jbagnato/machine-learning

Covers classical and deep learning algorithms—logistic/linear regression, decision trees, k-means clustering, k-NN, naive Bayes, random forests, neural networks, and CNNs—alongside practical applications like web scraping, NLP, time-series forecasting, and image classification. Implementations use scikit-learn, Keras, and TensorFlow, with notebooks designed for both local development (Anaconda) and cloud execution via Google Colaboratory with GPU support. Real-world datasets span financial markets, housing decisions, music charts, and text corpora to bridge theory and applied problem-solving.

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