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


In this chapter, we had our first experience of building a machine learning application, starting from the data and all the way over to the working iOS application. We went through several phases in this chapter:

  • Exploratory data analysis using Jupyter, pandas, and Matplotlib
  • Data preparation—splitting, and handling categorical variables
  • Model prototyping using scikit-learn
  • Model tuning and evaluation
  • Porting prototype for the mobile platform using Core ML
  • Model validation on a mobile device

There are several machine learning topics that we've learned about in this chapter: model parameters vs. hyperparameters, overfitting vs. underfitting, evaluation metrics: cross-validation, accuracy, precision, recall, and F1-score. These are the basic things that will be recurring topics throughout this book.

We've become acquainted with two machine learning algorithms, namely decision trees and random forest, a type of model ensemble.

In the next chapter, we're going to continue exploring classification...