1. Explain Matrix Factorization for the recommendation system. 2. Case study based question. 2. Explain generative adversarial networks. 3. Loss function of GAN 4. Coding question, solve n stairs problem. every time we can take step 1 ot step 2. print max possibles ways. use either dynamic or recursive approach.
search optimization, functional programming, NLP
I have mentioned them above.
Tipical HR interview, what do you know about Glovo etc etc
all ML questions, python and sql
SQL, machine learning related questions
Focus on my projects, AWS knowledge, data science and ML basics
One of the questions at the panel interview focused on how to handle high-cardinality categorical variables in a model, especially when a naive approach could introduce noise or performance issues. The discussion was very practical and centered on trade-offs rather than theoretical tricks.
Bias Variance tradeoff Random forest Hyper-parameter tuning How to build Random forest pandas based python queries Linear regression assumptions Precision Vs Recall Cloud related Pandas group-by, average, sum queries entropy, info gain Random Search CV Bagging Boosting
For a regression, under which assumptions MSE and log-likelihood are proportional?
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