Series
ml basics
8 parts, in reading order. Each one builds on the last.
- 01
What machine learning is: rules learned from examples
SEP 1 2026 · 5 min
Machine learning is teaching a computer by example instead of by instruction: you show it many past cases with the right answers, it works out the pattern on…
- 02
Regression vs classification: predicting a number or a label
SEP 8 2026 · 7 min
Most machine learning answers one of two questions: "how much?" (regression, the answer is a number) or "which one?" (classification, the answer is a category).
- 03
Linear regression: the first model, a straight line
SEP 14 2026 · 10 min
Linear regression draws the straight line that best fits your data, where "best" means the total of the squared misses is as small as possible; each number it…
- 04
Decision trees: a model made of yes/no questions
SEP 22 2026 · 8 min
A decision tree makes a prediction by asking a short series of yes/no questions about the input, like a flowchart an expert would draw; it learns which…
- 05
Bias and variance: where a model's errors come from
SEP 30 2026 · 7 min
A model's error on new data comes from three places: bias (it is too simple to capture the real pattern), variance (it changes too much depending on which data…
- 06
Bagging: many copies of a model, averaged
OCT 6 2026 · 7 min
Bagging trains many copies of the same model, each on a slightly different random resample of the data, and averages their answers; the copies make different…
- 07
Random forests: bagging with one extra rule
OCT 12 2026 · 8 min
A random forest is bagging with one extra rule: at every split, each tree may only choose from a random handful of the features.
- 08
Boosting: models that learn from the last one's mistakes
OCT 14 2026 · 10 min
Boosting builds a strong model from many weak ones trained in sequence, each focusing on what the previous ones got wrong.