Build robust ML pipelines with feature engineering, tuning, and evaluation.
What you'll learn
Engineer features, build reproducible pipelines, tune hyperparameters, handle imbalance and validation correctly, and compare models rigorously.
Who it's for
Practitioners who know ML basics.
Prerequisites
Python, pandas, and basic scikit-learn modeling.
Robust, real-world machine-learning pipelines from data to evaluated models.
Handling Class Imbalance with Resampling
Advanced Metrics for Imbalanced Datasets
Project Milestone: Building the Baseline Pipeline
Introduction to GridSearchCV
RandomizedSearchCV for Efficiency
Bayesian Optimization Principles
Early Stopping in Iterative Models
Managing Computational Resources
Hyperparameter Stability Analysis
Pipeline Parameter Nesting
Project Milestone: Tuning the Champion Model
Baseline-to-Champion Framework
Statistical Significance in Model Comparison
Model Ensembling: Voting and Averaging
Stacking Architectures
Blending Techniques
Interpreting Complex Ensembles
Managing Model Complexity
Bias-Variance Tradeoff in Ensembles
Project Milestone: The Ensemble Strategy
Serializing Pipelines with Joblib
Versioning Models and Data
Designing Inference APIs
Input Validation and Schema Enforcement
Monitoring Data Drift
Tracking Performance Degradation
Logging and Observability
Automated Retraining Triggers
Containerization Basics
Handling Environment Parity
Documentation for Production
Project Milestone: Deployment Readiness