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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.
Data Preparation & Feature Engineering
Data cleaning, normalization vs standardization, feature selection, dimensionality reduction, handling missing values and imbalanced datasets.
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