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Machine Learning & Deep Learning
Core ML/DL concepts: supervised and unsupervised learning, neural networks (CNN, RNN/LSTM, GAN, Transformers), training techniques, model optimization, and real-world applications in software engineering and NLP.
Unsupervised Learning
Clustering (k-means, hierarchical, DBSCAN), dimensionality reduction (PCA, t-SNE), association rules, and anomaly detection.
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