Machine LearningIntermediate
Hands-On Machine Learning with Python
Train, evaluate and ship real models with scikit-learn.
Taught by Bibek Sharma · Data Scientist
What you'll learn
- Set up a reproducible ML workflow in Python
- Build preprocessing + model pipelines
- Choose the right metric for the problem
- Tune hyperparameters with cross-validation
- Diagnose and fix over/underfitting
- Save and serve a trained model
About this course
Turn ML theory into working code. Using Python and scikit-learn, you'll load data, build a clean train/test pipeline, train and compare models, evaluate them honestly, and avoid the traps that break models in production.
Course content
6 lessons- 1Your ML toolboxThe Python stack you'll use.Preview
- 2Pipelines that prevent leakagePreprocess the right way.
- 3Choosing the right metricAccuracy isn't always the answer.
- 4Cross-validation & tuningReliable scores and better settings.
- 5Diagnose and improveRead the errors, then act.
- 6Save and serve your modelFrom notebook to usable artefact.