Learn machine learning fundamentals and practical algorithms from scratch. The tutorials below are numbered in the recommended order of learning, starting from the basics and progressing to advanced ensemble methods, clustering, and other algorithm families.

Recommended Learning Path:

1. 01. Machine Learning - Introduction — What machine learning is and the main types of problems it solves.
2. 02. ML in Plain English - A Friendly Tour — A non-technical, story-style tour of every technique before diving into the details.
3. Linear Regression, from first principles to advanced diagnostics:
   • 03. Linear Regression - Introduction
   • 04. Linear Regression - Advanced Topics
   • 05. Linear Regression - Expert Topics
4. 06. Logistic Regression - Introduction — Classification, sigmoid, decision boundaries, and ROC-AUC.
5. 07. Regularization - Complete Guide — Ridge, Lasso, and Elastic Net to prevent overfitting.
6. Decision Trees, from splitting criteria to a full worked demo:
   • 08. Decision Trees - Part 1 - Foundations
   • 09. Decision Trees - Part 2 - Measures of Impurity
   • 10. Decision Trees - Part 3 - Training and Demonstration
7. Ensembles, combining many models into one:
   • 11. Ensembles - Part 1 - Foundations of Ensemble Learning
   • 12. Ensembles - Part 2 - Bagging and Random Forests
   • 13. Ensembles - Part 3 - Boosting and Modern Methods
   • 14. Ensembles - Part 4 - Voting Classifier Example
8. 14. Similarity and Distance Metrics — Euclidean, cosine, Pearson, and Jaccard, the building blocks behind the clustering and neighbour methods below.
9. 15. K-Means Clustering — Elbow method, WCSS, and centroids.
10. 16. Hierarchical Clustering — Linkage methods and dendrograms.
11. 17. K-Nearest Neighbours — Classification and regression by proximity.
12. 18. Naive Bayes - A Friendly Guide — A single-page overview of probabilistic classification (see the full multi-part series under Advanced Machine Learning for the deep dive).
13. 19. Support Vector Machines - A Friendly Guide — A single-page overview of SVM (see the full multi-part series under Advanced Machine Learning for the deep dive).
14. 20. PCA and Dimensionality Reduction - A Friendly Guide — Reducing feature count while keeping the signal.
15. 21. Semi-Supervised Learning - A Friendly Guide — Learning from a small labelled set plus a large unlabelled one.
16. 22. Self-Supervised Learning - A Friendly Guide — Learning useful representations without any labels at all.
17. 23. Reinforcement Learning - A Friendly Guide — Learning by trial, error, and reward.
18. 24. Gaussian Mixture Models - A Friendly Guide — Soft clustering with overlapping probability distributions.
19. 25. Association Rules and Apriori - A Friendly Guide — Market-basket analysis and “customers who bought this also bought.”
20. 26. Anomaly Detection - A Friendly Guide — Spotting the data points that don’t belong.
21. 27. ML Quick Reference - Libraries, Metrics and Cheat Sheet — Every library, algorithm, and metric used in this handbook, on one page.
22. 34. Machine Learning with NLP - A Friendly Guide — How machine learning transforms text into predictions (sentiment analysis, classification, NER). See the full Natural Language Processing category for the complete series this builds on.

Beginner-friendly companions:

• 28. ML Glossary - Plain English Dictionary — A-Z dictionary of every term.
• 29. ML Common Pitfalls and Debugging Guide — Symptom, cause, and fix, looked up fast.
• 30. ML FAQ - What is the Difference Between… — “What’s the difference between X and Y” answers.
• 31. ML Algorithm Intuition Flashcards — One card per algorithm, for quick review.
• 32. ML Visual Algorithm Comparison — Every classifier compared on the same dataset.
• 33. ML End-to-End Worked Project - Titanic — A full ML pipeline, from raw CSV to evaluation.

New to ML? Start with #02 for a non-technical, story-style tour of every technique before diving into the details.

Need a quick lookup? Jump to #27 for the one-page reference covering every library, algorithm, and metric used in this handbook.

What You’ll Learn:

• Supervised, unsupervised, and reinforcement learning
• Linear regression for predicting continuous values
• Logistic regression for classification (sigmoid, decision boundaries, ROC-AUC)
• Regularization (Ridge, Lasso, Elastic Net) to prevent overfitting
• Decision trees (Gini, entropy, information gain, overfitting, pruning)
• Ensembles (bagging, Random Forest, AdaBoost, Gradient Boosting, XGBoost)
• Distance and similarity metrics (Euclidean, cosine, Pearson, Jaccard)
• K-Means clustering (elbow method, WCSS, centroids)
• Hierarchical clustering (linkage methods, dendrograms)
• K-Nearest Neighbours (KNN) for classification and regression
• Hyperparameter tuning with grid search and cross validation
• All assumptions, challenges, and how to fix them
• Categorical variable encoding (One-Hot, Ordinal, Target, Binary)
• Multicollinearity detection and solutions (VIF, PCA)
• Outliers, leverage, and influential points (Cook’s Distance)
• Polynomial regression and log transformations
• Cross-validation and feature selection
• Classification metrics (accuracy, precision, recall, F1, ROC-AUC)
• How to evaluate and improve ML models