Quick Introduction

What is machine learning?

Machine learning is a way for computers to predict information based on the data we given to them. Machine learning can do three main tasks: Regression, Classification and Clustering.

• Regression: Generates a continuous value (e.g. -1.1, 2.09, 20) from given data.

• Classification: Generates a discrete value (e.g. 1, 2, 3), mainly for classifying given data.

• Clustering: Generates centroids (center of data) based on the given data and predict which centroids that a data belongs to.

What is deep learning?

It is a more advanced version of machine learning, but mainly covers the neural network models. The training techniques are significantly improved and models are more complex compared to machine learning.

Use Cases:

LinearRegression:

• Make prediction on how long will a player reach certain level

• Spawn an enemy where the difficulty is based on input

LogisticRegression:

• Make an enemy that makes decision between 2 choices (e.g. fighting and running)

• Detect hacking players

KMeans:

• Group players in terms of experience level

SupportVectorMachine

• Detect hacking players

So how do we use this library?

In machine/deep learning, we mainly need to do training before we can predict things. To train, we need a lot of data and choose the correct models so that we can achieve very good results. Once training is done, you can use the model to predict values based on the data that was never seen before by the model.