Classify stuff with machine learning- Week 5- Kennadi

You’re starting a new venture firm MACHINES ARE SMART, that specializes in machine learning. You are going to pitch a new project to venture capital investors, and you need a proof of concept.  Use teachable machine to build a model that does one of the following:

  • A special pet door that only lets in only cats, for cat lovers.
  • A foreign language training app that teaches you to say “hello” in a foreign language and rejects attempts that aren’t correct. 
  • An app that rejects drinks that aren’t coffee. 
  • An app that tells if elderly nursing home residents are happy or sad, and alerts loved ones if the nursing home resident is too sad too often.

Reflect on the following question: You’ll have to think about how to train the machine.  What kind of data did you include in your training dataset and why? What other kind of data could have been helpful but maybe you couldn’t get in the short-term/for free? Your group may, in some cases, search for photograph sets.  One possibility to get large data sets is to convert YouTubes into clips. Did your model work well for what you wanted?  In what instances might your model not work very well?  Include the link to your project.

Link to teachable machine:

https://teachablemachine.withgoogle.com/models/_cRw-DEaC

I did “An app that rejects drinks that aren’t coffee”. For my data I included 2 classes- The first class was labeled as ‘coffee’ and I had two images of different cups of coffee uploaded. My second class was labeled ‘not coffee’ and had 3 different images with different drinks in each image. Two of these images included a variety of drinks within themselves rather than individual images of each drink. I did this that way the machine would know what drinks belong to which category and if it is coffee or not. Other data that could have been helpful would be different types of coffee or the cups that coffee could come in, in forms of pictures. However, it didn’t make the biggest difference with there only being two examples under ‘coffee’. My model did work very well for what I wanted to achieve. I was able to upload a few images off of google and get exactly what I wanted out of it. However, if I were to use this model and have my drink in a cup that doesn’t show the drink in it then it may not work well due to not being able to see what is in the cup itself and it would just be assuming the drink.

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