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This repository is for applied machine learning algorithms using python

Some basic Definitions

Model

A specific mathematical or computational description that expresses the relationship between a set of input variables and one or more outcome variables that are being studied or predicted.

Independent Variables

Also called features or input variables or predictors are an input to the model

Dependent Variables

Also called target value / response variables / target label or the outcome variable, which is the expected outcome of the model.

Feature Representation

Feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data.

Supervised learning

Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).

Algorithms

1. K Nearest Neighbors (k-NN) Algorithm