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Machine learning: overview of techniques (PT-ML)

Learning to use data by adapting processing method to each problem

Introduction

Machine learning overview: Concepts and glossary – Classification of problems – Algorithm family – Type of learning – Use case

Knowing the steps of a machine learning case

Data preparation: filtering, study of correlations, relevant data organisation – Selection of algorithm solution: regression, clustering, classification – Evaluation criteria

Understanding machine learning

Method mapping – Model choice – Existing tools – Possible results – Example on a relevant case

Discovering easy-to-use tools for machine learning

Presentation of tools to implement intelligent solutions

Presentation of Deep Learning tools

Image classification

Educational objectives

  • understand the terms and concepts used in data science, especially machine learning
  • discover the different classes of problems and families of machine learning solutions
  • know the different steps of machine learning and the importance of data preparation

Expected benefits

  • to be able to identify the machine learning techniques suitable for a project
  • to know the steps for implementing an efficient machine learning solution

Information

Duration: 1 day

Target audience:

  • engineers
  • senior technicians

Training level:

  • introduction course
  • technical course

Prerequisite:

  • knowledge of scientific calculation

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