Book Image

Artificial Intelligence for IoT Cookbook

By : Michael Roshak
Book Image

Artificial Intelligence for IoT Cookbook

By: Michael Roshak

Overview of this book

Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users’ lives easier. With this AI cookbook, you’ll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications. Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You’ll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you’ll learn how to deploy models and improve their performance with ease. By the end of this book, you’ll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.
Table of Contents (11 chapters)

How it works...

In the dataprep Python library, you will only import pandas so that we can take the CSV file and turn it into a pandas DataFrame. Once we have a pandas DataFrame we will filter out on Rainbow Beach (in our case, we are only looking at Rainbow Beach). Then we will take out anomalous data such as data where the water temperature is below -100 degrees. Then we will convert the time string into a string that pandas can read. We do this so that when it outputs, it outputs to a standard time series format. Then we select only the two columns we need to analyze, Measurement Timestamp and Turbidity. Finally, we save the file in CSV format.

Next, we create a Luminol file. From here, we use pip to install luminol and time. We then use the anomaly detector on the CSV file and return all of the scores. Finally, we return scores if the value of our score item is greater than 0. In other words, we only return scores if there is an anomaly.