Machine & Deep Learning Algorithms: Regression & Clustering

recognize the application of a confusion matrix and how it can be used to measure the accuracy, precision, and recall of a classification model

describe how regression works by finding the best fit straight line to model the relationships in your data

list the characteristics of regression such as simplicity and versatility, which have led to the widespread adoption of this technique in a number of different fields

distinguish between supervised learning techniques such as regression and classification, and unsupervised learning methods such as clustering

describe how clustering algorithms are able to find data points containing common attributes and thus create logical groupings of data

recognize the need to reduce large datasets with many features into a handful of principal components using the PCA technique

to recall concepts such as precision and recall and the use cases for unsupervised learning