|As a reminder, a one-page summary of all the courses, books & videos
I’ve reviewed in the past year can be found on my Journey Roadmap page.
It’s been a summer of incredible transition for me as I’ve made a permanent move from the relatively chilly climate of New York (old house shown to the right) to the equatorial heat misery of South Carolina. I can only hope that this investment pays off in the winter when I’m enjoying a balmy 50-degree day while the Northeast shovels out of a blizzard.
I’ve not posted an “Accomplishments” blog since May, but that certainly shouldn’t indicate that I’ve not been pursuing Data Science over the summer. Far from it! Although I hadn’t completed any new courses or books in June and July, when I wasn’t busy packing up or tossing out all of my life’s possessions, I took advantage of the time to revisit a lot of the topics I’d covered in the past year. I began creating hundreds of Mnemosyne flashcards to sharpen my skillset. I retook the UoW Machine Learning: Regression Course, going over all code examples in painstaking detail. I also re-read every word of “An Introduction to Statistical Learning with Applications in R”, working through all of R labs and exercises, incorporating sample code into my Mnemosyne card set. It was an absolutely necessary activity, and I feel much stronger as a result. Consider revisiting some old courses you’ve taken – you’d be surprised that you can still get something new from them with multiple tries.
August, however, with the move complete, a number of endeavors also came to a successful close.
Coursera – Machine Learning: Clustering and Retrieval
This is the fourth course in the University of Washington Machine Learning Specialization on Coursera. Grouping and association were the theme here. Diving into large datasets of Wikipedia article entries, we found commonality between groups of articles, implemented various measures of “alikeness”, assigned articles to topics based on word groupings and made predictions on new articles based on models build from large training sets.