Machine Learning

This is a credited micro-credential course with an interactive learning experience delivered over two months with a total of 120 notional hours distributed between online lectures, physical practical sessions, self-learning, hands-on assignments and capstone project. This micro-credential is an intensive course that is designed to equip participants with a variety of most in-demand machine learning algorithms to build effective predictive models for data-driven that would guide future action using data-driven decisions. Then, participants who successfully complete the course will be eligible to a credit transfer.

• Introduction to Machine Learning (Supervised and Unsupervised Learning)
• Neural Networks
• Convolutional Neural Networks
• Deep Learning Models
• Linear Regression and Logistic Regression
• Decision Trees, Naïve Bayes, K-Nearest Neighbors
• K-means Clustering and Support Vector Machines
• Capstone Project Presentation

• Students and academics interested in machine learning.
• Practitioners seeking to apply advanced algorithms for data analysis and predictive modeling.