Machine Learning for decision makers

This course takes 1 day with 8 hours (on site) or 5 hours (online).
The only difference is that there will be less practical exercises in an online course. However, we will hand them over to you and you can still do them on your own and ask our consultants for feedback or help if needed.

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.