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AI-Powered Automation Test Engineer Program

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Course Content

Phase 1: Python Programming & ML Foundations
Master Python for AI with data structures and preprocessing techniques. Build ML models covering regression, classification, and clustering basics.

Phase 2: Deep Learning & Neural Network Architectures
Implement CNNs for vision and RNNs for sequences with transfer learning. Optimize with gradient descent, backpropagation, and performance tuning.

Phase 3: Natural Language Processing Foundations
Handle tokenization, embeddings, sentiment analysis, and NER tasks. Prepare for generative systems through syntax-semantics understanding.

Phase 4: Large Language Models & Prompt Engineering
Explore transformers, attention, zero/few-shot, chain-of-thought prompting. Apply RAG and fine-tuning for domain-specific LLM customization.

Phase 5: LLM Application Development & Agentic AI
Build conversational AI, copilots, and autonomous task agents. Integrate memory, APIs, and orchestration for production workflows.

Phase 6: AI Engineering, Deployment & LLMOps
Version models, monitor performance, containerize, and scale architectures. Implement responsible AI governance for enterprise deployment.

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