Do you think a horse is acerouse or non-acerouse: A: non-acerouse supervised classification examples: A1: form an album of tagged photos, recognize someone in a picture + A2: given someone's music choices and a bunch of features of that music(tempo, genre, etc) recommend a new song features visualization: A: she likes those classification by eye: A: unclear speed scatterplot: Grade and bumpiness: A: smooth, flat speed scaterplot 2: A: medium, very steep speed scaterplot 3: A: bad flat from scatterplot to predictions: A: more like this from scatterplot to predictions 2: A: unclear from scatterplot to decision surfaces: A:Red cross A good linear decision surface: A: the line that is going between the blue and red GussianNB Deployment on terrain data: select the GussianNb.py file(this one will be a bit tricky) and past this under the defined function: clf = GaussianNB() clf.fit(features_train, labels_tr...