Looked at the optimisation take home test and discussed it further. It was a reasonably challenging algorithm that I didn't find the best solution to, but VP explained they spent some time on it to complete. The VP then walked me through the solution, prompting me at certain points to answer questions, it was actually really good to go through the best algorithm with him - I learnt something.
How RAG system works. Explain the Architecture of RAG model.
1)What is multicollinearity 2)Why will you not consider outliers in your model? 3)what are the assumptions you make in ML algorithms? 4)how do you deal with missing values? 5)what if you have 75 percent of the columns in your data is bull how do you handle??
Why are you a good fit for this role
How can you tell that a model is overfitting and how can you combat this?
How would you design recommender system?
what bagging and boosting in random forest?
What is the convolutional neural networks?
Can you walk us through a project you’ve worked on that involved solving a complex technical problem?
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