PyData London 2016
This talk outlines my journey from complete novice to machine learning practitioner. It started in November 2015 when I left my job as a project manager, and by April 2016 I was hired as a Data Scientist by a startup developing bleeding edge deep learning algorithms for medical imagery processing.
SHORT INTRO Who I am, my background and short summary of my story. Here I will list the steps I personally took to achieve the goal I had.
HOW DID I DO IT? Why I chose a “hacky” way to enter this career path. First mover advantage, why getting a degree doesn’t always improve your career prospects. Possibly a rant on the signalling function of formal education and how that is rarely aligned with a relevant practical skill set. Some stats to back it up (best career success predictors). Examples of hacking bureaucracies/social hierarchies from my experience and elsewhere. List of things not to do and common cognitive pitfalls. Networking for nerds - how to do it right. Time management for chronic procrastinators - how to plan a self-guided project. Some notes on psychology of time discounting and need for external reinforcement, with autobiographical examples. CONCLUSION You don’t need a PhD or even a masters to do machine learning. On taking calculated risks and especially calculated exits from one’s comfort zone. Some notes on soul searching and how to choose a career that is also a passion. Reading list.
Slides available here: https://slack-files.com/T0LFE6T6J-F170V6945-ad054e4f6a