Machine learning terms, cheat sheets

docs/Public/MOC/Google Sheets cheat sheet.md

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+Tool finder: `opt` + `\`
+
+Move to beginning / end of row: `fn` + `left_arrow / right_arrow`
+	- Of sheet: `fn` + `opt` + `left_arrow / right_arrow
+
+Switch sheets: `opt` + `up_arrow / down_arrow`

docs/Public/MOC/cheat sheets.md

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 - [[GitHub cheat sheet]]
 - [[circuit cheat sheet]]
 - [[Notion cheat sheet]]
+- [[Google Sheets cheat sheet]]
 
 Syntax:
 - [[LaTeX cheat sheet]]

docs/Public/Software/AI/dataset.md

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+A *dataset* ([[set]] of [[data]]) is important for building [[machine learning]] models. 
+
+- Completeness: contains all relevant features or observations necessary for a given task
+- Size: large enough set for patterns to be relevant
+- Validity: contains accurate labels

docs/Public/Software/AI/dimensional data.md

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+*Dimensional data* refers to to a [[dataset]] composed of individual, non-overlapping data elements.
+
+The purpose of having *dimensional data* is for filtering, grouping, and labeling. 
+
+We want to reduce *high-dimensional data* into something smaller to reduce the complexity of computations. 

docs/Public/Software/AI/feature engineering.md

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+*Feature engineering* transforms [[raw data]] into a more effective, useful set of inputs that a model i.e. [[machine learning]] can use. 

docs/Public/Software/AI/machine learning.md

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+*Machine learning* is a subfield of [[artificial intelligence]] that involves building models for computers to help make intelligence decisions.
+
+Usually when programming, you define the rules. But it isn't always known what those rules are, as some problems can be very complex. 
+
+*Machine learning* will identify those rules and patterns; learning a [[function]] that maps desired inputs and outputs.