Machine Learning.
Train and validate ML models using Python, Scikit-learn, PyTorch, and implement MLOps deployment cycles.
Beginner
Start here — no prior experience needed
Machine Learning Learning Roadmap
A structured path through ML fundamentals — from understanding algorithms to building production-grade models with interpretability and fairness.
Introduction to Machine Learning Models
Build, train, and evaluate your first classification model using Scikit-Learn.
Machine Learning Projects
Projects that build end-to-end ML intuition — from understanding algorithms to deploying production-grade systems.
Introduction to Machine Learning
Understand the core concepts of machine learning — supervised vs unsupervised learning, the bias-variance tradeoff, and the ML workflow.
Intermediate
For developers with core concepts down
Supervised Learning Algorithms
Understand and apply the core supervised learning algorithms — when to use each, how they work, and how to tune them.
Machine Learning Cross-Validation
Understand and implement k-fold, stratified, time-series, and nested cross-validation for reliable model evaluation.
Unsupervised Learning
Discover hidden structure in unlabeled data — dimensionality reduction with PCA/UMAP, anomaly detection, and topic modeling.
Feature Engineering Pipelines
Build robust, production-ready feature engineering pipelines that prevent data leakage, handle edge cases, and plug into scikit-learn.
Handling Imbalanced Data
Practical techniques for class imbalance — resampling, cost-sensitive learning, threshold tuning, and proper evaluation.
Advanced
Production-grade patterns for experienced engineers
Ensemble Methods
Combine multiple models to outperform any individual model — voting, bagging, boosting, and stacking ensembles.
Model Interpretability
Explain model predictions with SHAP values, LIME, partial dependence plots, and permutation importance.