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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.

• 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.