Machine Learning: AdvancedHome Training Catalog Machine Learning: Advanced Analysis Data Science Open Source On-site courses Remote/Virtual Face-to-face 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. Prerequisite: Attended the « Bases du Machine learning course » 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 have a global vision of the different multivariate modeling techniques. Program Day 1 Advanced data mining: o DBSCAN, unsupervised data clustering algorithm o 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 Download the full program 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. Are you looking for information about a training course? You want to set up a customized training session? Contact our pedagogical team! Notice: JavaScript is required for this content.
