learn and appreciate the processes involved in machine learning from various datasets.
2. Acquire thorough knowledge of the different learning algorithms and how to apply them.
3. Select appropriate machine learning models for various problems including, but not limited to, classification, pattern recognition, and optimization.
4. Implement successful machine learning algorithms for various tasks.
5. Obtain skills in evaluating the performance of machine learning algorithms.
6. Know machine learning models’ limitations (technical, data privacy, ethical considerations).
7. Acquire skills at presenting and accurately communicating results obtained from machine learning models.
8. Learn how to read machine learning journals and implement/recreate solutions done by other practitioners.

Each course in the series contributes to the performance of these primary objectives.

This course will introduce students to the world of machine learning and predictive analytics. This will be a hands-on and application-heavy module, looking at real-world data and cases in different fields. At the end of the course, students are expected to acquire foundational machine learning skills, including hypertuning of parameters and model metrics. In addition, they will familiarize themselves with the best practices in predictive analytics, including how to evaluate models properly. The most critical aim of this part of the series is for students to have a clear framework for building mental models with machine learning.