When Going Faster Slows You Down Usually when building a machine learning algorithm from scratch (like we do in the Cornell Machine Learning program), you want to employ vectorized / matrixed math operations everywhere you possibly can in order to speed up performance. The reason for this is that it’s…

Natural Language Processing — Defining Tokens I have what is now an embarrassing confession to make. Before I started deeply exploring the math, statistics, and theory behind data science, I had a notion that predictive analytics — i.e taking tabular data and using it to predict the future — was…

Rounding a Corner in my Master’s Degree Journey I recently submitted the final exam for the High Performance Computing course in my Master’s Degree program in Health Data Science. While this represents completion of 70% of my overall coursework (assuming I pass, fingers crossed…) this was the final code-specific data…

A Clear Answer to Which Machine Learning Approach is the Best A commonly asked question among those studying machine learning for the first time, especially after they have reviewed several different machine learning algorithms, is “Which one is the best one?” …

Use Dask for the Task and Bask in the Fast In my last post I compared four different approaches to performing a task in Python, with NumPy and vectorized Python dramatically outperforming for loops and list comprehensions. But what if there were a way to go even faster, particularly on…

NumPy and Vectorization vs. List Comprehensions and For Loops Occasionally when facilitating Cornell classes a student question why NumPy functions and vectorized code approaches are so much better in data science than list comprehensions and native for loops, and what the actual performance difference is in real life. …

Google Cloud Certified Professional Machine Learning Engineer Last Friday I took and passed the Google Cloud Certified Professional Machine Learning Engineer exam. (GCCPMLE Certification? That’s a mouthful…) This one was tough in part because of its incredibly large scope. It covers domains from machine learning practitioner, general ML ops, and…

Better Testing Error Gets! As a kid, I loved the play on words of the “Fuzzy Wuzzy was a bear…” tongue twister (probably a little too much). It struck a chord with me, not only because it was hard to say five times fast, but because of the imagery of…