rate points and build function approximation to make good decisions when the features are very numerousthis search becomes very expensiveboth from time and compute perspective in some casesit may be impossible to find good solution fast enough this problem is known as the curse of dimensionalityand unsupervised learning is well suited to help manage this with dimensionality reductionwe can find the most salient features in the original feature setreduce the number of dimensions to more manageable number while losing very little important information in the processand then apply supervised algorithms to more efficiently perform the search for good function approximation we will cover dimensionality reduction in feature engineering feature engineering is one of the most vital tasks data scientists perform without the right featuresthe machine learning algorithm will not be able to separate points in space well enough to make good decisions on never-before-seen examples howeverfeature engineering is typically very labor-intensiveit requires humans to creatively hand-engineer the right types of features insteadwe can use representation learning from unsupervised learning algorithms to automatically learn the right types of feature representations to help solve the task at hand we will explore automatic feature extraction in outliers the quality of data is also very important if machine learning algorithms train on raredistortive outlierstheir generalization error will be lower than if they ignored or addressed the outliers separately with unsupervised learningwe can perform outlier detection using dimensionality reduction and create solution specifically for the outliers andseparatelya solution for the normal data we will build an anomaly detection system in data drift machine learning models also need to be aware of drift in the data if the data the model is making predictions on differs statistically from the data the model trained onthe model may need to retrain on data that is more representative of the current data if the model does not retrain or does not recognize the driftthe model' prediction quality on current data will suffer by building probability distributions using unsupervised learningwe can assess how different the current data is from the training set data--if the two are different enoughwe can automatically trigger retraining we will explore how to build these types of data discriminators in using unsupervised learning to improve machine learning solutions