Week 4 · 45-60 min
Data for Machine Learning
Week Summary
A quick preview of the main ideas in this week before you begin the slides.
- Recap: Data Quality Matters
- Structuring Data for ML
- Choosing Good Features
- Handling Missing or Messy Data
Glossary
Review these key terms before starting this week. They are kept simple so students can learn the new vocabulary before moving into the slides.
- Recap: Data Quality Matters
- Recap: Data Quality Matters
- Structuring Data for ML
- Structuring Data for ML
- Choosing Good Features
- Choosing Good Features
Recap: Data Quality Matters
Progress
1 / 8
Understand how data is prepared and structured for ML models.
Read one card at a time. Focus on the main idea first, then look at the visual on the right to help it stick.
ML models depend heavily on data quality.
Incomplete or biased data weakens predictions.
Preparation often takes most of the time in real ML work.
Module 3 cleaning habits apply directly here.
Trustworthy models begin with trustworthy data.
Remember
Good ML depends on careful data preparation, not just model choice.
Recap: Data Quality Matters
Slide 1 of 8
Use arrows or dots to move through the lesson
Practice
- Write 2-3 sentences about what you learned this week.
Coming Next Week: Next: Getting Started with scikit-learn