Machine LearningBeginner
Machine Learning Foundations
The core ideas behind models that learn from data.
Taught by Bibek Sharma · Data Scientist
What you'll learn
- Tell supervised from unsupervised learning
- Explain why we split data into train/validation/test
- Read a loss curve and spot over/underfitting
- Describe linear models and decision trees
- Avoid the most common data-leakage mistakes
About this course
Before frameworks and buzzwords, machine learning is a few powerful ideas: learn a pattern from examples, measure how wrong you are, and improve. This free course builds that intuition in plain language, with just enough maths to be dangerous.
Course content
5 lessons- 1Learning from examplesWhat 'learning' means for a machine.Preview
- 2Why we split the dataTrain, validation, and test sets.Preview
- 3Loss, overfitting, underfittingReading the story a loss curve tells.
- 4Two models you should knowLinear models and decision trees.
- 5Mistakes that quietly ruin modelsData leakage and friends.