Machine Learning: Learn by Building

Course Philosophy

This course takes a hands-on approach to machine learning. You’ll:

  • Build real ML systems, not just study theory
  • Create an AI portfolio of mini research papers
  • Explain complex concepts simply (teaching = understanding)
  • Work with cutting-edge tools while mastering fundamentals

We believe the best way to understand machine learning is to build it, break it, and rebuild it better.

Our practical, project-first ethos is inspired by the excellent work from fast.ai.

Course Philosophy: Inspired by fast.ai, our motto is “build first, understand later.” You’ll start by building and fine-tuning powerful models, and then we’ll dive deep into the theory to understand why they work.

A Note on AI in Course Development: In the spirit of transparency, I want to acknowledge that AI-powered tools were used to help develop these course materials, from refining explanations to generating boilerplate code. This is to model the professional practice of using the best tools available to produce a high-quality result. Your goal in this course is different: it’s to learn the fundamental process yourself. Our course policies will reflect this important distinction.

Getting Started

  1. Set up your environment → Setup Guide
  2. Review the syllabus → Course Policies
  3. Check the schedule → Week-by-Week

Course Team

Instructor: Ming Jin
Email: jinming@vt.edu