Fixed important missing parenthesis. (#52)

y - ( mx + b) not equal to y - mx + b

docs/linear_regression.rst

@@ -128,7 +128,7 @@ To minimize MSE we use :doc:`gradient_descent` to calculate the gradient of our
 
 .. rubric:: Math
 
-There are two :ref:`parameters <glossary_parameters>` (coefficients) in our cost function we can control: weight :math:`m` and bias :math:`b`. Since we need to consider the impact each one has on the final prediction, we use partial derivatives. To find the partial derivatives, we use the :ref:`chain_rule`. We need the chain rule because :math:`(y - (mx + b))^2` is really 2 nested functions: the inner function :math:`y - mx + b` and the outer function :math:`x^2`.
+There are two :ref:`parameters <glossary_parameters>` (coefficients) in our cost function we can control: weight :math:`m` and bias :math:`b`. Since we need to consider the impact each one has on the final prediction, we use partial derivatives. To find the partial derivatives, we use the :ref:`chain_rule`. We need the chain rule because :math:`(y - (mx + b))^2` is really 2 nested functions: the inner function :math:`y - (mx + b)` and the outer function :math:`x^2`.
 
 Returning to our cost function: