Intermediate Machine Learning: Real-World Pipelines
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.
Curriculum
- 1
Pipeline Architecture Essentials
4 min - 2
ColumnTransformer for Heterogeneous Data
3 min - 3
Custom Transformers for Feature Engineering
3 min - 4
Handling Missing Values Strategically
4 min - 5
Scaling and Normalization Pipelines
3 min - 6
Encoding Categorical Variables
3 min - 7
Feature Selection in Pipelines
3 min - 8
Data Leakage Prevention Strategies
4 min - 9
Designing Reproducible Pipelines
3 min - 10
Project Initialization: Defining the Prediction Problem
3 min - 11
Introduction to Cross-Validation
3 min - 12
Stratification for Imbalanced Data
4 min - 13
Time-Series Validation Strategies
4 min - 14
Confusion Matrices and Beyond
4 min - 15
Precision-Recall Curves
4 min - 16
ROC-AUC Analysis
3 min - 17
Cost-Sensitive Learning
4 min - 18
Handling Class Imbalance with Resampling
3 min - 19
Advanced Metrics for Imbalanced Datasets
4 min - 20
Project Milestone: Building the Baseline Pipeline
3 min - 21
Introduction to GridSearchCV
3 min - 22
RandomizedSearchCV for Efficiency
3 min - 23
Bayesian Optimization Principles
3 min - 24
Early Stopping in Iterative Models
4 min - 25
Managing Computational Resources
3 min - 26
Hyperparameter Stability Analysis
4 min - 27
Pipeline Parameter Nesting
3 min - 28
Project Milestone: Tuning the Champion Model
3 min - 29
Baseline-to-Champion Framework
3 min - 30
Statistical Significance in Model Comparison
3 min - 31
Model Ensembling: Voting and Averaging
3 min - 32
Stacking Architectures
4 min - 33
Blending Techniques
4 min - 34
Interpreting Complex Ensembles
3 min - 35
Managing Model Complexity
3 min - 36
Bias-Variance Tradeoff in Ensembles
4 min - 37
Project Milestone: The Ensemble Strategy
3 min - 38
Serializing Pipelines with Joblib
4 min - 39
Versioning Models and Data
3 min - 40
Designing Inference APIs
3 min - 41
Input Validation and Schema Enforcement
4 min - 42
Monitoring Data Drift
4 min - 43
Tracking Performance Degradation
3 min - 44
Logging and Observability
4 min - 45
Automated Retraining Triggers
4 min - 46
Containerization Basics
4 min - 47
Handling Environment Parity
3 min - 48
Documentation for Production
4 min - 49
Project Milestone: Deployment Readiness
3 min