How to use an optimizer:
1) Provide a function to optimize, e.g f(x) = x**2+4
2) Provide an initial guess
3) Call the optimizer
  import scipy.optimize as spo
  min_result = spo.minimize(f, guess, method='SLSQP', options={'disp': True})
  print min_result.x, min_result.fun

Functions with multiple minima, any discontinuities or zero slope can be hard
to minimize.

Parameterized model
e.g. f(x) = mx + b <-- model with two parameters m, b
     now we can use an optimizer to minimise the squared error
     to find the line of best fit for the model given the data