Practical Application of Machine Learning and Artificial Intelligence – Dr Deanne Larson

 

Overview
and
Objectives

Introduction to Data Mining in Python

The course will introduce data miners to the Python environment and fundamentals as well as how to use popular packages to manipulate data and apply statistical analysis. The course will also introduce how to do linear and logistic regression and some basic classification models.

 

  • How to install Python and maneuver the environment
  • How to use Pandas
  • How to use the Data Frame
  • How to apply statistical analysis
  • Understand linear and logistic regression
  • Apply linear and logistic regression
  • Introduction to basic classification models

Overview
and
Objectives

Introduction to AI and Deep Learning

Deep learning and artificial intelligence (AI) is a capability organisations are striving to adopt for the competitive edge. Deep learning systems can be the differentiation factor between your organisation and your competitor.  This course focuses on deep learning concepts and principles and ensuring that the “black box” of deep learning is explained. Additionally, understand how to assess and prepare your organisation deep learning and AI readiness is explored.

 

  • Outline how deep learning works (explaining the black box)
  • Understand the applications of deep learning
  • Identify the readiness factors required to adopt deep learning
  • Preparing for deep learning projects
  • Assessing organisational readiness for deep learning