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ML Fundamentals
Supervised, unsupervised, reinforcement learning, transfer learning, overfitting, and the core ideas every ML practitioner needs.
Algorithms
Linear regression, decision trees, SVMs, random forests, gradient boosting, and clustering — how they work under the hood.
Deep Learning
Neural networks, CNNs, RNNs, LSTMs, Transformers, GANs, and modern deep learning techniques with PyTorch examples.
Model Evaluation
Cross-validation, confusion matrix, AUC-ROC, bias-variance tradeoff, hyperparameter tuning, regularization, and gradient descent.
Data & Features
Data preprocessing, feature scaling, missing data, categorical encoding, outlier detection, feature selection, and SMOTE.
Advanced Topics
Explainable AI with SHAP and LIME, MLOps, model deployment, containerization, and production ML monitoring.
Concepts & Theory
Probability, statistics, linear algebra, and the mathematical foundations of ML.
Programs & Labs
Hands-on projects and coding exercises to apply ML concepts end-to-end.