What is the difference between supervised and unsupervised learning, and when would you use each approach? Can
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- What is the difference between supervised and unsupervised learning, and when would you use each approach?
- Can you explain the mathematical underpinnings of a neural network and how it works in practice?
- What is the curse of dimensionality and how does it affect machine learning algorithms?
- How do you choose the appropriate evaluation metric for a machine learning model, and why is this important?
- What is the role of regularization in machine learning, and what are some common methods of regularization?
- Can you explain the difference between parametric and non-parametric models, and when would you use each approach?
- What is the bias-variance tradeoff, and how does it impact model performance?
- How do you deal with missing data in a machine learning dataset, and what are some common imputation methods?
- Can you explain the concept of cross-validation and how it can be used to evaluate model performance?
- What are some common approaches to feature selection and extraction in machine learning, and when would you use each approach?
Related Book For
Basic Business Statistics Concepts And Applications
ISBN: 9780134684840
14th Edition
Authors: Mark L. Berenson, David M. Levine, Kathryn A. Szabat, David F. Stephan
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