The Great Algorithm Tutorial Roundup

This is a collection of tutorials relating to the results of the recent KDnuggets algorithms poll. If you are interested in learning or brushing up on the most used algorithms, as per our readers, look here for suggestions on doing so!

KDnuggets recently ran a poll asking our readers "Which methods/algorithms you used in the past 12 months for an actual Data Science-related application?"

844 voters participated, with the top 10 algorithms shown below:

Top 10 algorithms

The results were summarized and some analysis was offered in this post, which is a great read if you are looking for further breakdowns of what algorithms were reported by which types of respondents, respondent locations, etc.

As a result, we thought that the following resources may be useful to readers looking to plug holes in their knowledge of these particular algorithms, as well as machine learning algorithms in general.

Algorithm Basics

For algorithms basics, including many of the top reported algorithms outlined in the above graphic, the following posts are good places to start:

Algorithm Specifics

What follows are select tutorials and additional information for each of the top 10 algorithms appearing in the poll.




Decision Tree/Rules


K-nearest Neighbors

Principal Component Analysis (PCA)


Random Forests

Time Series/Sequence

Text Mining

Going Further

Here are a few posts which bring some of the concepts of machine learning algorithms together, or leverage some of them for different or novel approaches.

As always, we thank our guest bloggers for their ongoing fantastic contributions in the realm of machine learning and all other areas of data science.