The Hiring process consisted of two rounds :
- Technical Round (45min)
- Teaching Round (30min)
1) In the first round, the interviewer asked me if I had a prior experience in Machine Learning, about my projects next he asked me to implement Linear Regression to convert Celsius to fahrenheit and print its output on a Celsius value, its intercept, and slope.
Next, he asked me basic Machine Learning and Data Science questions and you have to type it on Google doc exactly the way you would write for a student :
- What is L1 and L2 regularization?
- What is the dropout layer?
- What is the central limit theorem?
- What are the ways to avoid overfitting in Decision Tree?
- How does splitting happen in Decision Tree?
2) In the next round, you are given 3 topics (Decision Tree, Backpropagation, PCA) and select one topic to prepare and teach the interviewer. **You are not given much time to prepare**
I chose Decision Tree and prepared slides for it. You can teach on a writing pad too.
Basically after you teach the interviewer, he came up with some follow-up questions like why do we use log2 in the Entropy formula?
How do you handle data with Decision Tree if it has both numerical and Classification data?
Is Decision Tree parametric or non-parametric?
and some questions related to the slides I prepared.
This round tests more how you handle follow-up questions and not what you answer