Update docs/src/inverse_problems/optimization_ode_param_fitting.md

Co-authored-by: Sam Isaacson <[email protected]>

Author: TorkelE

docs/src/inverse_problems/optimization_ode_param_fitting.md

@@ -1,7 +1,7 @@
 # [Parameter Fitting for ODEs using SciML/Optimization.jl and DiffEqParamEstim.jl](@id optimization_parameter_fitting)
 Fitting parameters to data involves solving an optimisation problem (that is, finding the parameter set that optimally fits you model to your data, typically by minimising a cost function). The SciML ecosystem's primary package for solving optimisation problems is [Optimization.jl](https://github.com/SciML/Optimization.jl). It provides access to a variety of solvers from a single common interface, wrapping a large number of optimisation methods that have been implemented in Julia into this interface.
 
-This tutorial both demonstrate how to create parameter fitting cost functions using the [DiffEqParamEstim.jl](https://github.com/SciML/DiffEqParamEstim.jl) package, and how to use Optimization.jl to minimise these. Optimization.jl can also be used in other contexts, such as finding parameter sets that maximises the magnitude of some system behaviour. More details on how to use these packages can be found in their [respective](https://docs.sciml.ai/Optimization/stable/) [documentations](https://docs.sciml.ai/DiffEqParamEstim/stable/).
+This tutorial demonstrates both how to create parameter fitting cost functions using the [DiffEqParamEstim.jl](https://github.com/SciML/DiffEqParamEstim.jl) package, and how to use Optimization.jl to minimise these. Optimization.jl can also be used in other contexts, such as finding parameter sets that maximise the magnitude of some system behavior. More details on how to use these packages can be found in their [respective](https://docs.sciml.ai/Optimization/stable/) [documentations](https://docs.sciml.ai/DiffEqParamEstim/stable/).
 
 ## Basic example