AI Learning & Hands-on Lab
Understand modern AI by building with it, safely and practically.
- Who it's for
- Students and professionals with basic programming skills
- Format
- Lecture + guided lab notebooks + project
- Duration
- 8 weeks
Overview
A hands-on introduction to machine learning and applied AI. You build models, work with data, and ship a small AI-powered application — while learning how to evaluate systems, manage cost and latency, and handle privacy and safety concerns responsibly.
What you'll be able to do
- Explain how supervised learning, embeddings, and transformers work
- Train, evaluate, and iterate on a model with a real dataset
- Build a retrieval-augmented application on top of an LLM API
- Design evaluations and catch regressions before shipping
- Reason about cost, latency, prompt injection, and data privacy
Roles this prepares you for
- Software engineer building AI features
- ML-adjacent analyst
- Foundation for further ML study
Syllabus
- 01
ML foundations
Data splits, features, loss, overfitting, and honest evaluation.
- 02
Working with models
Classic ML with scikit-learn, then neural networks and embeddings.
- 03
LLMs in practice
Prompting, structured output, tool use, and retrieval-augmented generation.
- 04
Evaluation & safety
Building eval sets, measuring quality, prompt injection, and PII handling.
- 05
Lab project
Ship an AI feature end to end: data in, evaluated output, deployed demo.
Frequently asked
Do I need math beyond high school?+
Comfort with functions and basic statistics is enough. We introduce the rest as needed.
Which tools and languages are used?+
Python, Jupyter notebooks, scikit-learn, PyTorch basics, and a hosted LLM API.
Ready to get started?
Book a free intro call or send us a message with your questions.