of datathe rocket ship cannot fly but not all data is created equal to use supervised algorithmswe need lots of labeled datawhich is hard and costly to generate with unsupervised learningwe can automatically label unlabeled examples here is how it would workwe would cluster all the examples and then apply the labels from labeled examples to the unlabeled ones within the same cluster unlabeled examples would receive the label of the labeled ones they are most similar to we will explore clustering in overfitting if the machine learning algorithm learns an overly complex function based on the training datait may perform very poorly on never-before-seen instances from holdout sets such as the validation set or test set in this casethe algorithm has overfit the training data--by extracting too much from the noise in the data--and has very poor generalization error in other wordsthe algorithm is memorizing the training data rather than learning how to generalize knowledge based off of it to address thiswe can introduce unsupervised learning as regularizer regularization is process used to reduce the complexity of machine learning algorithmhelping it capture the signal in the data without adjusting too much to the noise unsupervised pretraining is one such form of regularization instead of feeding the original input data directly into supervised learning algorithmwe can feed new representation of the original input data that we generate this new representation captures the essence of the original data--the true underlying structure--while losing some of the less representative noise along the way when we feed this new representation into the supervised learning algorithmit has less noise to wade through and captures more of the signalimproving its generalization error we will explore feature extraction in curse of dimensionality even with the advances in computational powerbig data is hard for machine learning algorithms to manage in generaladding more instances is not too problematic because we can parallelize operations using modern map-reduce solutions such as spark howeverthe more features we havethe more difficult training becomes there are startups such as figure eight that explicitly provide this human in the loop service underfitting is another problem that may occur in building machine learning applicationsbut this is easier to solve underfitting occurs because the model is too simple--the algorithm cannot build complex enough function approximation to make good decisions for the task at hand to solve thiswe can allow the algorithm to grow in size (have more parametersperform more training iterationsetc or apply more complicated machine learning algorithm unsupervised learning in the machine learning ecosystem