machine learning semi-supervised learning techniques need labeled dataand therefore human interactionto find patterns in the data setbut they can still progress toward result and learn even if passed unlabeled data as well in this sectionwe'll look at all three approachessee what tasks each is more appropriate forand use one or two of the python libraries mentioned earlier to give you feel for the code and solve task in each of these exampleswe'll work with downloadable data set that has already been cleanedso we'll skip straight to the data modeling step of the data science processas discussed earlier in this supervised learning as stated beforesupervised learning is learning technique that can only be applied on labeled data an example implementation of this would be discerning digits from images let' dive into case study on number recognition case studydiscerning digits from images one of the many common approaches on the web to stopping computers from hacking into user accounts is the captcha check-- picture of text and numbers that the human user must decipher and enter into form field before sending the form back to the web server something like figure should look familiar figure simple captcha control can be used to prevent automated spam being sent through an online web form with the help of the naive bayes classifiera simple yet powerful algorithm to categorize observations into classes that' explained in more detail in the sidebaryou can recognize digits from textual images these images aren' unlike the captcha checks many websites have in place to make sure you're not computer trying to hack into the user accounts let' see how hard it is to let computer recognize images of numbers our research goal is to let computer recognize images of numbers (step one of the data science processthe data we'll be working on is the mnist data setwhich is often used in the data science literature for teaching and benchmarking