AI & Machine Learning Essentials

Three Overlapping Circles Artificial intelligence is the broad goal of building machines that perform tasks that normally require human intelligence, such as understanding ...

The Learning Loop A machine learning project is an ordinary loop repeated until the model is good enough. You gather data, prepare it, train a model, measure how wrong it is, ...

The Two Main Families Almost every machine learning problem falls into one of two families, distinguished by whether the data has labels. Supervised learning trains on examples ...

Forecasting a Number Regression predicts a continuous number. Tomorrow temperature, tomorrow sales, the price of a flat, the demand for electricity at noon. The core idea is a ...

Predicting a Category Classification predicts which group a sample belongs to. Email spam or not, a loan approved or refused, an image showing a cat or a dog. Unlike regression, ...

Never Grade on the Training Data A model that memorises its training data achieves perfect scores at home and fails in the world. That is why evaluation happens on a test set, a ...

Inspired by the Brain, Built with Math A neural network is a chain of simple calculations stacked into layers. The smallest useful unit is the perceptron: it multiplies inputs by ...

Your First Real Model Scikit-learn is the friendliest path from idea to working model in Python. It ships with built-in datasets, ready-made algorithms, and tuning tools. This ...

Models Learn What the Data Contains Machine learning models do not invent reality; they learn the patterns inside the data they were trained on. If that data carries bias, the ...

Build the Foundations A little maths goes a long way. Linear algebra powers matrix operations, calculus explains gradients, and statistics drives evaluation and uncertainty. You ...