How to model using Machine Learning techniques?

Machine Learning offers a wide range of powerful prediction tools, such as neural networks or decision trees. This course aims at providing the methodological bases to take advantage of the different types of most commonly used models from diverse data.

2-day course

Objectives

  • Learn how to prepare a set of data.
  • Learn, understand and use the different families of predictive models.
  • Learn how to evaluate the performance of a predictive model.

Prerequisites

  • Knowledge of mathematics and probability.
  • Knowledge of Python recommended.

AUDIENCE

  • R&D Engineer
  • Design Office Engineer
  • Operational Safety Engineer.

Upcoming sessions

  • June 3-4, 2021
  • December 2-3, 2021

PLACE

18/20 Boulevard Reuilly, 75012, Paris ; Métro Dugommier


MANAGER

Fabien Taghon

Program

Introduction to data analysis with python

Reading a dataset

Cleaning and preparation of a dataset

Regression and classification models

Introduction to the most common models

  • Lasso and Ridge Linear Regressions
  • Logistic regression
  • K nearest surroundings
  • Decision trees
  • Neural networks

Practice work with Python

catalogue_2021_incertitude

Application form

Register or ask for further information. Personalised training courses can also be provided. The location of the training is subject to change. Training courses can be held at a distance. In this case, the training can be delivered in several short sessions (e.g. 2 hours) via videoconference software, spread over several days, in agreement with the trainer and the participants.



or





The following information is retrieved for the following purpose: management of the commercial relationship.
Phimeca undertakes to comply with the regulations in force applicable to the processing of personal data and, in particular, Regulation (EU) 2016/679 of the European Parliament of 27 April 2016 applicable from 25 May 2018 (the “European Data Protection Regulation” or GDPR).
You have the right of withdrawal and can write to dpo@phimeca.com