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Machine Learning tutorials
33 articles · page 2 of 2Hand-written tutorials, ordered as a recommended learning path.
- 21 Confusion Matrix A thorough look at the confusion matrix: how to read it, the metrics it produces, and how to use it to diagnose classifier behavior beyond a single accuracy number that often hides what is going wrong.
- 22 Trees and Forests How decision trees work, why a single tree overfits, and how random forests solve that problem by averaging many trees trained on different data.
- 23 Hyperparam Tuning A practical comparison of hyperparameter tuning strategies including grid search, random search, Bayesian optimization, and Hyperband, with guidance on when to use each.
- 24 K-Means vs DBSCAN Compare the two most popular clustering algorithms in practice: how K-Means partitions by centroids while DBSCAN finds density-based clusters, and when each one is the right tool for your data.
- 25 KNN Algorithm Understand how the k-nearest neighbors algorithm classifies and regresses by looking at similar examples, when it works well, and how to tune k and distance metrics for real problems.
- 26 Naive Bayes A practical walkthrough of the Naive Bayes classifier: how it uses probability and a strong independence assumption to build a fast, surprisingly accurate baseline for text and tabular data.
- 27 Regularization See how models overfit, why it happens, and how L1, L2, dropout, and early stopping fight it without crippling capacity.
- 28 PCA Learn how Principal Component Analysis compresses high-dimensional data into a handful of informative axes, the math intuition behind it, and how to apply it without losing the signal that matters.
- 29 P/R/F1 Decode precision, recall, F1, and accuracy with concrete intuition, threshold tuning, and PR vs ROC curve guidance for imbalanced data.
- 30 SVMs An intuitive walkthrough of support vector machines, the kernel trick, and when SVMs still make sense in a world dominated by gradient boosted trees and neural networks.
- 31 Time Series Intro A practical introduction to time series forecasting: the unique properties of temporal data, classical and modern modeling approaches, and how to evaluate forecasts honestly without leaking the future.
- 32 Train/Val/Test Understand why machine learning data is split into three sets, how to choose proportions, and how to avoid leakage that silently inflates scores.
- 33 Logistic Regression Learn how logistic regression turns a linear score into a probability, how to train it with scikit-learn, and how to evaluate binary classifiers using ROC-AUC.