AI/ML Foundations

Start Simple - The Power of Linear Models

Linear regression from scratch to Scikit-learn pipeline. Build the baseline every production team starts with.

Linear models¹ power more production systems than most people realize: recommendation engines, fraud detection, pricing algorithms. The smartest ML teams start here, not because linear models are a stepping stone, but because they force you to understand your data and build something you can actually debug.

This tutorial takes the math you just learned (vectors, gradients, optimization) and turns it into a working prediction system.

Tutorial Goals

Build linear regression from scratch with NumPy
Understand why linear models dominate production systems
Apply feature engineering to extend linear models
Train and evaluate a housing price predictor with Scikit-learn
Develop judgment for when simple beats complex

Scikit-learn Linear Models Documentation ↩
Rules of Machine Learning: Best Practices for ML Engineering ↩
Statistical Modeling: The Two Cultures ↩
Scikit-learn OneHotEncoder ↩
California Housing Dataset ↩
Scikit-learn Pipeline ↩
Scikit-learn StandardScaler ↩
An Introduction to Statistical Learning ↩

AI/ML Foundations

Start Simple - The Power of Linear Models

Tutorial Goals

Why Start With Linear Models

References

Footnotes