Lilly Tech Systems
Scikit-learn Deep Dive
Master the most popular machine learning library in Python. From data preprocessing and model selection to production pipelines and advanced features — learn scikit-learn the right way.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Scikit-learn philosophy, the estimator API, and building your first ML model in minutes.
2. Preprocessing
Scaling, encoding, imputation, feature extraction, and data transformation techniques.
3. Model Selection
Cross-validation, hyperparameter tuning, grid search, and evaluation metrics.
4. Pipelines
Building reproducible ML workflows with Pipeline, ColumnTransformer, and FeatureUnion.
5. Advanced Features
Custom estimators, model persistence, multiclass strategies, and ensemble methods.
6. Best Practices
Production deployment, performance tuning, debugging, and common pitfalls to avoid.
What You'll Learn
By the end of this course, you'll be able to:
Master the Estimator API
Understand fit/predict/transform patterns and navigate scikit-learn's consistent interface.
Build Robust Pipelines
Create end-to-end ML workflows that handle preprocessing, training, and evaluation seamlessly.
Tune Hyperparameters
Use grid search, randomized search, and cross-validation to find optimal model configurations.
Deploy to Production
Serialize models, build custom estimators, and integrate scikit-learn into production systems.