Alexey Grigorev is the founder of DataTalks.Club, one of the world's most popular data communities. He is the principal data scientist at OLX. He has written a few books on machine learning. His most recent book is Machine Learning Bookcamp, which is especially relevant to software engineers who want to get into machine learning.
In today's episode we cover a range of topics including:
Personal journey into the world of data science:
- How did you enter the world of data science?
- Can you talk about your recent book Machine Learning Bookcamp?
- You run one of the best data communities in the world. How did you launch it and get the first few members to join?
- What's the hardest part about running a community?
Careers, Jobs, and Interviews:
- How do you guide new professionals in evaluating what area they like within ML?
- How should a data scientist look for jobs?
- How do you interview people?
- What are some of the red flags during hiring?
- How do you onboard a new hire?
- What does a great ML professional look like?
Product:
- How should ML professionals talk to users/customers?
- What do you believe makes for a great product experience?
- How important is the ability to write production-level code?
Overall trends:
- What product that you've personally used has impressed you the most? And why?
- What has been the biggest positive development in ML compared to 5 years ago?
- Looking forward, what aspect of ML excites you the most?
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By Prateek Joshi
4.9
88 ratings
Alexey Grigorev is the founder of DataTalks.Club, one of the world's most popular data communities. He is the principal data scientist at OLX. He has written a few books on machine learning. His most recent book is Machine Learning Bookcamp, which is especially relevant to software engineers who want to get into machine learning.
In today's episode we cover a range of topics including:
Personal journey into the world of data science:
- How did you enter the world of data science?
- Can you talk about your recent book Machine Learning Bookcamp?
- You run one of the best data communities in the world. How did you launch it and get the first few members to join?
- What's the hardest part about running a community?
Careers, Jobs, and Interviews:
- How do you guide new professionals in evaluating what area they like within ML?
- How should a data scientist look for jobs?
- How do you interview people?
- What are some of the red flags during hiring?
- How do you onboard a new hire?
- What does a great ML professional look like?
Product:
- How should ML professionals talk to users/customers?
- What do you believe makes for a great product experience?
- How important is the ability to write production-level code?
Overall trends:
- What product that you've personally used has impressed you the most? And why?
- What has been the biggest positive development in ML compared to 5 years ago?
- Looking forward, what aspect of ML excites you the most?
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