DUBLIN | 2nd March 2018| BOOK HERE

Course Description

This course will provide you with an introduction to core Machine Learning techniques: you will get an overview of the Machine Learning landscape as well as tools and resources in Python.


Learning Objectives

  • To have a familiarity with the Machine Learning landscape
  • Understand the different types of Machine Learning algorithms
  • Identify suitable Machine Learning techniques for different types of data
  • To be understand how to implement and evaluate basic Machine Learning algorithms in Python

Course set-up

None – materials will be provided



1 day


Target Audience

This is ideally suited for Python developers. No Machine Learning experience is required.


Course Prerequisites

Proficiency in Python development including: array manipulations with NumPy, loading libraries, and plotting using Matplotlib.


Course Content


  • A high level overview of key Machine Learning concepts

Topic 1

  • Introduction to Regression: a supervised learning technique
  • Regression analysis
  • Regression demonstration

Topic 2

  • Working with data
  • Feature transformation introduction and demonstration
  • Feature selection introduction and demonstration

Topic 3

  • Introduction to clustering: an unsupervised learning technique
  • Clustering demonstration

Topic 4

  • Introduction to data classification
  • Classifying data using Na├»ve Bayes demonstration

Topic 5

  • Introduction to time-series analysis
  • Time series analysis demonstration

Topic 6

  • Introduction to Neural Networks
  • Demonstration of Multilayer perceptrons


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