Some interview questions related to Data science
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
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Linkedin
Abhishek Kumar Singh on LinkedIn: Cheatsheets for deep learning
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🤓 ML Cheatsheets!
Supervised Learning: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf
Unsupervised Learning: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf
Deep Learning: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf
ML Tips/Tricks: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf
Stats/Prob: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf
Linear Algebra: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf
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Supervised Learning: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf
Unsupervised Learning: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf
Deep Learning: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf
ML Tips/Tricks: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf
Stats/Prob: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf
Linear Algebra: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf
Save for later, share with tech-circle, stay subscribed for more! 😎
GitHub
stanford-cs-229-machine-learning/en/cheatsheet-supervised-learning.pdf at master · afshinea/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning - afshinea/stanford-cs-229-machine-learning
The coefficient of correlation
Anonymous Quiz
27%
is the square of the coefficient of determination
45%
is the square root of the coefficient of determination
16%
Is same as r square
11%
Can never be negative
Forwarded from DataSpoof- Ask your questions
In regression analysis, the variable that is being predicted is the
Anonymous Quiz
68%
Response variable or dependent variable
24%
Independent variable
4%
Intervening variable
4%
None of these
Sum of squared error can never be
Anonymous Quiz
32%
larger than SST
24%
Smaller than SST
19%
Equal to 1
24%
Equal to 0
In a regression analysis if r squared = 1, then
Anonymous Quiz
34%
SSE must also be equal to one
37%
SSE must be equal to zero
26%
SSE can be any positive value
3%
SSE must be negative
If the coefficient of determination is equal to 1, then the correlation coefficient
Anonymous Quiz
40%
must also be equal to 1
30%
can be either -1 or +1
26%
can be any value between -1 to +1
4%
must be -1
If the correlation coefficient is a positive value, then the slope of the regression line
Anonymous Quiz
56%
must also be positive
28%
can be either negative or positive
8%
can be zero
8%
can not be zero
Sensitivity in confusion matrix is
Anonymous Quiz
18%
True negative rate
39%
True positive rate
34%
Both A and B
9%
None of these
In regression analysis, if the independent variable is measured in kilograms, the dependent variable
Anonymous Quiz
33%
must also be in kilograms
20%
must be in some unit of weight
7%
cannot be in kilograms
40%
can be any units
Overfitting is a major problem for neural networks. Which of the following can help prevent overfitting?
Anonymous Quiz
8%
Retraining on the same data many times
23%
Using a larger learning rate for Backpropagation
52%
Dropping random neurons in each iteration of Backpropagation
17%
Training until you get the smallest training error
Regression line can be drawn in which of the following plots
Anonymous Quiz
7%
Pair plot
36%
Regression plot
4%
Joint plot
52%
All of the above
Which of the following is statements is false
Anonymous Quiz
24%
Boosting combines weak classifiers to output a strong classifier
24%
Adaboost is a generative model as it can generate classifier
32%
Boosting can only improve performance if weak learners can predict better than random chance
21%
None of these
High entropy means that the partitions in classification
Anonymous Quiz
27%
Pure
44%
Not pure
18%
Useful
12%
Useless
Which of the following is a way to avoid local minima?
Anonymous Quiz
19%
Increase the learning rate
23%
Use momentum and Adaptive learning
12%
Add some noise while updating weights
46%
All the above
Curated papers, articles, and blogs on data science & machine learning in production.
https://github.com/eugeneyan/applied-ml
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GitHub
GitHub - eugeneyan/applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production. - eugeneyan/applied-ml