Fondamenti del machine learning: regressione, classificazione, clustering, alberi decisionali, random forest, SVM, ensemble methods e valutazione dei modelli con scikit-learn e Python.
Learn what Machine Learning is, the 3 fundamental types (supervised for predictions, unsupervised for patterns, reinforcement for decisions), real-world use cases, and a roadmap to choose the right paradigm for your problem.
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.
Discover decision trees (interpretable, easy to understand) and random forests (ensemble of trees for better results). Learn how entropy works, how to build trees, overfitting issues, pruning, feature importance, and when to use them vs linear regression.
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