Machine Learning

Learning patterns from data — the foundation everything else sits on.

Machine learning is the broader field that deep learning is part of. Most working ML has nothing to do with neural networks: it's gradient-boosted trees on tabular data, quietly outperforming anything fancier and doing so in seconds.

That's worth stating plainly, because the assumption that deep learning is always better is expensive and wrong. On spreadsheet-shaped problems it usually loses, and there's a well-cited paper saying so.

This field covers the fundamentals that don't change: supervised and unsupervised learning, clustering, feature engineering — still where most of the accuracy comes from outside deep learning — and the two ideas that cause the most real-world failure: overfitting and getting your train/test split wrong.

Start with Machine Learning, then Overfitting — it explains more failures than anything else here.

6 concepts in this field

Supervised Learning

Teaching an AI by showing it labelled examples — inputs paired with the correct answers — so it can predict answers for new inputs.

Overfitting

When a model memorizes its training data instead of learning the general pattern — so it looks great in training but fails on new data.

Unsupervised Learning

Finding structure in data nobody labelled — useful, underrated, and much harder to know if you got right.

Clustering

Grouping things that resemble each other — and the fact that the algorithm always returns groups, whether or not any exist.

Feature Engineering

Reshaping raw data into things a model can actually use — still where most of the accuracy comes from outside deep learning.

Train/Test Split

Holding back data the model never sees, so you can find out whether it learned anything or just memorised.