The purpose of this project is to use Python to play Grand Theft Auto 5. There are many things to do in GTA V, but our first goal will be to create a self-driving car, well scooter in this case. The idea of using GTA V is that it is such a massive, open, sand-box type of environment that we can control, so it makes for a great development area. Using the methods we use here, you should be able to follow along with a different game as well, it's certainly not required that you use GTA V, but that's what I will be using.
In the last part we introduced Classification, which is a supervised form of machine learning, and explained the K Nearest Neighbors algorithm intuition. In this tutorial, we're actually going to apply a simple example of the algorithm using Scikit-Learn, and then in the subsquent tutorials we'll build our own algorithm to learn more about how it works under the hood. To exemplify classification, we're going to use a Breast Cancer Dataset, which is a dataset donated to the University of California, Irvine (UCI) collection from the University of Wisconsin-Madison. UCI has a large Machine Learning Repository.
Welcome to the 10th part of our of our machine learning regression tutorial within our Machine Learning with Python tutorial series. We've just recently finished creating a working linear regression model, and now we're curious what is next. Right now, we can easily look at the data, and decide how "accurate" the regression line is to some degree. What happens, however, when your linear regression model is applied within 20 hierarchical layers in a neural network? Not only this, but your model works in steps, or windows, of say 100 data points at a time, within a dataset of 5 million datapoints.