An RF Signal is an element that a human cannot see nor hear, but could be measured with many means today. Particularly, the Software-Defined Radio allows even people with a low budget to observe radio frequencies in real-time, and so make they capture different types of communications: AM/FM, Mobile & LPWAN communications, etc. There are many ways to classify all the technologies depending on the used frequency, used bandwidth, duty cycle, and patterns, but it is sometimes hard and/or time-consuming to recognize these technologies. To resolve these types of challenges, we thought about using Machine & Deep Learning tools to optimize our classification, and we wanted to share with you our successes, mistakes, and other feedback. In addition to proper classification, RF emanations are also permanent in the air, and we will see that the same techniques can be applied to match harmonics, but also for side-channel attacks as well. In this presentation, we will go through the steps of observing a signal, doing capture, talking about challenges to classifying the signal, and show techniques of using ML & DL from making a model, to using algorithms and available functions. This will be an opportunity to talk about our infrastructure, today's results, failures, and future improvements.