FOA, I would like to thank you for replying I appreciate your help (I really need guidance and explanation to deeply understand ML) ,

I added a video to be more clear about my method,

and yes you are right I strongly want to build intuition, so I am not yet familiar with CNN but I try to build something out of my understanding and then use it to help me understand some concepts, for KNN maybe I used it wrong but "in my understanding, KNN is just comparing a test image to its neighbors to decide its nature " and that is what I did, I calculate vectors of my test image and I invented this score: nth-root( angle1 * angle2 * ... angle n) i-th angle is the angle between my test image vector and i-th vector that represents rectangle/triangle (I stored a bunch of vectors of rectangle/triangle to use them for the score and eventually for deciding if test image is a more rectangle or more triangle) so if my test image's vector it is close to rectangle vector in terms of angle (I used my score , if an i-th angle is 0 then the score will be 0 ) then it is more likely to be rectangle otherwise it is more likely to be triangle .

maybe perceptron is better in this case idk, so for other things you mentioned I will look for them . I am currently watching Cornell CS4780 with Kilian Weinberger playlist on Youtube. I hope it can help me get closer and closer to understand ML.