Best books to prepare for Machine Learning Engineer interview?
I will likely be promoted to SDEII Q3 or Q4 of 2019. At that time I want to land an offer at Google or other team doing ML as an Machine Learning Engineer (coding because I don't have a PhD for doing research). That means the interviewing will be 7-8 months from now. How do I best prepare? Introduction to Algorithms?
Try not to get PIPed for now.
Why lol
The book from Kevin Murphy is good ( Machine Learning - A probabilistic perspective) . Also, best strategy could be to do a MOOC while studying a book. Try to understand the mathematical intuitions behind algorithms. Take up 2-3 good ML projects. Maybe try to replicate the results from a paper or 2. Get comfortable with 1 or 2 ML libraries. I personally like PyTorch. Read ML papers to tackle ML design rounds in interviews. As you learn more, you will get some grip and can explore different areas depending on what are some of the recent breakthroughs in the field.
ML is alchemy. Good luck.
Would you be able to suggest ML papers and other resources for ML design rounds?
Just remembered this friend from school with a PhD in ML, with papers and stellar projects, goes to Google for interview, gets some string matching algorithm question and fails the phone screen. Leetcode your ass my friend, you won't be spared.
This is so funny!
Same here. My friend was a post doc and a nips paper reviewer, but failed Google onsite.
2018? U have officially two more days!
Corrected