HCLTech Data Science interview questions

asked about projects i did. the team lead asked most of the questions like how large datset i handled, what libraries i used, what algorithms i used. when told about deep learning i used for research, they straightaway told they are using machine learning techniques, not involving deep learning and hence disconnected the call. no question on machine learning or deep learning or statistics. waste of time and effort.

1) List out all the M.L. algo which you know? 2) difference between SVM and Naive Bayes algo? 3) what is the assumption for linear regression? and if it fails then what will be the action which you take? 4) what is AIC and BIC? 5) explain PCA? 6) explain the confusion matrix and if you are using a medical domain application then which matric you should focus? 7) what is precession and recall matric ? 9) take two dictionaries and create one data frame in python? write code 10) what is the assumption for logistic regression? 11) explain naive Bayes in detail and which probability is used in naive Bayes algo? 12) if two independent variables are there and both are highly correlated (corr = 0.98) then what'll be your approach to resolve it? 13) what is entropy? 14) what is gini? 15) what is information gain? where it used? 16) what is the knee or albo method and where we can use it.?