Matrix
A matrix is a rectangular array of numbers arranged in rows and columns, used to organize data and perform transformations.
Definition
A matrix is a grid of numbers organized in rows and columns, a rectangular array with $m$ rows and $n$ columns called an $m \times n$ matrix, like a spreadsheet of numbers packed into one mathematical object; the entry in row $i$, column $j$ is denoted $a_{ij}$. Matrices can be added (if the same dimensions), multiplied by scalars, and multiplied together (if dimensions are compatible), representing linear transformations, so the product $AB$ represents the composition of transformation $B$ first, then $A$, which is why matrix multiplication is not commutative. Formally, an $m \times n$ matrix over a field $F$ is an element of the vector space $M_{m,n}(F)$ of dimension $mn$, and the set $M_n(F)$ of square matrices forms a (non-commutative, for $n \ge 2$) ring; every linear map $T: F^n \to F^m$ corresponds to an $m \times n$ matrix relative to chosen bases, and Jordan normal form shows that every complex square matrix is similar to a canonical "almost diagonal" form encoding the multiplicities of its eigenvalues.
Example
The $2\times 3$ matrix $\begin{bmatrix}1 & 2 & 3\\4 & 5 & 6\end{bmatrix}$ has $2$ rows and $3$ columns, with the entry in row $1$, column $2$ equal to $2$. For $A = \begin{bmatrix}2 & -1\\0 & 3\end{bmatrix}$ ($2\times 2$) and $B = \begin{bmatrix}1\\4\end{bmatrix}$ ($2\times 1$), the product $AB$ is defined and gives a $2\times 1$ result. Changing basis by an invertible matrix $P$ transforms $A \to P^{-1}AP$, a similarity transformation, so similar matrices represent the same linear map in different bases.
Key Insight
Matrices organize information compactly: a system of equations, a network of roads, or a color image can all be represented as matrices, making them one of the most versatile tools in mathematics, and matrix multiplication's non-commutativity reflects that order of transformations matters, rotating then reflecting differs from reflecting then rotating. Jordan normal form reveals the structure of every linear transformation over $\mathbb{C}$, with Jordan blocks encoding the geometric and algebraic multiplicities of eigenvalues.