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.

Prérequis

  • none

Pedagogical and technical material and resources

  • Dedicated digital training platform (LMS).
  • 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

    Prerequisites

    • None

    Instructional and supervisory resources

    • In-class training sessions,
    • Instructional materials in digital format,
    • Concrete case studies,
    • Theoretical presentations,
    • Use of data provided by participants,
    • In-depth work on the data,
    • Paper-board, video projector, internet connection

     

    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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