this is little exercisenot just in playing with svm and different kinds of svcbut also in familiarizing yourself with how to learn more on your own about svc andhonestlya very important trait of any data scientist or engineer is going to be the ability to go and look up information yourself when you don' know the answers soyou knowi' not being lazy by not telling you what those other kernels arei want you to get used to the idea of having to look this stuff up on your ownbecause if you have to ask someone else about these things all the time you're going to get really annoyingreally fast in workplace sogo look that upplay around itsee what you come up with sothat' svm/svca very high power technique that you can use for classifying datain supervised learning now you know how it works and how to use itso keep that in your bag of trickssummary in this we saw some interesting machine learning techniques we covered one of the fundamental concepts behind machine learning called train/test we saw how to use train/test to try to find the right degree polynomial to fit given set of data we then analyzed the difference between supervised and unsupervised machine learning we saw how to implement spam classifier and enable it to determine whether an email is spam or not using the naive bayes technique we talked about -means clusteringan unsupervised learning techniquewhich helps group data into clusters we also looked at an example using scikit-learn which clustered people based on their income and age we then went on to look at the concept of entropy and how to measure it we walked through the concept of decision trees and howgiven set of training datayou can actually get python to generate flowchart for you to actually make decision we also built system that automatically filters out resumes based on the information in them and predicts the hiring decision of person we learned along the way the concept of ensemble learningand we concluded by talking about support vector machineswhich is very advanced way of clustering or classifying higher dimensional data we then moved on to use svm to cluster people using scikit-learn in the next we'll talk about recommender systems