Difference between random forest and XGboost. Interpretation of the missing values. Overfitting vs Underfitting. Describe one project in which I participated recently.
Machine Learning Interviews
Machine Learning Interview Questions
"To get a job in machine learning, you must have the programming and mathematical knowledge to create artificial intelligence that is capable of learning new tasks without being explicitly coded. In an interview you may be asked about your experience with pertinent coding languages such as Java and C++ as well as with writing algorithms. The interview will be comprised mainly of technical questions that test your knowledge of the fundamental concepts of machine learning such as data mining and signal processing."
8,207 machine learning interview questions shared by candidates
Give an example of using hyperparameter tuning to train ML models in production.
I am not sure whether I can go into the details. But it was general coding question for the first round. And for the second round questions were relevant to the role I applied for.
What is your biggest weakness?
Asked me about GANs and machine learning in general
1. Optimization Equation for SVM 2. Implementation of Random Forest on demo dataset
introducing your self , your education , experience and projects you worked in
Q(R1): What is your past experience, and how would you fit the role we are hiring for? Q(R2): What is the difference between bagging and boosting? What algorithms do you know from each class, and on what types of problems have you applied them in the past? Q(R3): Given this data, how would you build the dataset and the ML training pipeline?
What is the difference between SVM and Logistic Regression?
Assessment based on the machine learning
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