Learn Python from the ground up, including data processing, APIs, error handling, AI models QA automation.

Understand zero-shot (ZSL), few-shot (FSL), chain-of-thought (CoT), self-consistency, tree-of-thought (ToT) prompting to optimize AI models.

Develop intelligent chatbots, memory-based AI assistants, and AI-powered search systems.

Combine reasoning with action. Build AI agents that can decide, plan, and execute — calling calculators, APIs, and custom tools autonomously.

Train, specialize, and optimize foundation models using the Hugging Face Transformers library, a popular Python toolkit for working with modern AI models.

Efficiently fine-tune large foundation models using Low-Rank Adaptation (LoRA) and Adapter methods.

Implement safety layers with real-time toxicity filtering, factuality scoring, and fallback mechanisms to detect hallucinations, prevent harmful output, and ensure responsible AI behavior.

Implement test coverage for data pipelines, model performance, and LLM outputs, ensuring reliability in production.

Take models live. Deploy to Cloud for scalable, enterprise-grade inference.