Machine Learning Tutorial

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.

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.

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.

How random forest regression works, where it fails, and how to evaluate, tune, and interpret it. Includes a Python implementation and model comparison framework.

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.

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.

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.

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.

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.

Master the inner workings of LSTM networks, the foundation for modern LLMs. Explore gating mechanisms, gradients, and build a sentiment classifier with PyTorch.

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.

Accuracy isn't enough. Learn the difference between precision and recall, understand the trade-off, and choose the right metric for your model.