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Machine Learning - Questions and Answers

Q&A Topics:

  1. Supervised vs Unsupervised
  2. Bias vs Variance
  3. Underfitting vs overfitting
  4. Tackle overfitting
  5. L1 regulariziation
  6. L2 regularization
  7. L1 (Lasso) vs L2 (Ridge)
  8. Dropout
  9. Why feature reduction / dimensionality reduction
  10. How feature reduction / dimensionality reduction
  11. AUC ROC
  12. No Free Lunch Theorum
  13. Empirical Risk
  14. Class imbalance tackle
  15. Selection bias
  16. What is random forest? Why "random"?
  17. Decision trees vs Logisitc regression
  18. SVM vs Logistic Regression
  19. Kernel trick
  20. Primal vs Dual version of classifier
  21. Parametric vs Non-parametric
  22. KNN
  23. KMeans
  24. KNN vs Kmeans
  25. Smoothing Time series
  26. Gradient descent
  27. Backpropagation
  28. Regularization
  29. Normalization
  30. Batch Normalization
  31. Vanishing Gradients
  32. Exploding gradients

Other Topics:

  1. Dimensionality reduction
  2. PCA
  3. Kernel PCA
  4. Ridge regression
  5. L1 vs L2 loss
  6. Activation Functions
  7. Advantages of RelU
  8. Thresholds for a classifier
  9. Interpretation of an ROC area under the curve as an integral
  10. Confusion matrix

More elaborate and extensive Questions:

  1. General Machine Learning
  2. Deep Learning
  3. Mathematics for Machine Learning