// ◇ Classical Machine Learning
Linear Regression
Fits a line (hyperplane) between inputs and a numeric output.
线性回归 Beginner core
IN PLAIN WORDS
Each feature gets a weight and the weighted sum is the prediction; training finds weights minimizing squared error. It is the best entry point to loss functions and gradient descent.
Used in: Sales forecasting, price estimation, A/B analysis
TIMELINE · prerequisites → this → what builds on it
01Original paper
! No single verifiable original paper
02Where it sits
Learn the left column first; the right column builds on this one.
Learn first
- No prerequisites — a good starting point
Linear Regression
Left: prerequisites · right: builds on this · dashed: related. Full map →
03Learning resources
Official docs, explainers, courses and reference code. All links checked.
04Where the course teaches it
On learning paths:Zero to Deep Learning
For AI / data
- Verification: partial, checked 2026-10-21.
- JSON: /api/learn/algorithms.json (id:
linear-regression)