I wanted to download some course material on RL shared by the author via Google drive using the command line.  I got a bunch of stuff using wget a folder in google drive was a challenge. I looked it up in SO which gave me a hint but no solution. I installed gdown using pip and then used: gdown --folder --continue https://drive.google.com/drive/folders/1V9jAShWpccLvByv5S1DuOzo6GVvzd4LV if there are more than 50 files you need to use --remaining-ok and only get the first 50. In such a case its best to download using the folder using the UI and decompress locally. Decompressing from the command line created errors related to unicode but using the mac UI I decompressed without a glitch.

### Data Science Glossary - K

 Cluster analysis.  K-Means produces poor results, while EM did better. The visualization was generated using ELKI.  (Image credit: Wikipedia)
K-Means Clustering
is a clustering method is the most basic clustering algorithm. This method has complexity of O(N). But it can converge to a locally optimal clustering solution, it is sensitive to outliers, noise only works on convex clusters and does not assist in selecting the correct number of clusters. K-means is a hard selection clustering method - for the soft selection method it is possible to use the expectation-maximization algorithm for mixtures of Gaussian distributions.
K-Medoids Clustering
is a clustering method based on K-Means which uses representatives for the means rather than computed geometric center of the cluster. It is more robust to noise and outliers than K-means.
K-Medians Clustering
is a clustering method based on K-Means, it selects the median point geometrically using the Manhattan distance and will provide more compact clusters.
K-Modes Clustering
is a clustering method based on K-Means which can be used to cluster categorical data using a number of multidimensional features.
Kernel K-Means Clustering
is a clustering method based on K-Means using the Kernel trick to solve cases where clusters are non convex. However this method has complexity of $O(N^2)$.