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Enseignement ATHENS - TPT40 : Practice of Large Scale Machine Learning

Descriptif

Good knowledge of Python / Jupyter notebook.

Good knowledge in probability.

Experiment the basics of supervised machine learning (logistic regression, random forest, xgboost, etc.). Discover tools to handle large datasets (Hadoop Spark) (n.b.: No deep learning in this course)

Objectifs pédagogiques

Day 1: Introduction to Pandas and Scikit-learn – Logistic regression – The Titanic dataset.

Day 2: Feature engineering - Random Forest, xgboost – The Avazu dataset.

Day 3: Mini-challenge

Day 4: The computing tools for large scale machine learning

Day 5: Introduction to Spark Mllib

Format des notes

Numérique sur 20

Pour les étudiants du diplôme Diplôme d'ingénieur

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 3 ECTS
  • Crédit d'UE électives acquis : 3
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