Machine Learning for decision makers

Level:

Beginner

Date:

On demand

Duration:

1 days

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Audience

This Machine Learning course is for employees in managerial (technical and non-technical) and non-technical positions willing to deepen their knowledge on the possibilities of Machine Learning and how they can implement it in their own organisation.

Course outline

  • History and evolution of Machine Learning: Where do we come from and where are we now?
  • Main Machine Learning concepts
  • Introduction to supervised vs unsupervised learning

 

Supervised learning:

    • Regression
    • Classification
    • Deep Learning
    • Activity: possible applications of regression and classifications

 

Unsupervised learning:

    • Conceptual introduction
    • Activity: possible applications of unsupervised learning

 

Other Machine Learning related learning problems:

    • Optimization
    • Sorting
    • Reinforcement learning

 

Infrastructure in Machine Learning: 

    • Upscaling vs outscaling
    • Distributed computing
    • CPU vs GPU
    • How to estimate resource utilization

 

From lab to production:

    • Response times
    • Training serving skew
    • Online vs batch prediction

 

Planning a Machine Learning approach for a specific Use Case.

 

Final summary, discussion, Q&A, etc.

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