Six months into my first machine learning role as a Computer Vision Engineer, I began to hit some roadblocks. Coming across Ganes Kesari’s article, I gained some superpowers to help overcome the challenges I faced.
I have to thank Natassha Selvaraj’s effort on creating more awareness and insight into the lack of female representation in tech fields. As ML practitioners and individuals who can change the status quo, we have to pay attention to Natassha’s article points.
Arnuld On Data provides ten challenges and the experience he’s gained from overcoming each challenge. There are few writers on Medium that can fill the gap of direct mentorship for data science practitioners, Arnuld is one of those writers. And so is Nicole Janeway Bills. I even borrowed one of the methods she uses to understand software packages, give her article a read below to find out more.
Bernardo Pereira provides a review of a book that offers a non-technical perspective of the field of machine learning and associated topics. It so happens the book is a recommended read by Bill Gates.
My good friend Ken Jee started arguably the largest data science hashtag movement of 2020. You’ve probably stumbled upon data science posts with #66DaysOfData. Read all about the movement’s intention in Ken’s article below.
Speaking of 2020, this year’s theme is unquestionably Covid-19, although it put a halt in a lot of people’s plan, it didn’t stop Chris Lovejoy from making a career transition from a Doctor to a Data Scientist.
As a result of the Covid lockdown, social platforms saw an increase in usage, especially TikTok. I still don’t understand the app enough, but information from Catherine Wang explained the TikTok’s algorithms’ components, and I think I have a better understanding of why users keep coming back to it.