03 - Decision Trees and Random Forest: Classification and Regression
Discover decision trees (interpretable, easy to understand) and random forests (ensemble of trees fo…
02 - Linear and Logistic Regression: From Theory to Practice
Master the 2 most important ML algorithms: linear regression for numerical predictions and logistic regression for binary classification. Learn essential math, Python implementation with scikit-learn, evaluation metrics, and how to recognize and fix overfitting issues.
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Master the 2 most important ML algorithms: linear regression for numerical predictions and logistic regression for binary classification. Learn essential math, Python implementation with scikit-learn, evaluation metrics, and how to recognize and fix overfitting issues.
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- Intermediate Mathematics and Statistics for AILinear algebra, probability, and optimisation are the essential mathematical tools underpinning ML.
- Advanced MLOps and Machine Learning in ProductionMLOps takes ML models from the training environment to production in a reliable and scalable way.
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