Machine Learning Course

Design a machine learning course: supervised learning (regression, classification, SVM, decision trees), unsupervised learning (clustering, PCA, anomaly detection), deep learning fundamentals (neural networks, CNNs, RNNs), practical projects with real datasets, model evaluation and selection, and deployment basics. Include math prerequisites and recommended resources