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

Understanding NLP


NLP is a huge topic, and it is beyond the scope of this book to go into detail on the subject. However, in this section, we will go through the high-level details of NLP and try to understand the key concepts required to prepare and process the textual data using NLP, in order to make it ready for consumption by machine learning algorithms for prediction

Introducing NLP

Huge, unstructured textual data is getting generated on a daily basis. Social media, websites such as Twitter and Facebook, and communication apps, such as WhatsApp, generate an enormous volume of this unstructured data daily—not to mention the volume created by blogs, news articles, product reviews, service reviews, advertisements, emails, and SMS. So, to summarize, there is huge data (in TBS).

However, it is not possible for a computer to get any insight from this data and to carry out specific actions based on the insights, directly from this huge data, because of the following reasons:

  • The data is unstructured...