Day 2 with R. Ideas for diving in deep waters

Start with example here: https://stat.ethz.ch/R-manual/R-devel/library/class/html/knn.html
Then go through: http://blog.datacamp.com/machine-learning-in-r/
========================
Additional reading:
https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Classification/kNN
https://www3.nd.edu/~steve/computing_with_data/17_Refining_kNN/refining_knn.html
http://blog.webagesolutions.com/archives/1164
http://www.analyticsvidhya.com/blog/2015/08/learning-concept-knn-algorithms-programming/

K means vs K nearest neighbors are two different things.
https://www.quora.com/Is-the-k-Means-algorithm-related-to-the-k-Nearest-Neighbors-algorithm
https://www.quora.com/How-is-the-k-nearest-neighbor-algorithm-different-from-k-means-clustering

What next:
http://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-r/

Additional topic:
R and SQL/MS SQL
http://www.burns-stat.com/translating-r-sql-basics/
http://www.r-bloggers.com/make-r-speak-sql-with-sqldf/ - use SQL in R
https://www.simple-talk.com/sql/reporting-services/making-data-analytics-simpler-sql-server-and-r/  make R talk to MS SQL

Ideas for day 3: http://courses.had.co.nz/10-tokyo/
http://www.analyticsvidhya.com/learning-paths-data-science-business-analytics-business-intelligence-big-data/learning-path-r-data-science/