fix typos

docs/FAQ.rst

@@ -10,7 +10,7 @@ Constraining the number of assets
 Unfortunately, cardinality constraints are not convex, making them difficult to implement.
 
 However, we can treat it as a mixed-integer program and solve (provided you have access to a solver). 
-or small problems with less than 1000 variables and constraints, you can use the community version of CPLEX:
+for small problems with less than 1000 variables and constraints, you can use the community version of CPLEX:
 ``pip install cplex``. In the below example, we limit the portfolio to at most 10 assets::
 
     ef = EfficientFrontier(mu, S, solver=cp.CPLEX)

docs/UserGuide.rst

@@ -217,8 +217,10 @@ To combat this, I have introduced an objective function which borrows the idea o
 regularisation from machine learning. Essentially, by adding an additional cost
 function to the objective, you can 'encourage' the optimizer to choose different
 weights (mathematical details are provided in the :ref:`L2-Regularisation` section).
-To use this feature, change the ``gamma`` parameter::
+To use this feature, change the ``gamma`` parameter::   
 
+    from pypfopt import objective_functions
+
     ef = EfficientFrontier(mu, S)
     ef.add_objective(objective_functions.L2_reg, gamma=0.1)
     w = ef.max_sharpe()