This course will start by providing a definition of the term. We will talk about how various trends have enabled the Internet of Things, and how it changes the way programming is done. We will also discuss some of the ramifications that IoT is having on society today.

In this module we explore some of the details involved in the design and implementation of IoT devices. Unlike traditional computer-based systems, IoT devices are “embedded” within other devices in order to provide enhanced functionality without exposing the user to the complexities of a computer. The users interact with the device in a natural way, similar to their interactions with any other objects in the world. In this module we will discuss the structure of embedded systems and describe these interactions with the physical world.

IoT devices are implemented using both hardware and software components. Dedicated hardware components are used to implement the interface with the physical world, and to perform tasks which are more computationally complex. Microcontrollers are used to execute software that interprets inputs and controls the system. This module discusses the roles of both the hardware and software components in the system.

You would not find a top programmer, web developer, or AI enginner who does not use version control. Because it helps you produce better results and makes collaboration easy. Around the world, in teams large and small, Git is an essential part of the tool chain. We will start learning our learning process by covering Git and Github.

Linux containers are poised to take over the world; we will start this module with an introduction of Linux and the command line. For many non-technical people, the command line (also referred to as CLI, Terminal, bash, or shell) is a place of mystery. However, you only have to know a handful of basic commands to start feeling comfortable. In this module we will cover the basic commands to get you started.

This module provides a soup-to-nuts learning experience for core Docker technologies, including the Docker Engine, Images, Containers, Registries, Networking, Storage, and more. All of the behind the scenes theory is explained, and all concepts are clearly demonstrated on the command line. No prior knowledge of Docker or Linux is required.

Before implementing deep-learning algorithms in this quarter, we will first familiarize ourselves with mathematical blocks of neural networks theory. We going to start by getting our hands dirty writing some simple TensorFlow 2.0 code in Rust. And then move on to advanced deep learning concepts applicable to IoT projects. This module will also cover some essential advantages of TensorFlow 2.0 to convince you it’s the deep-learning library of choice.