Machine Learning

The math behind the algorithms separates someone who runs a model from someone who knows why it works or fails.

Before any algorithm, you must grasp the types of learning and generalisation — the mental frame for all that follows.

Clean, prepared data determines model quality — garbage in, garbage out is an unforgiving rule.

The real starting point: predicting a continuous value, the foundation behind all linear models.

The most common production ML task — from fraud detection to medical diagnosis.

Gradient-boosted models win most tabular-data competitions — an indispensable practical tool.

When data has no labels, clustering and dimensionality reduction reveal hidden structure.

Correct measurement and smart tuning separate a model that looks good from one that actually works in production.

For unstructured data neural networks dominate — and they underpin every modern large model.

Convolutional networks transformed image processing — they're the heart of modern vision applications.

Transformers are the architecture behind every large language model — understanding them is now essential.

A model that serves no users has no value — deployment and monitoring turn research into a product.

A biased or opaque model can harm real people — responsibility is part of the engineer's professionalism.