Head over to https://teachablemachine.withgoogle.com/train and select the project you want to make:
I selected the pose project because I want to know if the pose of the user is similar to that of a dab.
After selecting the project you want to make, you'll be taken to the model training page. You can add
classes which are basically categories that your program will select based upon the input data. In this case, I want two classes, dab and idle. The class dab will have a higher attributed probability if the person is dabbing and idle will have a higher attributed probability if the person is not doing anything.
Training should be fairly straightforward, after defining the classes you give it training data using your camera or microphone.
After training (which took about 5 minutes for me with 150 samples for both classes), you can preview your model to see how well it's performing. If it doesn't work great try giving it more training data or more varied training data giving it more examples to learn from.
Once you're happy with the resultant model, press export model and then press upload and a link should come up below as well as an HTML code snippet and at this point, most of it has been done for you, for a basic app now you simply need to copy-paste the code into the repl.it HTML editor and maybe add a stylesheet (I just used water.css dark but you can do whatever).
This was the code snippet that should've been generated (for posenet only) in case you can't find it (the model url should be replaced with yours) :
You can also get fairly creative with the output array of predictions and use them as inputs into your own scripts.
Just note that the app you made might not always be fast.
Overall though, I think teachable machine is a great idea to make machine learning accessible.