Data Science & Engineering

Build a solid foundation in Python programming with focus on data structures, libraries like NumPy and Pandas, and scientific computing. This module prepares you for machine learning workflows.

Understand core ML concepts including supervised/unsupervised learning, training/validation/test splits, and evaluation metrics. Learn how to approach ML problems systematically.

Enter the world of neural networks and deep learning. Learn about perceptrons, backpropagation, and building your first neural networks using PyTorch.

Explore specialized neural network architectures including CNNs for computer vision and RNNs/LSTMs for sequential data. Understand their strengths and applications.

Understand the transformer architecture that powers modern LLMs. Learn about self-attention, multi-head attention, and the encoder-decoder structure fundamental to NLP.

Dive into natural language processing, learning about embeddings, language models, and how to work with text data effectively. Prepare for understanding LLMs.

Learn the intricacies of training LLMs including distributed training, optimization techniques, and practical fine-tuning strategies. Understand what happens at scale.

Explore the intersection of computer vision and language, learning to build models that understand both images and text. Understand vision transformers and multimodal architectures.

Explore cutting-edge optimization techniques, efficient architectures, and emerging research directions. Stay at the frontier of deep learning.