Over 6 weeks, I will teach the essentials of AI through practical examples, from simple report automation 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, data engineering, machine learning, generative AI, prompt engineering, RAG, 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 at the start of their AI engineering journey. While this can include many backgrounds, basic coding skills (>1 year, preferably Python) are highly recommended.

• IT Professionals seeking hands-on experience with AI

• Researchers looking to use AI more effectively

• Software Engineers building with AI systems

• Business Leaders and Product Managers leading AI initiatives and teams

• Entrepreneurs building AI-native products and services

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

**Sessions 1 and 2 will serve as a review for those with a solid grasp of ML and data science concepts.

❌ Who should NOT take this course?

• People seeking mathematical and algorithmic details behind AI development.

• People who want to learn LLMOps

🧑‍💻 10 example projects we will build in this course...

• Automated analytics report emailer

• Gmail ETL data pipeline

• Machine learning 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