K Nearest Neighbors (KNN) is one useful supervised ML algorithm, generally used for classification problems but can be used for regression problems too. It is non-parametric algorithm, meaning the algorithm does no make any particular assumption about the kind of mapping function or the underlying data. For now, let us assume we are working on a binary classification problem and each observation in the data set (2-D) belongs to either positive class or negative class The main purpose of classification is given a point'xq', we have to predict the output value'yq'. Please note: There are various types of methods to calculate the distance between two points. I will try to cover them in a separate article later.

Linear algebra, via the use of matrices and vectors, along with linear algebra libraries (such as NumPy in Python), allows us to perform a large number of calculations in a more computationally efficient way while using simpler code. Knowing at least the numeric operations of linear algebra is crucial to further understanding what happens in our machine learning models. Although having the geometric intuition behind linear algebra can be incredibly useful in visualizing the operations we will discuss below, it is not required to understand most machine learning algorithms. In this tutorial, we will discuss scalars, vectors, matrices, matrix-matrix addition and subtraction, scalar multiplication and division, matrix-vector multiplication, matrix-matrix multiplication, identity matrices, matrix inverses, and matrix transposes. In addition, we will very briefly discuss some of the geometric intuition behind some of these numeric operations.