Data mining has become an integral part of R&D activities.

However, understanding the issues and the state of the art in the field requires a solid mathematical background.

Technical resources

Course material projected during training and sent to all trainees at the end of the course; case studies and practical examples chosen according to trainees' areas of interest

Performance monitoring

All trainees are asked to sign in every half-day Evaluation: Questionnaire to assess skills acquired at the end of the course

Assessment of results

Post-training satisfaction questionnaire

Pedagogical objectives

Firmly focused on what is needed, this course aims to provide the theoretical foundations required to understand and apply recent advances in machine learning.

Technologies covered

Ipython notebook, git, PyTorch, TensorFlow, CPU vs GPU, numpy, Pandas, matplotlib, scikit-learn, bokeh

Target skills

  • Syntax, flow controls
  • Object programming, inheritance
  • Data visualization