ML - I

1. Machine Learning Interviews
Arpit Agarwal

2. Questions
• What is Machine Learning?
- “Field of study that gives computers the ability to learn without
being explicitly programmed.”
• What is Data Mining?
- “Process in which we try to extract knowledge or unknown interesting
patterns from unstructured data.”
• Difference between Data Mining and ML?
- “ML algorithms are applied to solve data mining problems”

3. –Difference between supervised and
unsupervised machine learning?

4. - What is training set, test set and validation
set?

5. - What is Overfitting? How to avoid it?
- “Cross-validation, regularization”
- What is regularization? Why do we need it?
- What is Bias-Variance tradeoff?

6. Overfitting – Curve Fitting

7. Overfitting

8. –Difference between generative and
discriminative models?
– “In generative model we try to model the
underlying probability distribution from which the
data was generated and then solve the task
whereas in discriminative model we directly solve
the task by learning discriminant functions”

9. –What is Naïve Bayes classifier? Why is
it called naïve?

10. –Explain Logistic Regression.

11. – Explain SVM and SMO.