Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

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.
Table of Contents (18 chapters)
Title Page
Packt Upsell

Data preprocessing

In the following sections we will take a look at the different data processing techniques.

Converting categorical variables

As you already have noticed, a data frame can contain columns with the data of different types. To see which type has each column, we can check the dtypes attribute of the data frame. You can think about Python attributes as being similar to Swift properties:

In []: 
length    float64 
color      object 
fluffy       bool 
label      object 
dtype: object 

While length and fluffy columns contain the expected datatypes, the types of color and label are less transparent. What are those objects? This means those columns can contain any type of the object. At the moment, we have strings in them, but what we really want them to be are categorical variables. In case you don't remember from the previous chapter, categorical variables are like Swift enums. Fortunately for us, data frame has handy methods for converting columns from one type...