Machine Learning

Provides students with a broad knowledge in two main areas of Machine Learning: supervised and unsupervised. The program introduces systems that learn from experience and outline the problems based on classification, clustering and regression. Covers topics like Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, Ensemble Learning with Bagging and Boosting, Random Forest, k-NN, Dimensionality Reduction, Principal Component Analysis, K-means algorithm, Self-Organizing Feature Maps, Apriori algorithm, FP-growth algorithm, Dimensionality Reduction, Anomaly Detection and Semi-supervised Learning.

In Class, Assignment

Completing the Diploma in Machine Learning exposes students to various fields for hire, predominantly for Machine Learning Engineer, software engineers, information systems, information analysts, research analyst, data management, and data analysis in various IT industries. The technology sector in Canada is growing at an expansive rate which is allowing for new opportunities to arise in this field. The job market for graduates with understanding in artificial intelligence, machine learning and deep learning in IT industry is projected to grow 56% in the next few years and create new jobs as the number of companies expanding their IT departments, are growing.