AI Engineering Course - Generative AI & Agentic AI

IBM & GeeksforGeeks Certification

Earn industry-recognized certification

Modern AI Toolkit

Work with the latest AI tools and technologies

Live Soft Skills Sessions

Biweekly expert-led sessions for interview success

Skills You'll Gain

Key AI Engineering concepts you'll build and master.

Tech Stack You'll Learn

Tools and technologies you'll work with

Projects You'll Build

Real-world projects to apply your skills

Build your first end-to-end neural network by training a model to recognize handwritten digits and understand how forward propagation and backpropagation work in a practical deep learning workflow.

Build multiple mini-projects using Hugging Face pipelines to explore pre-trained AI models and understand how modern NLP and AI tasks can be integrated into applications.

Adapt a pre-trained open-source model to a custom domain using transfer learning and the Hugging Face Trainer API, gaining practical experience in model adaptation and fine-tuning.

Build an AI-powered data analysis workflow that uses Large Language Models to interact with and analyze real-world datasets through effective prompt engineering.

Create a Natural Language to SQL system that enables non-technical users to query databases using plain English and convert natural-language questions into executable SQL queries.

Develop a grounded AI chatbot that answers questions about review data and deploy a shareable interactive interface using Gradio.

Build AI workflows using small language models running locally, exploring how prompt engineering can improve model behavior without relying entirely on cloud-based APIs.

Build a fully local Retrieval-Augmented Generation pipeline using small language models, enabling private document retrieval without sending data to external APIs.

Build an Agentic AI system where multiple specialized AI agents collaborate to research a market, analyze information, and generate a structured research report.

Develop an AI agent system that uses tools, memory, and a knowledge base to perform automated background verification and support real-world due-diligence workflows.

Build a multi-agent fact-checking system with an orchestrator, specialized agents, and adversarial debate to analyze claims and improve the reliability of AI-generated conclusions.

Rebuild the fact-checking workflow using LangGraph to implement graph-based agent orchestration, routing, cycles, and checkpoint-based execution.

CAPSTONE

Build your own production-oriented AI application from ideation to deployment. Containerize the application, deploy it, and receive one-on-one review from industry professionals to strengthen your portfolio.