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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
Causal Machine Learning: From Prediction to Cause and Effect
Causal machine learning combines ML with causal inference to move beyond prediction and estimate what happens when you intervene. This article covers the core concepts, methods, Python tools, and common mistakes.
September 14, 2026
Self-Supervised Learning: How Models Learn Without Labeled Data
Self-supervised learning trains models to pull their own supervision from unlabeled text, images, audio, and video, using techniques like contrastive learning, masked modeling, and autoregressive prediction to build the representations behind today's language, vision, and multimodal models.
September 9, 2026
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
January 8, 2026