Data science: What is feature selection and how will you choose the best features to describe the data and to build the good model?
Machine Learning Engineer Intern Interview Questions
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What is python and data types
Como eu agiria em certas situações.
A coding question and brief introduction of myself.
Question-related to machine learning basics and programming basics
Cosa si intende per polimorfismo in programmazione a oggetti?
what can I do if the data does not fit into the batch size?
Q. How would you chunk
Explain the bias-variance trade-off in machine learning and its significance. Can you describe the difference between supervised and unsupervised learning? Provide examples of each. What is regularization in the context of machine learning, and why is it important? How do decision trees work, and what are some methods to prevent overfitting in decision trees? Explain the K-nearest neighbors (KNN) algorithm. What are its pros and cons? What is cross-validation, and why is it used in machine learning? Describe a few different cross-validation techniques. Discuss the difference between precision and recall. How would you choose between models with different precision-recall trade-offs? What is gradient descent? How does it relate to training machine learning models? Can you explain the concept of feature engineering and its role in improving model performance? Describe the process of dimensionality reduction. When and why might you apply it in a machine learning pipeline?
Asked me questions about neural networks.
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