10 Weeks Advanced AI & AI Agent Course

Deepen your understanding of modern LLM architectures by building and running models locally. Master the end-to-end data pipeline, from raw ingestion to training a custom transformer from scratch for text prediction.

Learn to optimize open-source models using techniques like LoRA and QLoRA for domain-specific data. Focus on evaluation metrics such as Perplexity and ROUGE to ensure high-quality performance.

Explore Retrieval-Augmented Generation by building pipelines connecting LLMs to external documents. Implement vector search and indexing using FAISS and Chroma for high-accuracy retrieval.

Advance RAG implementations by managing complex knowledge ingestion pipelines and metadata. Focus on factual consistency, automated evaluation dashboards, and multi-domain knowledge bases.

Expand into vision and audio by integrating models like CLIP, BLIP, and Whisper. Build assistants capable of image captioning, transcription, and multimodal reporting.

Transition from static models to reasoning agents using prompt chaining, state management, and tool integration. Build a local automation agent capable of planning and task execution.

Design ecosystems where specialized agents collaborate using frameworks like CrewAI and LangGraph. Create planner-executor workflows and domain-specific monitoring systems.

Prepare AI applications for real-world deployment using Docker and observability tools. Focus on safety, governance, guardrails, content filtering, and PII redaction.

Build a full-scale multi-agent GenAI product from ideation to testing and demo. Cover the entire lifecycle including implementation, support, and final rehearsal.

Explore autonomous AI systems by building research, operational, and collaborative agents. Conclude with a Demo Day showcasing systems that can search, reason, and report autonomously.