Machine Learning & Data Science – IMR

 

In this series of presentations, Dr Brian O'Halloran - a highly experienced data scientist - provides a broad and unbiased overview of the machine learning landscape, from the introductory building blocks to more state-of-the-art approaches, with an overall lean towards uses in the manufacturing domain.

Is this course for you?

Intended for anyone interested in understanding and potentially using machine learning; it covers the core machine learning concepts plus more state-of-the-art approaches, planning and evaluating a machine learning project, plus outlining the pitfalls involved.

Delivery Online learning

Duration 1 Day
Cost €567
Level Introduction

Target Audience

Managers who want to learn the language and tools of Machine Learning.

Learning Objectives

Learn the most important steps and implementation concepts for the realization of machine learning projects.

Participants will be introduced to the practical implementation of machine learning algorithms through use-case examples in the machine learning programming language Python.

Module 01 Introduction (IMR), Introduction to ML

  • Intro IMR
  • What is Data Science & ML
  • ML/DS Problems
  • Use cases: Supply chain, Smart robots Process improvements, Maintenance
  • Module 02 Approaching ML problems

  • What can go wrong in ML projects
  • How to ask the right questions
  • Simple workflow
  • More complex workflow
  • How do we predict things
  • Module 03 ML Algorithms – part #1

  • Regression
  • Classification
  • Evaluating Your Models
  • ML Algorithms – part #2

  • Unsupervised learning
  • Decision Trees/Random Forests
  • Time Series
  • Module 05 Advanced ML techniques & example(s) for manufacturing & Next Steps

  • Deep Learning
  • Computer Vision
  • Natural Language Processing
  • Course Content

    Delivery: Live facilitator led Microsoft Teams based workshops.
    Learning platform: Zoom or MS Teams.
    Learning methodology: Real world activities by topic.
    Learning transfer: SME presentation, interactive Q&A, Scenario based quiz and reinforcement reflection.