Over 6 weeks, I will teach the essentials of AI through practical examples, from simple API calls to fine-tuning LLMs for unique use cases. Each weekly session will consist of hands-on examples (with code) that you can adapt to your projects and use cases.

📖 Course Overview: Acquiring a practical understanding of AI through building projects, lectures will center around specific use cases that students can leverage in developing their projects. Students learn the basics of AI, machine learning, LLMs, prompt engineering, RAG, agentic systems, fine-tuning, and AI project management.

🎯 Learning Objectives

1. Define key terms and concepts associated with AI

2. Effectively use AI tools to accelerate existing workflows

3. Transform project ideas into valuable prototypes and MVPs with Python

4. Develop custom AI systems using OpenAI and Hugging Face APIs

5. Identify which AI techniques are best suited to a particular problem

✅ Who should take this course?

This course is designed for people who want to develop practical AI skills. While this can include many backgrounds, it is highly recommended that you have some coding experience (ideally Python).

• Founders building AI products (or just trying to use them effectively)

• Freelancers selling data/ML services and want to add LLM-based solutions

• Business Leaders and Product Managers leading AI initiatives and teams

• Data scientists and ML Engineers new to LLM development and GenAI**

❌ Who should NOT take this course?

• People seeking mathematical and algorithmic details behind AI development

• People who want to learn LLMOps

🧑‍💻 10+ example projects built in past cohorts...

• Automated report builder and emailer

• AI Job Board Scraper

• Interactive AI Job Dashboard

• ML-based email classifier

• Research paper summarizer

• LLM-based text classifier

• (Local) Document QA Chatbot

• Structuring unstructured survey data

• Semantic search over Blog Posts

• RAG Chatbot with LLM Blog series

• Fine-tuning LLM response style

• Fine-tuning BERT for text classification