Book Image

Machine Learning for Mobile

By : Revathi Gopalakrishnan, Avinash Venkateswarlu
Book Image

Machine Learning for Mobile

By: Revathi Gopalakrishnan, Avinash Venkateswarlu

Overview of this book

Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples. You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains. By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Question and Answers
Index

Introduction to regression


Regression analysis is a basic method used in the statistical analysis of data. It's a statistical method that helps to find the relationships between variables. It is basically used for understanding the relationship between input and output numerical variables. We should first identify the dependent variable, which will vary based on the value of the independent variable. For example, the value of the house (dependent variable) varies based on the square footage of the house (independent variable). Regression analysis is very useful for prediction.

In a simple regression problem (a single x and a single y), the form of the model would be as follows:

y = A + B*x

In higher dimensions, when we have more than one input (x), the line is called a plane or a hyperplane

In our example, we predict the price of the house based on the various parameters that may impact the price of the data in that particular area.

The following are some of the important points to be considered...