Data Foundations & Engineering

• Cloud Data Warehousing: Advanced SQL & Snowflake architecture.

• Python for Analytics: High-performance data processing ($Pandas$, $Polars$).

• Mathematics for AI: Linear Algebra, $Calculus$, and Probability.

2. Machine Learning (ML)

• Supervised & Unsupervised: Regression, XGBoost, and Clustering.

• Feature Engineering: Automated selection and data transformation.

• Model Evaluation: Bias-Variance tradeoff and $F1-Score$ optimization.

3. Deep Learning & AI

• Neural Networks: Architecture of CNNs, RNNs, and MLPs using PyTorch.

• The Transformer: Understanding the $Self-Attention$ mechanism.

4. Large Language Models (LLM) & Gen AI

• Generative Architectures: Fine-tuning (LoRA/QLoRA) and Prompt Engineering.

• RAG (Retrieval-Augmented Generation): Connecting LLMs to Vector Databases.

• Agentic AI: Building autonomous agents that can execute SQL and use tools.

5. MLOps & Deployment

• Lifecycle Management: Model versioning, monitoring, and scaling.

• Ethics & Governance: Managing AI bias, safety, and data privacy.

Capstone Project: Build an AI Agent that queries a Snowflake database via natural language and provides a summarized business insight.