supervised machine learning this is the first on machine learning it covers the following topicsdatahow to load ittraining and test sets featuresmany of the features come directly from the data sometimes it is useful to construct featurese height might be boolean feature constructed from the real-values feature height the next is about neural networdks and how to learn featuresin this we construct explicitly in what is often known feature engineering learning with no input featuresthis is the base case of many methods what should we predict if we have no input featuresthis provides the base cases for many algorithms ( decision tree algorithmand baselines that more sophisticated algorithms need to beat it also provides ways to test various predictors decision tree learningone of the classic and simplest learning algorithmswhich is the basis of many other algorithms cross validation and parameter tuningmethods to prevent overfitting linear regression and classificationother classic and simple techniques that often work well (particularly combined with feature learning or engineeringboostingcombining simpler learning methods to make even better learners good source of classic datasets is the uci machine learning repository [lichman [dua and graff the spect and car datasets are from this repository