Introduction to Machine Learning

This introductory course to machine learning will give you an overview and understanding of the basic concepts of machine learning. You will become familiar with the different types of machine learning, understand how models are trained, and know how model performance is evaluated. All participants will gain an understanding of how the Python environment and supporting libraries can enable their machine learning solutions.

Course objectives

  • Become familiar with the main groups of machine learning algorithms and how they are applied
  • Understand the difference between supervised and unsupervised machine learning
  • Know how machine learning models are evaluated and understand the metrics used
  • Be able to identify business cases where machine learning can be used successfully

Prerequisites:

Basic coding knowledge (any language)

Audience:

  • Managers and business professionals wanting to build basic understanding of machine learning
  • Developers and aspiring data scientists who want to get started with applied machine learning
  • Anyone involved in projects or business functions related to artificial intelligence

Course outline

  • Introduction to artificial intelligence and machine learning
  • Introduction to Python and Python notebooks and the most important machine learning libraries
  • Get basic understanding of different machine learning models including support vector machines, clustering, decision trees, neural networks and deep learning models
  • Build understanding about how algorithms learn, and how we can optimize learning
  • Learn how to evaluate the performance of machine learning models and understand the most important metrics.
  • Investigate example models/learning showing how some machine learning models are applied
  • Work through a decision tree example (pre-prepared code example/exercise in Python notebook)