# Descending into ML

Linear regression is a method for finding the straight line or hyperplane that best fits a set of points. This module explores linear regression intuitively before laying the groundwork for a machine learning approach to linear regression.

# Descending into ML

• There are lots of complex ways to learn from data
• Starting simple will open the door to some broadly useful methods L2 Loss for a given example is also called squared error

= Square of the difference between prediction and label

= (observation - prediction)2

= (y - y')2 $$L_2Loss = \sum_{(x,y)\in D} (y - prediction(x))^2$$

$$\sum \text{:We're summing over all examples in the training set.}$$ $$D \text{: Sometimes useful to average over all examples,}$$ $$\text{so divide by} {\|D\|}.$$

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]