Machine Learning Foundations: Models That Learn
Understand how machines learn — from data to predictions
By Dr. Amara Okafor
About this course
A practical, beginner-friendly introduction to machine learning. Learn how ML models work, the difference between supervised, unsupervised, and reinforcement learning, and how to train, evaluate, and deploy simple models — all without heavy math. By the end, you'll build and test your first ML model on real data.
What you'll learn
Income Potential
Turn your new skills into real earnings
$90-220/hr
Career paths you can pursue:
Course content
5 lessonsWhat Is Machine Learning? The Big Picture
A clear, jargon-free introduction to what machine learning is and why it matters.
Supervised Learning: Learning from Examples
Explore the most common ML approach — learning from labeled data.
Unsupervised Learning & Reinforcement Learning
Discover how machines find patterns without labels and learn by trial and error.
Training, Testing, and Evaluating Models
Learn how to build reliable models and avoid the #1 ML mistake: overfitting.
Deploying Your First ML Model
Take a model from notebook to production — and keep it working.
Student reviews
Switched careers from teaching to ML engineering
I was a high school math teacher wanting to break into tech. The ML Foundations course made the concepts click without drowning me in math. I built my first model in week two, and three months later I landed an ML engineer role.
Diego Ramirez
ML Engineer at fintech startup
Your instructor
Dr. Amara Okafor
ML Researcher & Data Scientist
Prerequisites
- Basic comfort with data and spreadsheets
- No programming experience required