Supervised Learning

Question 1

What is machine learning in simple terms?

Question 2

What are the two main types of machine learning algorithms?

Question 3

What is Supervised Learning, and what are some common tasks it is used for?

Question 4

What is linear regression, and what is the goal of this algorithm?

Question 5

What is the linear regression function definition?

Question 6

What are other names for the variables w and b in linear regression?

Question 7

What is the cost function formula for a simple linear function.

Question 8

How is the cost function used to measure the difference between the predicted value and actual label for each training example?

Question 9

How is the cost function used to aggregate the error across all examples in the training set?

Question 10

What is the regularization term used in the cost function for linear regression, and why is it helpful for gradient computation?

Question 11

What is the goal of gradient descent in machine learning, and how is the gradient used in this process?

Question 12

How does the learning rate affect the efficiency of the gradient descent algorithm?

Question 13

What is the consequence of using a learning rate that is too small in gradient descent?

Question 14

What is the consequence of using a learning rate that is too large in gradient descent?

Question 15

How is the gradient used to update the values of the parameters during optimization process in gradient descent.

Question 16

How is the choice of learning rate related to the convergence rate of the gradient descent algorithm?

Question 17

What is the equation for the model’s prediction with multiple variables give by the linear model?

Question 18

What is vectorization, and why is it useful?

Question 19

What is the vector notation equation for the linear model?

Question 20

What happens to the parameters w and b in multiple linear regression?

Question 21

What is the equation for the cost function with multiple variables?

Question 22

What is feature engineering and why is it important for machine learning

Question 23

What are the different tasks involved in feature engineering?

Question 24

Explain the process of feature scaling, and what it is important for machine learning.

Question 25

What is polynomial regression, and how does it relate to feature engineering?

Question 26

Provide an example of how you have used feature engineering in your project.

Question 27

What is classification, and how does it differ from other types of machine learning techniques?

Question 28

Explain the logistic regression model and how it is used for binary classification models?

Question 29

What is the sigmoid function, and how is it used in logistic regression?

Question 30

What is a decision boundary, and how is it used in logistic regression?

Question 31

What is a decision boundary, and how is it determined in binary classification problems?

Question 32

Why is the squared error cost function not suitable for logistic regression?

Question 33

What is the cost function in logistic regression and how is it calculated?

Question 34

Explain the difference between loss and cost function in machine learning.

Question 35

How does gradient descent work in logistic regression?

Question 36

What are some advantages of logistic regression over other classification algorithms.

Question 37

How do you evaluate the performance of a logistic regression model?

Question 38

What is overfitting, and how does it occur in machine learning?

Question 39

Explain the differences between overfitting and underfitting.

Question 40

How is overfitting addressed?

Question 41

What is regularization, and how does it address overfitting?

Question 42

What is the role of the regularization parameter in controlling the trade off between model fit and the model complexity?

Question 43

What is the difference between the cost functions of regularized and unregularized linear regression?

Question 44

What is the difference between the cost functions of regularized and unregularized logistic regression?

Question 45

What is the gradient calculation for regularized linear and logistic regression, and how does it differ from the gradient calculation for unregularized regression?