Build deep theoretical foundations for original AI research: math, RL, transformers, and cutting-edge topics.
Master Python fundamentals and essential libraries for ML including NumPy, pandas, and scikit-learn.
Build the mathematical foundation you need: linear algebra, calculus, probability, and statistics.
Supervised and unsupervised learning, model evaluation, feature engineering, and real-world ML pipelines.
Neural networks, CNNs, RNNs, and transfer learning. Build and train deep models from scratch.
MDPs, Q-learning, policy gradients, PPO, and deep RL. Train agents that learn from interaction.
Understand attention mechanisms, build transformers from scratch, and explore BERT, GPT, and beyond.