Master the full machine learning stack from Python fundamentals through production deployment and system design.
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.
Understand attention mechanisms, build transformers from scratch, and explore BERT, GPT, and beyond.
From tokenization to modern NLP: embeddings, sequence models, text classification, and generation.
Image classification, object detection, segmentation, and generative models for visual data.
Design scalable ML systems: recommendation engines, search ranking, fraud detection, and real-time serving.