ODSC West wasn’t; I went home with a number of practical tricks I will apply to the machine learning problems that await me at my day job.For starters, now I can succinctly disambiguate the terms “Data Science” and “Machine Learning” to my laymen colleagues, thanks to a talk by Amir Najmi, Principal Data Scientist at Google..Najmi said Data Science “frames decisions to be made under uncertainty,” while Machine Learning is but one of many data science tools that is “…not concerned with [the] provenance or truth of [a] model.” Najmi demonstrated he hasn’t drunk the well-documented conflation Kool Aid.This was a sobering perspective of our field, given how current media is tripping over buzzwords, and who better to hear it from than one of the co-editors of Google’s Unofficial Data Science blog.Easily one of my favorite presentations was from Alex Spangher, a Data Scientist at the New York Times..(That tool is used nowhere near the newsroom to preserve journalistic integrity.) I haven’t had many good applications of NLP (natural language processing) at work — my exposure to it has been limited to sentiment analysis — so when he shared how they built emotion-specific deep learning models, I was stoked.The real value of his talk for me, however, was a description of active learning, a resampling method he and his team used to make the most out of the dearth of articles labeled with the emotions they evoke..I’ll be putting this to use soon.Another helpful talk was from R for Everyone author Jared Lander, Principal Data Scientist at Lander Analytics, and a Columbia professor.. More details