Ml Fundamentals

Machine learning is teaching computers to find patterns in data instead of writing rules by hand.

Linear regression draws the best straight line through your data. It is the "hello world" of machine learning.

Logistic regression bends a straight line into an S-curve to answer yes-or-no questions with probabilities.

A decision tree is just a flowchart. But a forest of them is one of the most powerful tools in ML.

Find the widest street between two classes. That is the entire idea.

Store everything. Predict by looking at your neighbors. The simplest algorithm that actually works.

No labels, no teacher. The algorithm finds structure on its own.

A good feature is worth a thousand data points.

A model is only as good as the way you measure it.

Every model error comes from one of three sources: bias, variance, or noise. You can only control the first two.

A group of weak learners, combined correctly, becomes a strong learner. This is not a metaphor. It is a theorem.

Hyperparameters are the knobs you turn before training starts. Turning them well is the difference between a mediocre model and a great one.

A model is not a product. A pipeline is. The pipeline is everything from raw data to deployed prediction, and every step must be reproducible.

The "naive" assumption is wrong, and it works anyway. That's the beauty of it.

Past performance does predict future results -- if you check for stationarity first.

Normal is easy to define. Abnormal is whatever doesn't fit.

When 99% of your data is "normal," accuracy is a lie.

More features is not better. The right features is better.