Data is available in all shapes, sizes, and forms: tweets, daily stock prices, per minute heartbeat signals, photos from cameras, video obtained from CCTV, audio recordings, and so on. Each of them contain information and when properly processed and used with the right model, we can analyze the data and, obtain advanced information about the underlying patterns. In this section, we will cover the basic preprocessing required for each type of data before it can be fed to a model and the models that can be used for it.
Time underlies many interesting human behaviors, and hence, it is important that AI-powered IoT systems know how to deal with time-dependent data. Time can be represented either explicitly, for example, capturing data at regular intervals where the time-stamp is also part of data, or implicitly, for example, in speech or written text. The methods that allow us to capture inherent patterns in time-dependent data is called...