Book Image

Enterprise Internet of Things Handbook

By : Arvind Ravulavaru
Book Image

Enterprise Internet of Things Handbook

By: Arvind Ravulavaru

Overview of this book

There is a lot of work that is being done in the IoT domain and according to Forbes the global IoT market will grow from $157B in 2016 to $457B by 2020. This is an amazing market both in terms technology advancement as well as money. In this book, we will be covering five popular IoT platforms, namely, AWS IoT, Microsoft Azure IoT, Google IoT Core, IBM Watson IoT, and Kaa IoT middleware. You are going to build solutions that will use a Raspberry Pi 3, a DHT11 Temperature and humidity sensor, and a dashboard to visualize the sensor data in real-time. Furthermore, you will also explore various components of each of the platforms that are needed to achieve the desired solution. Besides building solutions, you will look at how Machine Learning and IoT go hand in hand and later design a simple predictive web service based on this concept. By the end of this book, you will be in a position to implement an IoT strategy best-fit for your organization
Table of Contents (12 chapters)

What is machine learning?

Machine learning is the process by which a system learns by itself without programming. The main goal of machine learning is to answer a question based on the data model that was created during the process of machine learning.

Let's say that we have a climate and weather dataset that has a correlation between temperature, humidity, and rainfall. The machine would observe this dataset using various algorithms and would generate a data model. A data model is the gist of the dataset, which can then be used to answer questions such as, "If the temperature is x and the humidity is y, will it rain?".

I may have over-simplified ML, but this is what lies at its core.

Tom M. Mitchell ( defined ML as the following:

"A computer program is said to learn from experience E with respect to some class of tasks T and performance...