Skip to main content
Machine Learning & AI

Machine Learning Tutorial with Examples.

Machine learning tutorial with Python: train and validate models with Scikit-learn and PyTorch, then take them through MLOps deployment cycles.

4 Beginner 5 Intermediate 2 Advanced +6 From other topics

Beginner

Start here — no prior experience needed

4 lessons
ml beginner
5 min read

Machine Learning Learning Roadmap

A structured path through ML fundamentals — from understanding algorithms to building production-grade models with interpretability and fairness.

Read Tutorial
ml beginner
5 min read

Introduction to Machine Learning

Understand the core concepts of machine learning — supervised vs unsupervised learning, the bias-variance tradeoff, and the ML workflow.

Read Tutorial
ml beginner
5 min read

Introduction to Machine Learning Models

Build, train, and evaluate your first classification model using Scikit-Learn.

Read Tutorial
ml beginner
5 min read

Machine Learning Projects

Projects that build end-to-end ML intuition — from understanding algorithms to deploying production-grade systems.

Read Tutorial

Intermediate

For developers with core concepts down

5 lessons
ml intermediate
5 min read

Supervised Learning Algorithms

Understand and apply the core supervised learning algorithms — when to use each, how they work, and how to tune them.

Read Tutorial
ml intermediate
5 min read

Machine Learning Cross-Validation

Understand and implement k-fold, stratified, time-series, and nested cross-validation for reliable model evaluation.

Read Tutorial
ml intermediate
5 min read

Unsupervised Learning

Discover hidden structure in unlabeled data — dimensionality reduction with PCA/UMAP, anomaly detection, and topic modeling.

Read Tutorial
ml intermediate
5 min read

Feature Engineering Pipelines

Build robust, production-ready feature engineering pipelines that prevent data leakage, handle edge cases, and plug into scikit-learn.

Read Tutorial
ml intermediate
5 min read

Handling Imbalanced Data

Practical techniques for class imbalance — resampling, cost-sensitive learning, threshold tuning, and proper evaluation.

Read Tutorial

Advanced

Production-grade patterns for experienced engineers

2 lessons
ml advanced
5 min read

Ensemble Methods

Combine multiple models to outperform any individual model — voting, bagging, boosting, and stacking ensembles.

Read Tutorial
ml advanced
5 min read

Model Interpretability

Explain model predictions with SHAP values, LIME, partial dependence plots, and permutation importance.

Read Tutorial

Also part of Machine Learning

Taught in other topics. Each link opens the lesson in its home topic.

6 lessons
sklearn intermediate
5 min read

Scikit-Learn Pipelines

Chain preprocessing and models into scikit-learn Pipelines and ColumnTransformers, with a real run showing how preprocessing outside one leaks data.

Read Tutorial
sklearn intermediate
5 min read

Scikit-Learn Model Evaluation

Evaluate scikit-learn models with cross-validation, precision and recall, ROC curves, regression metrics and calibration, with real output for each.

Read Tutorial
sklearn intermediate
5 min read

Scikit-Learn Hyperparameter Tuning

Tune models with GridSearchCV, RandomizedSearchCV, successive halving and Optuna, and measure how optimistic a tuned model's best CV score really is.

Read Tutorial
pytorch beginner
5 min read

PyTorch Neural Networks with nn.Module

Build networks with nn.Module and nn.Sequential, pick the right loss and optimizer, write the standard training loop, and save and restore checkpoints.

Read Tutorial
tensorflow beginner
5 min read

Introduction to TensorFlow and Keras

Learn TensorFlow tensors, GradientTape, and Keras 3 through runnable examples: train an MNIST classifier, a functional model, and a custom training loop.

Read Tutorial
mlops intermediate
5 min read

Model Serving and Deployment

Serve a model with FastAPI and pydantic, load it once from an MLflow alias, batch-score Parquet in chunks, and gate promotion and rollback on a holdout.

Read Tutorial