Description Build a comprehensive understanding of Artificial Intelligence by exploring its evolution, computational paradigms, intelligent agents, knowledge representation, search algorithms, and decision-making systems. Learn the differences between AI, Machine Learning, Deep Learning, and Generative AI while understanding enterprise AI architectures and industry adoption. This module establishes the theoretical foundation required for advanced AI engineering. Lessons Introduction to Artificial Intelligence History & Evolution of AI Types of Artificial Intelligence AI vs Machine Learning vs Deep Learning Enterprise AI Ecosystem AI Development Lifecycle

Description Master the mathematical principles that power modern machine learning algorithms. Learn linear algebra, multivariable calculus, probability theory, statistical inference, optimization techniques, and numerical computing using Python. Develop the analytical skills required to understand research papers and optimize complex neural network architectures. Lessons Linear Algebra for Machine Learning Matrix Operations & Vector Spaces Differential Calculus Probability Theory Bayesian Statistics Gradient Descent & Optimization

Description Learn the complete machine learning lifecycle from data preprocessing and feature engineering to model training, evaluation, optimization, deployment, and monitoring. Implement supervised and unsupervised learning algorithms while developing production-ready predictive systems. Lessons Data Preparation Feature Engineering Regression Algorithms Classification Algorithms Ensemble Learning Model Evaluation & Validation

Description Design and implement advanced neural network architectures using TensorFlow and PyTorch. Learn backpropagation, activation functions, convolutional neural networks, recurrent neural networks, attention mechanisms, and distributed GPU training for large-scale deep learning applications. Lessons Artificial Neural Networks CNN Architecture RNN & LSTM Networks Attention Mechanisms Distributed Training GPU Optimization

Description Explore the architecture behind modern Large Language Models by implementing transformer networks, self-attention mechanisms, embeddings, tokenization, decoder architectures, and fine-tuning methodologies. Learn inference optimization using LoRA, PEFT, ONNX Runtime, and TensorRT for enterprise AI deployments. Lessons Transformer Architecture Self-Attention Embedding Models Tokenization Hugging Face Transformers LLM Fine-Tuning Quantization & Optimization

Description Build enterprise Retrieval-Augmented Generation systems using vector databases, semantic embeddings, document chunking, hybrid retrieval strategies, reranking algorithms, and knowledge-grounded AI architectures. Develop scalable enterprise search solutions integrated with modern LLMs. Lessons Introduction to RAG Embedding Models FAISS & Pinecone Qdrant & Milvus LangChain Retrieval Hybrid Search Production RAG Pipeline

Description Engineer autonomous AI agents capable of reasoning, planning, tool invocation, API integration, memory management, and multi-agent collaboration. Implement intelligent workflows using LangChain, LangGraph, CrewAI, AutoGen, and enterprise orchestration frameworks. Lessons Agentic AI Fundamentals LangChain LangGraph CrewAI AutoGen Memory Systems Multi-Agent Collaboration