#### Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Getting Started with Machine Learning
Classification – Decision Tree Learning
K-Nearest Neighbors Classifier
K-Means Clustering
Association Rule Learning
Linear Classifier and Logistic Regression
Neural Networks
Convolutional Neural Networks
Natural Language Processing
Machine Learning Libraries
Optimizing Neural Networks for Mobile Devices
Best Practices
Index

## Implementing k-means in Swift

Similar to the KNN from the previous chapter, we'll have a structure to represent an algorithm and keep all its hyperparameters:

```struct KMeans {
public let k: Int ```

The standard k-means algorithm was designed to be used only with Euclidean distance:

`internal let distanceMetric = Euclidean.distance `

We need several arrays to store different kinds of data during the clustering.

Storage for samples:

`internal var data: [[Double]] = [] `

Coordinates of centroids:

`public var centroids: [[Double]] = [] `

An array that matches each sample to its cluster. It should be of the same length as the data, and for every sample, it stores an index of centroid in the `centroids` array:

`private(set) var clusters: [Int] = [] `

Within-cluster sum of squares is a measure that we'll use later to assess the quality of the result:

`internal var WCSS: Double = 0.0 `

For now, the only parameter that we pass on the initialization is the number of clusters:

```public init (k: Int) {
self.k = k
}
} ```

Unlike...