Machine Learning: Advanced

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

Objectives

  • Master more complex machine learning models, in particular ensemble methods based on bagging and boosting techniques, to use and optimize penalty models (lasso and elasticnet), to understand the bootstrap resampling technique for estimation and cross-validation, to know how to implement collaborative filtering techniques.
  • At the end of this training, the participant will have a global vision of the different multivariate modeling techniques.

Prerequisites

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 have a global vision of the different multivariate modeling techniques.

Program

Day 1

  • Advanced data mining:
    • DBSCAN, unsupervised data clustering algorithm
    • Manifold Learning

DAY 2

  • Gaussian Mixture Modelling (GMM)
  • Optimizing penalty models with Lasso and elasticnet (regression, PLS)
  • Support Vector Machine (SVM)

DAY 3

  • Random Forest and Gradient Boosting Machines
  • Bootstraping estimation and cross-validation
  • Collaborative filtering and recommendation system

 

Duration
21 hours
Level
Intermediate
Audience
Anyone wishing to deepen their knowledge of Machine Learning.
Participants
8 people maximum
Please contact us for a personnalized offer.

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