There were **6 professors** in the panel. The total duration was around **20 minutes**.

P1 – Introduce yourself.

Me- Gave my introduction

P3 – Tell us about your Project.

Me – Answered

P2- Tell us more about it

Me- Explained the features and type of problem(It was a classification problem)

P4- What are the algorithms you used

Me- Discussed Naive Bayes, KNN, Logistic Regression, Random Forest, SVM for classification

P4-How you select the best algorithm?

Me- Explained Log loss correctly. I told them, logistic regression is performing better in terms of log loss

P4 – Write Logistic Regression Equation on board

Me- Wrote the Cost function equation and sigmoid equation.

P4- Explain how Logistic Regression works.

Me- Trying to explain taking Gradient Descent into consideration

P5- We are landing on different islands(He meant that they were not able to connect the dots)

Me- Drew a graph of gradient descent(convex function) and tried to explain but they were not satisfied

P6- Okay now tell me what you want to do in Research work

Me- Something related to NLP

P1- What is NLP

Me- Natural Language Processing

P5- Are you interested in Climate Study?

Me- said no but not directly

P6-Do you know Python

Me-Yes but I haven't practiced much since I was preparing for GATE

P4- So in what area do you want to work?

Me- In the health sector, genetics variation, predicting diseases, and automation of testing processes.

P6- Do you know Statistics

Me- Yes(with confidence)

P6- Tell us about Normal Distribution, and why it is useful

Me- Gave an answer but they were not satisfied

P6- Do you know about kurtosis, skewness, deviation

Me- I don't know about Kurtosis, but I know about skewness and deviation. explained Deviation

P4- Asked many questions related to the normal distribution and Random variable

Me- Answered a few of them. (In between I said “I don't know” more than 4 times)

P6- Are you interested in Statistics

Me – Absolutely

P1- Okay you can go now.

Me- Thank you, everyone

Result: Awaited.

PS: P4 helped me a few times when I was about to say “I don't know”.

Resources you can follow:

-Machine Learning(Krish Naik): https://rb.gy/ovemb

-Linear Algebra(Gilbert Strang): https://rb.gy/ay1b0

-Probability(John Tsitsiklis): https://ocw.mit.edu/courses/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/

Tips: They ask plenty of questions about your** project**, so prepare it thoroughly. If using some particular algorithms, then make sure you know the **maths** behind it. Do some research about the professor's work prior to the Interview. Professors are quite helpful when you are stuck, so try to take the hints. Have a positive attitude when you are not able to answer and accept your mistakes when you are wrong.

**All the best!**