Am I understanding the ML breadth vs depth difference right:
1. In breadth, you cover most topics in a quick way
2. In depth, you dive into your resume and project, and maybe deep dive into a specific ML topic the company cares about? Feels more like a resume deep-dive round.
1. is on point. Its basically to evaluate how much you actually know ML. it can cover multiple algorithms, stats basics, pitfalls of each approach etc.
2. ML Depth is more nuanced actually. I have seen more deep dives on specific ML topic than actually projects from resume but yes you get resume projects questions once in a while. Generally the recruiter will pick a topic, for example transformer models (if in NLP) or from your resume, and will ask highly detailed questions, maths behind your objective functions and constraints, and most importantly the limitations of that particular approach and how to solve it.
This was a great read! Simple, yet useful.
Thank you for your support @Meri
Am I understanding the ML breadth vs depth difference right:
1. In breadth, you cover most topics in a quick way
2. In depth, you dive into your resume and project, and maybe deep dive into a specific ML topic the company cares about? Feels more like a resume deep-dive round.
1. is on point. Its basically to evaluate how much you actually know ML. it can cover multiple algorithms, stats basics, pitfalls of each approach etc.
2. ML Depth is more nuanced actually. I have seen more deep dives on specific ML topic than actually projects from resume but yes you get resume projects questions once in a while. Generally the recruiter will pick a topic, for example transformer models (if in NLP) or from your resume, and will ask highly detailed questions, maths behind your objective functions and constraints, and most importantly the limitations of that particular approach and how to solve it.
Very nicely written. Do MLE interviews at big tech also have a general SysD round?
Depends from company to company but generally yes there can be a general System design round.
I had gone through the article! It is very helpful.
Great list of resources, thanks for putting it together! 🙏
Based on my experience, machine learning interviews can be categorized into the following parts:
1. Coding
2. Machine Learning Foundation
3. Machine Learning System Design
4. Past Machine Learning Project Experience (resume discussion)
Thank you very much for sharing this! Can you please share how you prepared for these rounds, please? Will be sincerely grateful :)
Akanksha, I will be adding more details around machine learning interview rounds in future of my newsletters.
Thank you so much, Kartik! Looking forward to it! Really appreciate the kind help and support! 😇😊
Follow my public account, I will write articles later.
Can you please share your public account? Really looking forward to your preparation materials and other articles.
Thank you for your attention, I have replied to you in the Chat.
Hai Karthik,big thanks for sharing this.Btw im looking for ML internship and what type of projects do i need to build?
Was searching for a simply explained roadmap for a long time. Thanks for sharing
Do we require Data Structures and Algorithms to clear any product based companies.
If yes means what level of difficulty and sources ?
Amazing
This is helpful !
Glad you find it useful Harsh