Book Image

Hands-On Artificial Intelligence for IoT - Second Edition

By : Amita Kapoor
Book Image

Hands-On Artificial Intelligence for IoT - Second Edition

By: Amita Kapoor

Overview of this book

There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence.
Table of Contents (20 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Decision trees


In this section, you'll learn about another ML algorithm that's very popular and fast—decision trees. In decision trees, we build a tree-like structure of decisions; we start with the root, choose a feature and split into branches, and continue till we reach the leaves, which represent the predicted class or value. The algorithm of decision trees involves two main steps:

  • Decide which features to choose and what conditions to use for splitting
  • Know when to stop

Let's understand it with an example. Consider a sample of 40 students; we have three variables: the gender (boy or girl; discrete), class (XI or XII; discrete), and height (5 to 6 feet; continuous). Eighteen students prefer to go to the library in their spare time and rest prefer to play. We can build a decision tree to predict who will be going to the library and who will be going to the playground in their leisure time. To build the decision tree, we'll need to separate the students who go to library/playground based...