This module covers fundamental Python programming concepts essential for generative AI and machine learning, including programming fundamentals, data structures, control flow, file operations, functions, object-oriented programming, exception handling, data analysis, and visualization techniques.

This module provides comprehensive coverage of machine learning techniques including data preprocessing, feature engineering, supervised learning methods for regression and classification, unsupervised learning, clustering algorithms, dimensionality reduction, ensemble learning, time series analysis, reinforcement learning, and recommender systems.

This module focuses on deep learning and neural network architectures, covering foundational neural networks, optimization techniques, convolutional neural networks for computer vision, recurrent neural networks for sequential data, and LSTM networks for handling long-term dependencies.

This module covers comprehensive natural language processing techniques including NLP fundamentals, text processing and feature engineering, named entity recognition, parsing, tokenization, text encoding, sentiment analysis from basic to advanced levels, neural language models, machine translation, speech processing, multimodal NLP, and chatbot development.

This module provides essential knowledge of generative artificial intelligence, covering foundational concepts, autoencoder architectures, Generative Adversarial Networks (GANs), transformer architectures, attention mechanisms, small language models, and both essential and advanced prompt engineering strategies.

This module focuses on developing applications powered by large language models, covering LLM fundamentals, open-source models and variants, vector databases, Retrieval Augmented Generation techniques, LLM frameworks, application development, fine-tuning, model adaptation, deployment strategies, and cloud-based generative AI implementations.

This module covers agentic AI and autonomous systems development, including foundational concepts, architectural patterns, working with LangChain and LCEL, building AI agents with various frameworks like LangGraph and Phidata, implementing agentic RAG, developing multi-agent systems, advanced agent development with Autogen, agent observability, AgentOPs, and no-code/low-code agent building tools.

This module explores AI-driven software engineering practices, covering AI pair programming for enhanced developer productivity, AI-driven development and testing methodologies, mastering Amazon Q tools for developers, and implementing generative AI capabilities using Google's Vertex AI platform.

This module addresses LLMOps (Large Language Model Operations) for building scalable AI systems, covering MLOps and LLMOps fundamentals, infrastructure and tooling including open-source solutions, data management and model training best practices, continuous integration and deployment, scaling strategies, monitoring, governance, ethical AI considerations, and production LLM management.

This module involves a comprehensive capstone project where students apply all learned concepts and techniques from previous modules to solve real-world problems in generative AI and machine learning, demonstrating practical implementation skills and project management capabilities.