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Interview Questions on Logistic Regression and Linear Regression

Instructor: Applied AI Course Duration: 30 mins

Revison  Questions

  1. Explain about Logistic regression?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/geometric-intuition-1/)

  2. What is Sigmoid function & Squashing ?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/sigmoid-function-squashing-1/)

  3. Explain about Optimization problem in logistic regression. (https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/mathematical-formulation-of-objective-function-1/)

  4. Expalain Importance of Weight vector in logistic regression.(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/weight-vector-1/)

  5. L2 Regularization: Overfitting and Underfitting.(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/l2-regularization-overfitting-and-underfitting/)

  6. L1 regularization and sparsity. (https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/l1-regularization-and-sparsity/)

  7. What is Probabilistic Interpretation: Gaussian Naive Bayes ?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/probabilistic-interpretation-gaussian-naive-bayes-1/)

  8. Explain about Hyperparameter search: Grid Search and Random Search ?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/hyperparameter-search-grid-search-and-random-search/)

  9. What is Column Standardization.?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/column-standardization/)

  10. Explain about Collinearity of features?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/collinearity-of-features-1/)

  11. Find Train & Run time space and time complexity of Logistic regression?(https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/testrun-time-space-and-time-complexity-1/)

Self Learning:

  1. After analyzing the model, your manager has informed that your regression model is suffering from multicollinearity. How would you check if he’s true? Without losing any information, can you still build a better model?(Self Learning)

  2. What are the basic assumptions to be made for linear regression?(Self Learning)

  3. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?(Self Learning)

  4. When would you use GD over SDG, and vice-versa?(Self Learning)

  5. How do you decide whether your linear regression model fits the data?(Self Learning)

  6. Is it possible to perform logistic regression with Microsoft Excel?(Self Learning)

  7. What is logistic regression? Or State an example when you have used logistic regression recently.(Self learning)

  8. When will you use classification over regression?(Self Learning)

  9. Explain the tradeoff between bias and variance in a regression problem.(Self Learning)

External Resources:

1.https://www.analyticsvidhya.com/blog/2017/08/skilltest-logistic-regression/

2.https://www.listendata.com/2017/03/predictive-modeling-interview-questions.html

3.https://www.analyticsvidhya.com/blog/2017/07/30-questions-to-test-a-data-scientist-on-linear-regression/

4.https://www.analyticsvidhya.com/blog/2016/12/45-questions-to-test-a-data-scientist-on-regression-skill-test-regression-solution/

5. https://www.listendata.com/2018/03/regression-analysis.html

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1.1
Why learn Python?

4 min
1.2
Keywords and identifiers

6 min
1.3
comments, indentation and statements

9 min
1.4
Variables and data types in Python

32 min
1.5
Standard Input and Output

7 min
1.6
Operators

14 min
1.7
Control flow: if else

10 min
1.8
Control flow: while loop

16 min
1.9
Control flow: for loop

15 min
1.10
Control flow: break and continue

10 min
1.11
Python, Anaconda and relevant packages installations

23 min
2.1
Introduction to IRIS dataset and 2D scatter plot

26 min
2.2
3D scatter plot

6 min
2.3
Pair plots

14 min
2.4
Limitations of pair plots

2 min
2.5
Histogram and Introduction to PDF(Probability Density Function)

17 min
2.6
Univariate Analysis using PDF

6 min
2.7
CDF(Cumulative Distribution Function)

15 min
2.8
Mean, Variance and Standard Deviation

17 min
2.9
Median

10 min
2.10
Percentiles and Quantiles

9 min
2.11
IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)

6 min
2.12
Box-plot with Whiskers

9 min
2.13
Violin Plots

4 min
2.14
Summarizing Plots, Univariate, Bivariate and Multivariate analysis

6 min
2.15
Multivariate Probability Density, Contour Plot

9 min
2.16
Exercise: Perform EDA on Haberman dataset

4 min
3.1
Why learn it ?

4 min
3.2
Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector

14 min
3.3
Distance of a point from a Plane/Hyperplane, Half-Spaces

10 min
3.4
Dot Product and Angle between 2 Vectors -

14 min
3.5
Projection and Unit Vector

5 min
3.6
Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane

23 min
3.7
Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)

7 min
3.8
Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)

6 min
3.9
Hyper Cube,Hyper Cuboid

3 min
3.10
Assignment[Optional]

30 min
4.1
Questions & Answers

30 min
5.1
Gaussian/Normal Distribution and its PDF(Probability Density Function)

27 min
5.2
CDF(Cumulative Distribution function) of Gaussian/Normal distribution

11 min
5.3
Co-variance

14 min
6.1
Questions & Answers

30 min
7.1
what is dimensionality reduction?

3 min
7.2
Row Vector and Column Vector

5 min
7.3
How to represent a data set?

4 min
7.4
How to represent a dataset as a Matrix.

7 min
7.5
Data preprocessing: Column Normalization

20 min
7.6
Mean of a data matrix

6 min
7.7
Data preprocessing: Column Standardization

16 min
7.8
Co-variance of a Data Matrix

24 min
7.9
MNIST dataset (784 dimensional)

20 min
7.10
Code to load MNIST Dataset

12 min
8.1
Questions & Answers

30 min
9.1
Why learn PCA?

4 min
9.2
Geometric intuition of PCA

14 min
9.3
Mathematical objective function of PCA

13 min
9.4
Alternative formulation of PCA: Distance minimization

10 min
9.5
Eigen values and Eigen vectors

23 min
9.6
PCA for Dimensionality Reduction and Visualization

10 min
9.7
Visualize MNIST dataset

5 min
9.8
Limitations of PCA

5 min
9.9
PCA Code example using Visualization

19 min
9.10
PCA Code example using non-Visualization

15 min
10.1
What is t-SNE?

7 min
10.2
Neighborhood of a point, Embedding

7 min
10.3
Geometric intuition of t-SNE

9 min
10.4
Crowding Problem

8 min
10.5
How to apply t-SNE and interpret its output

38 min
10.6
t-SNE on MNIST

7 min
10.7
Code example of t-SNE

9 min
11.1
Questions & Answers

30 min
12.1
Questions & Answers

30 min
13.1
Questions & Answers

30 min
14.1
Questions & Answers

30 min
16.1
Questions & Answers

30 min
17.1
Questions & Answers

30 min
18.1
Questions & Answers

30 min
19.1
Questions & Answers

30 min