AI & Machine Learning · Course
Intermediate Machine Learning
Take your models further. Handle missing values and non-numeric data, bundle your steps into clean pipelines, test models honestly with cross-validation, boost accuracy with gradient boosting, and avoid the subtle trap of data leakage. Hands-on in your browser with real scikit-learn. Builds on Intro to Machine Learning and Pandas.
7 lessonsSelf-pacedintermediateNo video neededFree · Certificate
What you’ll learn
- Handle missing values by dropping or imputing them
- Turn categorical (non-numeric) data into something a model can use
- Bundle preprocessing and modelling into a single clean pipeline
- Test models more reliably with cross-validation
- Boost accuracy with gradient boosting (and understand XGBoost)
- Spot and prevent data leakage that secretly ruins a model
Lessons
Getting Started
Handling Messy Data
Better Workflow
Stronger Models
Avoiding Pitfalls
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Certificate of Completion
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