- Q1. What is the difference between AI, Data Science, ML, and DL?
- Q2. What is the difference between Supervised learning, Unsupervised learning and Reinforcement learning?
- Q3. Describe the general architecture of Machine learning.
- Q4. What is Linear Regression?
- Q5. OLS Stats Model (Ordinary Least Square)
- Q6. What is L1 Regularization (L1 = lasso) ?
- Q7. L2 Regularization(L2 = Ridge Regression)
- Q8. What is R square(where to use and where not)?
- Q9. What is Mean Square Error?
- Q10. Why Support Vector Regression? Difference between SVR and a simple regression model
- Q1. What is Logistic Regression?
- Q2. Difference between logistic and linear regression?
- Q3. Why we can’t do a classification problem using Regression?
- Q4. What is Decision Tree?
- Q5. Entropy, Information Gain, Gini Index, Reducing Impurity?
- Q6. How to control leaf height and Pruning?
- Q7. How to handle a decision tree for numerical and categorical data?
- Q8. What is the Random Forest Algorithm?
- Q9. What is Variance and Bias tradeoff?
- Q10. What are Ensemble Methods?
- Q11(a). What is SVM Classification?
- Q11(b). What is Naive Bayes Classification and Gaussian Naive Bayes
- Q12. What is the Confusion Matrix?
- Q13. What is Accuracy and Misclassification Rate?
- Q14. True Positive Rate & True Negative Rate
- Q15. What is False Positive Rate & False negative Rate?
- Q16. What are F1 Score, precision and recall?
- Q17. What is RandomizedSearchCV?
- Q18. What is GridSearchCV?
- Q19. What is BaysianSearchCV?
- Q20. What is ZCA Whitening?
- Q1: What is upsampling and downsampling with examples?
- Q2: What is the statistical test for data validation with an example, Chi-square, ANOVA test, Z statics, T statics, F statics, Hypothesis Testing?
- Q3: What is the Central limit theorem?
- Q4: What are the correlation and coefficient?
- Q5: What is the difference between machine learning and deep learning?
- Q6: What is perceptron and how it is related to human neurons?
- Q7: Why deep learning is better than machine learning?
- Q8: What kind of problem can be solved by using deep learning?
- Q9: List down all the activation function using mathematical Expression and example. What is the activation function?
- Q10: Detail explanation about gradient descent using example and Mathematical expression?
- Q11: What is backward propagation?
- Q12: How we assign weights in deep learning?
- Q13: What is optimizer is deep learning, and which one is the best?
- Q14: What is gradient descent, mini-batch gradient descent, batch gradient decent, stochastic gradient descent and Adam?
- Q15: What are autoencoders?
- Q16: What is CNN?
- Q17: What is pooling, padding, filtering operations on CNN?
- Q18: What is the Evolution technique of CNN?
- Q19: How to initialize biases in deep learning?
- Q20: What is learning Rate?