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Machine Learning Tutorial
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K-Nearest Neighbors (KNN) Classification with scikit-learn
This article covers how and when to use k-nearest neighbors classification with scikit-learn. Focusing on concepts, workflow, and examples. We also cover distance metrics and how to select the best value for k using cross-validation.
Adam Shafi
February 20, 2023
Decision Tree Classification in Python Tutorial
In this tutorial, learn Decision Tree Classification, attribute selection measures, and how to build and optimize Decision Tree Classifier using Python Scikit-learn package.
Avinash Navlani
January 16, 2026
A Complete Guide to Data Augmentation
Learn about data augmentation techniques, applications, and tools with a TensorFlow and Keras tutorial.
Abid Ali Awan
March 3, 2026
All posts
Hyperparameter Tuning: Methods, Best Practices, and Examples
A practical guide to hyperparameter tuning, covering how it works, the main search methods (grid, random, Bayesian), evaluation strategies, and how to run it in Python and R.
Dario Radečić
July 28, 2026
Random Forest Regression: A Complete Guide
How random forest regression works, where it fails, and how to evaluate, tune, and interpret it. Includes a Python implementation and model comparison framework.
Srujana Maddula
June 17, 2026
Zero-Shot Classification: How It Works and When to Use It
Learn what zero-shot classification is, how it works under the hood with NLI models, how it compares to few-shot and fine-tuning, and how to apply it with Hugging Face Transformers.
Dario Radečić
June 11, 2026
FIFA World Cup 2026 Winner Prediction: An MLOps Guide
See how an end-to-end MLOps pipeline predicts World Cup 2026 results, from automated retraining and DVC to a 10,000-run Monte Carlo simulation of the bracket.
Tom Farnschläder
June 10, 2026
Contrastive Learning: How Models Learn by Comparison
A practical overview of contrastive learning - how models learn by comparing similar and dissimilar examples, the loss functions and methods behind it, and how to implement it in PyTorch.
Dario Radečić
May 4, 2026
Kernel Trick Explained: How SVMs Learn Nonlinear Patterns
A conceptual guide to the kernel trick - what it is, how it enables SVMs and other kernel-based models, and when to use it over other approaches to nonlinear modeling.
Dario Radečić
May 4, 2026
Regularization in Machine Learning: L1, L2, and Elastic Net Explained
A practical overview of regularization in machine learning - what it is, how it works, and when to use L1, L2, and Elastic Net to build models that generalize.
Dario Radečić
April 13, 2026
LSTM Models: A Complete Guide to Long Short-Term Memory Networks
Master the inner workings of LSTM networks, the foundation for modern LLMs. Explore gating mechanisms, gradients, and build a sentiment classifier with PyTorch.
Bex Tuychiev
February 11, 2026
How to Normalize Data: A Complete Guide With Examples
Stop vanishing gradients and biased models. Learn how to normalize data using min-max and z-score in Scikit-learn to improve machine learning models.
Josep Ferrer
January 27, 2026
Precision vs Recall: The Essential Guide for Machine Learning
Accuracy isn't enough. Learn the difference between precision and recall, understand the trade-off, and choose the right metric for your model.
Mark Pedigo
January 8, 2026
Cost Functions: A Complete Guide
Learn what cost functions are, and how and when to use them. Includes practical examples.
Mark Pedigo
December 18, 2025
F1 Score in Machine Learning: A Balanced Metric for Precision and Recall
Understand how the F1 score evaluates model performance by combining precision and recall. Learn its use in binary and multiclass classification, with Python examples.
Vidhi Chugh
November 12, 2025