there is python markov decision process toolbox that wraps it up in all that terminology we talked about there' an example you can look ata working example of the cat and mouse gamewhich is similar andthere is actually pac-man example you can look at online as wellthat ties in more directly with what we were talking about feel free to explore these linksand learn even more about it and so that' reinforcement learning more generallyit' useful technique for building an agent that can navigate its way through possible different set of states that have set of actions that can be associated with each state sowe've talked about it mostly in the context of maze game butyou can think more broadlyand you know whenever you have situation where you need to predict behavior of something given set of current conditions and set of actions it can take reinforcement learning and -learning might be way of doing it sokeep that in mindsummary in this we saw one of the simplest techniques of machine learning called -nearest neighbors we also looked at an example of knn which predicts the rating for movie we analysed the concepts of dimensionality reduction and principal component analysis and saw an example of pcawhich reduced data to two dimensions while still preserving its variance nextwe learned the concept of data warehousing and saw how using the elt process instead of etl makes more sense today we walked through the concept of reinforcement learning and saw how it is used behind the pac-man game finallywe saw some fancy words used for reinforcement learning ( -learningmarkov decision processand dynamic learningin the next we'll see how to deal with real-world data