Overview

This course aims to provide students with a broad overview of the field of machine learning and will introduce some important terms and techniques which will enable them to follow a discussion on the topic. I will discuss the fundamental aspects of data pipelines and will point out what some of the common pitfalls are when preparing data for a machine learning project. I will also discuss what the different types of machine learning models are and how they differ from deep learning models.Broad overviews will be provided of some of the most popular supervised and unsupervised models and students will be introduced to some of the popular neural network variants. This will be followed by a few practical demonstrations which will show students how they can combine the discussed topics to create basic machine learning solutions.This course will not provide in-depth explanations regarding the mathematical underpinnings of these models, nor will it provide detailed discussions regarding how to implement machine learning models from scratch. Instead, the aim is to simplify and condense the subject matter to provide students with an easily digestible introduction to the field.Whether students are employers or employees, we believe it to be highly beneficial to have a basic understanding of what machine learning models are and what they are not -- especially as machine learning tools become increasingly common in many domains.

Skills

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