Machine Learning Basics

A gentle, no-math introduction to how machines learn from examples: your warm-up before deep learning.

Beginner · ~300 min · Pass threshold 70%

Free

Before the neural networks and the transformers, there is one big idea: machines can learn from examples instead of being told every rule. This gentle, no-math course is that idea, explained from zero.

You will not write code or do any math. You will build clear intuition for what machine learning actually is, so that terms like "training data," "model," and "overfitting" stop being mysterious and start making sense.

What you will understand by the end

Why we let machines learn from examples instead of writing every rule by hand
What data, features, and labels are, and why good data matters most
How a model trains, and how we honestly test whether it learned
The main flavors of machine learning, in plain language
A first, gentle look at the neuron behind neural networks

Who this is for

Complete beginners. If you have ever wondered how a spam filter, a photo tagger, or a recommendation feels like it "just knows," this course is your starting point. It is also the recommended warm-up before the Deep Learning Specialization: finish here and the deep end will feel shallow.

Course sections

Section 1: Module 1: Learning From Examples Section 2: Checkpoint: Learning From Examples Quiz · 4 Q Section 3: Module 2: Data, Features, and Labels Section 4: Checkpoint: Data, Features, and Labels Quiz · 4 Q Section 5: Module 3: How a Model Learns and How We Test It Section 6: Checkpoint: Learning and Testing Quiz · 4 Q Section 7: Module 4: The Flavors of Machine Learning Section 8: Checkpoint: The Flavors of ML Quiz · 4 Q Section 9: Module 5: From Simple Models to Neural Networks Section 10: Checkpoint: Toward Neural Networks Quiz · 4 Q Section 11: Module 6: Your Path Into Deep Learning Section 12: Checkpoint: Your Path Forward Quiz · 4 Q