Machine Learning: basics

Analysis
Data Science
Open Source
On-site courses
Face-to-face
Remote/Virtual
English
French

Objectives

  • Understand the basics of machine learning and machine learning on structured data, apply standard dimension reduction and clustering methods, know how to implement a regression model by controlling overfitting and validating model predictions, understand the basics of text mining.
  • At the end of this training, the participant will be able to determine the type of techniques to apply based on the questions asked and perform elaborate pre-processing to implement predictive models.

Prerequisites:

  • none

Pedagogical and technical material and resources:

  • Sessions with the trainer, training material in digital format, balance between theoretical and practice, concrete cases.

Assessment:

  • Practical application and exercises

Results & skills expected at the end of the training:

  • At the end of this training, the participant will be able to determine the type of techniques to apply based on the questions asked and perform elaborate pre-processing to implement predictive models.

Program

DAY 1

  • Introduction to unsupervised methods:
    • Principal component analysis (PCA)
    • Automatic classification (k-means)
    • Association rules (apriori, eclat)
  • Introduction to supervised methods:
    • Linear and logistic regression models with regularization (ridge regression)
    • Decision trees (regression and classification)

DAY 2

  • Standard methods for implementing predictive models:
    • Feature engineering: learning to reduce the complexity of a problem,
    • Variable selection,
    • Cross-validation,
    • Calibration of a predictive model
    • Text mining and web scraping
Download the full program
Duration
14 hours
Level
Beginner
Audience
Anyone who wants to discover the basic principles of Machine Learning.
Participants
8 people maximum
Please contact us for a personnalized offer.

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