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Machine Learning & AI

Scikit-Learn.

Machine learning with Scikit-Learn — estimators, pipelines, cross-validation, feature engineering, and model selection.

4 Beginner 6 Intermediate 2 Advanced

Beginner

Start here — no prior experience needed

4 lessons
sklearn beginner
5 min read

Scikit-Learn Learning Roadmap

A structured path from sklearn beginner to ML practitioner — from your first classifier to building production-ready pipelines.

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sklearn beginner
5 min read

Scikit-Learn Feature Engineering

Transform raw features into model-ready inputs with scaling, encoding, imputation, and feature creation techniques.

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sklearn beginner
5 min read

Scikit-Learn Projects

End-to-end ML projects that take you from raw data to deployed models — covering classification, regression, clustering, and production pipelines.

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sklearn beginner
5 min read

Introduction to Scikit-Learn

Learn the Scikit-Learn API, understand the estimator interface, and train your first classification and regression models.

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Intermediate

For developers with core concepts down

6 lessons
sklearn intermediate
5 min read

Scikit-Learn Model Evaluation

Evaluate classifiers and regression models correctly using cross-validation, confusion matrices, ROC curves, and calibration.

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sklearn intermediate
5 min read

Scikit-Learn Pipelines

Chain preprocessing and model steps into reproducible, production-safe Pipelines that prevent data leakage.

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sklearn intermediate
5 min read

Scikit-Learn Classification

Train, compare, and tune classification algorithms — Logistic Regression, Random Forest, SVM, and Gradient Boosting — on real datasets.

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sklearn intermediate
5 min read

Scikit-Learn Hyperparameter Tuning

Find optimal model parameters with GridSearchCV, RandomizedSearchCV, and Bayesian optimization using Optuna.

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sklearn intermediate
5 min read

Scikit-Learn Regression

Train and evaluate regression models — Linear Regression, Ridge, Lasso, Elastic Net, and Gradient Boosting — on real datasets.

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sklearn intermediate
5 min read

Scikit-Learn Clustering

Group unlabeled data with K-Means, DBSCAN, and hierarchical clustering — and evaluate cluster quality without ground truth labels.

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Advanced

Production-grade patterns for experienced engineers

2 lessons
sklearn advanced
5 min read

XGBoost and LightGBM

Master gradient boosting with XGBoost and LightGBM — the dominant algorithms for tabular ML competitions and production systems.

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sklearn advanced
5 min read

Scikit-Learn Advanced Model Selection

Select models correctly — learning curves, validation curves, stratified splitting strategies, and avoiding selection bias.

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