Machine Learning case study interview. Open ended question with no right answer - they want to evaluate your ability to walk through a solution
Applied Scientist Interview Questions
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Why do you want to join Microsoft?
Self introduction and my work experience.
Coding test task 1: pandas on what looked like a phone company database (super lengthy) Coding test task 2: lasso, ridge and elastic net Screening: questions about past experience
🔹 1. Conceptual Questions (Beginner–Intermediate) ❓ Supervised vs. Unsupervised learning What is the difference between supervised and unsupervised learning? Give examples of real-world problems for each. ❓ Model Understanding What is overfitting and underfitting? How do you prevent overfitting? What is the bias-variance trade-off? What are precision, recall, F1-score, and when do you prefer one over another? ❓ Algorithms How does a decision tree work? What is the difference between logistic regression and linear regression? How does K-nearest neighbors (KNN) work? What is regularization (L1 vs. L2)? 🔹 2. Intermediate to Advanced Topics ❓ Ensemble Methods How does random forest work? What is gradient boosting (e.g., XGBoost, LightGBM)? Difference between bagging and boosting? ❓ Neural Networks What is backpropagation? What are activation functions and why are they important? Difference between CNNs and RNNs. What is dropout, and why is it used? ❓ Optimization What are common optimizers in deep learning? How does stochastic gradient descent (SGD) differ from batch gradient descent?
Given that we have a machine learning model that performs well locally but when deployed to users doesn't what could possible be the cause of this
alot on resume and theory on concepts covered in resume
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