Master enterprise-grade Artificial Intelligence by designing, training, optimizing, and deploying production-ready AI systems. Build Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, AI Agents, Computer Vision applications, and distributed machine learning workloads using modern AI frameworks and cloud-native infrastructure.
Curriculum
- 8 Sections
- 24 Lessons
- 12 Weeks
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- Foundations of Artificial Intelligence & Intelligent ComputingDescription 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 Lifecycle9
- 1.1Welcome to the Course
- 1.2What is Artificial Intelligence?
- 1.3Machine Learning vs Deep Learning vs Generative AI
- 1.4AI System Architecture
- 1.5Understanding Intelligent Agents
- 1.6Types of AI Agents
- 1.7AI Basics Quiz5 Questions
- 1.8Machine Learning Fundamentals Quiz6 Questions
- 1.9Artificial Intelligence Concepts Quiz4 Questions
- Mathematical Foundations & Scientific ComputingDescription 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 & Optimization4
- Python for AI Software EngineeringDescription Develop enterprise-grade Python applications for Artificial Intelligence by mastering advanced programming concepts, software architecture, asynchronous execution, REST API development, dependency management, testing, debugging, and performance optimization. Build scalable AI-ready applications following software engineering best practices. Lessons Python Fundamentals Object-Oriented Programming NumPy & Scientific Computing Pandas Data Engineering FastAPI Development Testing & Debugging5
- Machine Learning Systems EngineeringDescription 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 & Validation5
- Deep Learning & Neural Network EngineeringDescription 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 Optimization5
- Transformer Architecture & Large Language Models (LLMs)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 & Optimization5
- Retrieval-Augmented Generation (RAG) EngineeringDescription 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 Pipeline5
- Autonomous AI Agents & Workflow OrchestrationDescription 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 Collaboration0
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