Gradient descent is an optimization method used to find the minimum value of a function by iteratively updating the parameters of the function.
Principal component analysis is a method used to reduce the number of dimensions in a dataset without losing much information.
K-Means is one of the simplest unsupervised clustering algorithm which is used to cluster our data into K number of clusters.
Support Vector machines (SVM) can be used for both classification as well as regression tasks but they are mostly used in classification applications.
Polynomial regression is a process of finding a polynomial function that takes the form f( x ) = c0 + c1 x + c2 x2 ⋯ cn xn where n is the degree of the polynomial and c is a set of coefficients.
Previously we built a simple linear regression model using a single explanatory variable to predict the price of pizza from its diameter.