AI Engineering Training in Chennai

• VS Code, Python and AI development setup
• Python fundamentals and programming logic
• Functions, modules and object-oriented programming
• Virtual environments and package management
• Git, GitHub and version control
• APIs, JSON and environment variables

Beginner AI Developer Starter Python Project.

• NumPy and Pandas
• Data cleaning and preprocessing
• Missing values and outlier handling
• Exploratory Data Analysis
• Statistical fundamentals for AI
• Feature engineering
• Data visualization with Matplotlib

Beginner → Intermediate Real-World Data Analysis Project.

• Supervised and unsupervised learning
• Regression and classification
• Decision Trees, Random Forest and Gradient Boosting
• Clustering and dimensionality reduction
• Feature selection and scaling
• Model evaluation and cross-validation
• Hyperparameter tuning
• Scikit-learn pipelines

Intermediate End-to-End ML Prediction System.

• Neural network architecture
• Forward and backward propagation
• Activation, loss functions and optimizers
• PyTorch and TensorFlow fundamentals
• Convolutional Neural Networks
• Transfer learning
• Model training and optimization

Intermediate → Advanced AI Image Classification System.

• Generative AI and LLM fundamentals
• Transformer architecture
• Tokens, embeddings and context windows
• Prompt engineering and structured outputs
• LLM APIs and SDKs
• Open-source LLMs and Ollama
• LLM application architecture
• LLM evaluation fundamentals

Advanced LLM-Powered AI Application.

• RAG architecture and workflows
• Document loading and preprocessing
• Chunking strategies
• Embeddings and semantic search
• Vector databases
• Retrieval optimization and metadata filtering
• RAG evaluation and hallucination reduction

Advanced Enterprise RAG Knowledge Assistant.

• AI agent architecture and reasoning
• Function calling and tool integration
• Agent memory and context management
• Planning and multi-step workflows
• Single-agent and multi-agent systems
• MCP fundamentals and MCP servers
• External API and tool integration
• Agent evaluation and guardrails

Advanced Autonomous AI Research Agent.

• Computer vision and OCR fundamentals
• Vision-language and multimodal models
• Multimodal document processing
• FastAPI for AI applications
• Docker and containerization
• Cloud deployment fundamentals
• Monitoring, logging and model versioning
• AI security and responsible deployment

Advanced Multimodal AI Application.

• Business problem identification and solution design
• AI system architecture
• LLM, RAG and agent integration
• API and application development
• AI testing and evaluation
• Security and performance optimization
• Cloud deployment and monitoring
• GitHub portfolio and project presentation

Expert / Capstone Enterprise Agentic AI Copilot.