Linear Regression With Multiple Variables

Notation:
$n$ : number of features
$x^{(i)}$ : input (features) of $i^{th}$ training example
$x^{(i)}_j$ : value of feature $j$ in $i^{th}$ training example

$h_\theta (x) = \theta_0 + \theta_1 x_1 + \theta_2 x_2 + \theta_3 x_3 + \cdots + \theta_n x_n$

$<span class="ql-right-eqno"> </span><span class="ql-left-eqno"> </span><img src="https://teach.sg/wp-content/ql-cache/quicklatex.com-06f129ca3a97efd2c74e813c392da568_l3.png" height="32" width="384" class="ql-img-displayed-equation quicklatex-auto-format" alt="\begin{align*} h_\theta(x) = \begin{bmatrix} \theta_0 \hspace{2em} \theta_1 \hspace{2em} ... \hspace{2em} \theta_n \end{bmatrix} \begin{bmatrix} x_0 \newline x_1 \newline \vdots \newline x_n \end{bmatrix} = \theta^T x \end{align*}" title="Rendered by QuickLaTeX.com"/>$