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

Exploratory data analysis

First, we want to see how many individuals of each class we have. This is important, because if the class distribution is very imbalanced (like 1 to 100, for example), we will have problems training our classification models. You can get data frame columns via the dot notation. For example, df.label will return you the label column as a new data frame. The data frame class has all kinds of useful methods for calculating the summary statistics. The value_counts() method returns the counts of each element type in the data frame:

In []: 
platyhog       520 
rabbosaurus    480 
Name: label, dtype: int64 

The class distribution looks okay for our purposes. Now let's explore the features.

We need to group our data by classes, and calculate feature statistics separately to see the difference between the creature classes. This can be done using the groupby() method. It takes the label of the column by which you want to group your data:

In [...