Automated tuning of Hyperparameter
There are a number of methods to automate the optimisation of your hyper-parameters, such as GridSearch and RandomSearch which the article you linked discusses briefly.

The main reason to choose one over the other is if you want the best possible parameters, and don't care how long it takes to get them: go for GridSearch. On the other hand, if you don't want the optimisation to take a long time, but still want some good parameters then go for RandomSearch.

These two implementations in ScikitLearn aren't exactly "advanced packages" but they'll get the job done for any model in Scikit (Random Forest, MLPClassifiers, etc). Emre's comment also has some pretty cool advanced packages which are Scikit compatible or Tensorflow compatible.