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From own experience (switched to ML 1.5 years ago): 1. That software engineering skills are way more important than ML skills. 2. That you'd be spending more
by deepsun 8y ago
From own experience (switched to ML 1.5 years ago):
1. That software engineering skills are way more important than ML skills.
2. That you'd be spending more time on making presentation than doing ML (and it makes sense, it's very important to present statistics properly).
3. That most problems don't need good ML models. Something cheap and easy is often good enough. What you do need to be good, is data pipelines around them (see 1.)
In my case, I learned ML enough to feel "senior" compared to other people in company and online in less than a year. Same path to Senior SWE took me much longer (way larger mandatory knowledge base, probably because ML is a young field). So I'd say ML is definitely easier.
- rasikjain 8y agoVery good pointers. I would like to get in touch with you regarding how you transitioned to ML. I don't see a contact info in the profile. My email is in my profile. Pls let me know.
- deepsun 8y agoKaggle is more than enough to get started. I would hire anyone who's Master there. Probably not even need for Master, just enough knowledge to explain why that thing work and that would not. See this course to get into Kaggle: https://www.coursera.org/learn/competitive-data-science https://www.coursera.org/learn/competitive-data-science
- rasikjain 8y agoThank you for the inputs and course reference
- luhego 8y agoThose are great points. Could you comment what resources you have used to learn ML?
- deepsun 8y agoMostly Kaggle -- reading others solutions and notebooks and integrating them into mine code. Also there's a great Coursera course on ML for Kaggle: https://www.coursera.org/learn/competitive-data-science https://www.coursera.org/learn/competitive-data-science I think once you finish it, you're better than 60% of silicon valley data scientists, no kidding.