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. Design and implement basic machine learning systems 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 learners 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

• Software Engineers building with AI systems

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

• Business Leaders and Product Managers leading AI initiatives and teams

• Entrepreneurs building AI-native products and services

❌ Who should NOT take this course?

Those who have less than 1 year of coding experience.

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

• Automated analytics report emailer

• Gmail ETL data pipeline

• Machine learning email classifier

• PDF parser and summarizer

• Document Question Answering chatbot

• Structuring unstructured survey data

• Semantic search over YouTube videos

• Multimodal Desktop RAG system

• Fine-tuning LLM response style

• Fine-tuning LLM for tool use