Machine Learning
K-means clustering
I wanted to remind myself how the k-means clustering algorithm worked. Following are the steps involved in K-means clustering - 1. Start with a vector of 12 data points. For instance, [1, 2, 3, 4, 7, 8, 9, 10, 20, 21, 22, 23] 2. Randomly select 3 data points. These
Steps in Machine Learning
Let us assume that we have a matrix of 1,000 training examples (houses) as rows, and 10 features (distance to downtown, sq. foot of house and no. of schools) as columns. The last column 'y' denotes the affordability of the house - expensive (denoted as 1) or affordable (denoted
