My learning journey so far:
spring-summer '17: fast.ai part 1 & 2
spring '18: diving deep on Bayesian methods
spring '19: facilitating a second run of MLE in 10 Weeks. From a review from one participant: "I feel like a much more confident and prepared machine learning practitioner with these tools in my toolbox." also spring '19: build the creativity-stimulating Duet, enabling users to write collaboratively with OpenAI's state-of-the-art language model (with Austin Hou and Tina Kim
also spring '19: became Lead MLE.
This field is great fun - it moves very quickly, you can have an impact on many different and otherwise unrelated fields (because it's very general), and it's still pretty rare. I remember thinking in 2016 that it was too late to get into machine learning, and now I realize more than ever that it's still early days. Another thing that makes it fun is that it's young and still relatively shallow: unlike physics where it takes a 5 year PhD to make a small improvement, you can reach the cutting edge of an ML subfield within a year or two of learning (assuming a programming background), and PhDs are often not necessary even for research positions.
Updated: March 22nd, 2019