Which Scipy package can be used for standard continuous and discrete probability distributions?
Anonymous Quiz
15%
Scipy.integrate
68%
Scipy.stats
8%
Scipy.signal
10%
Scipy.optimize
Which Scipy package can be used to solve differential equations?
Anonymous Quiz
50%
Scipy.integrate
26%
Scipy.linalg
12%
Scipy.sparse
13%
Scipy.stats
Which of the following is incorrect code to replace na values with 0 in data?
Anonymous Quiz
36%
data.replace(0,na.nan)
31%
data.replace(na.nan,0)
33%
data.fillna(0)
Data Science & Machine Learning
Which of the following is incorrect code to replace na values with 0 in data?
Just a correction here
"np.nan" instead of "na.nan"
Hope you all are aware that mostly we
import numpy as np
"np.nan" instead of "na.nan"
Hope you all are aware that mostly we
import numpy as np
❤1
Which of the following is correct code to generate 20 normally distributed random numbers?
import numpy as np
import numpy as np
Anonymous Quiz
28%
np.random(20)
39%
np.random.rand(20)
33%
np.random.randn(20)
Which function is used to randomly reorder a Series or rows in Dataframe?
Anonymous Quiz
24%
np.random
39%
np.random.rand
23%
np.random.permutation
13%
np.random.combination
👍2❤1
You should definitely check the documentation of numpy and scikit learn if you are facing difficulty in answering above questions
https://numpy.org/doc/stable/
https://scikit-learn.org/
Share the channel link with your friends to help them too in learning data science and machine learning
👇👇
http://t.me/datasciencefun
https://numpy.org/doc/stable/
https://scikit-learn.org/
Share the channel link with your friends to help them too in learning data science and machine learning
👇👇
http://t.me/datasciencefun
❤1
Which website is best to learn Programming and Data science according to you?
Anonymous Poll
27%
Udemy
5%
Udacity
3%
Edx
18%
Coursera
8%
Datacamp
1%
Eduonix
7%
Freecodecamp
13%
Kaggle
2%
Dataquest
17%
Can't decide
👍2
Learning and Practicing SQL: Resources and Platforms
1. https://sqlbolt.com/
2. https://sqlzoo.net/
3. https://www.codecademy.com/learn/learn-sql
4. https://www.w3schools.com/sql/
5. https://www.hackerrank.com/domains/sql
6. https://www.windowfunctions.com/
7. https://selectstarsql.com/
8. https://quip.com/2gwZArKuWk7W
9. https://leetcode.com/problemset/database/
10. http://thedatamonk.com/
1. https://sqlbolt.com/
2. https://sqlzoo.net/
3. https://www.codecademy.com/learn/learn-sql
4. https://www.w3schools.com/sql/
5. https://www.hackerrank.com/domains/sql
6. https://www.windowfunctions.com/
7. https://selectstarsql.com/
8. https://quip.com/2gwZArKuWk7W
9. https://leetcode.com/problemset/database/
10. http://thedatamonk.com/
Which area of machine learning is concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward?
Anonymous Quiz
25%
Supervised Learning
67%
Reinforcement Learning
8%
Unsupervised Learning
What is gradient boosting trees?
Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
❤1
Which of the following is a supervised Learning technique?
Anonymous Quiz
18%
Clustering
75%
Classification
7%
Association
Which training set is provided for supervised learning?
Anonymous Quiz
19%
Only inputs
5%
Only outputs
76%
inputs and correct outputs
Which of the following is correct for unsupervised learning?
Anonymous Quiz
11%
models are supervised using training dataset
74%
models itself find the hidden patterns and insights from the given data
15%
models cannot itself find the hidden patterns and insights from the given data
Which type of problems can be solved with Unsupervised learning?
Anonymous Quiz
15%
Classification problems
76%
Customer segmentation (Clustering)
9%
Linear Regression
Supervised learning requires a training set to teach models to yield the desired output. Training dataset includes inputs and correct outputs, which allow the desired model to learn over time. The algorithm also measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.
👍5
What is the ROC curve? When to use it?
ROC stands for Receiver Operating Characteristics. The diagrammatic representation that shows the contrast between true positive rate vs false positive rate.
It is used when we need to predict the probability of the binary outcome.
ROC stands for Receiver Operating Characteristics. The diagrammatic representation that shows the contrast between true positive rate vs false positive rate.
It is used when we need to predict the probability of the binary outcome.
❤1
What’s the interpretation of the bias term in linear models?
Bias is simply, a difference between predicted value and actual/true value. It can be interpreted as the distance from the average prediction and true value i.e. true value minus mean(predictions). But dont get confused between accuracy and bias.
Bias is simply, a difference between predicted value and actual/true value. It can be interpreted as the distance from the average prediction and true value i.e. true value minus mean(predictions). But dont get confused between accuracy and bias.
❤1
Data scientists often tune bias values to
Anonymous Quiz
10%
Reset the inputs
50%
better fit the data
33%
Improve the quality of data
6%
None of the above
What kind of problems neural nets can solve?
Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.
Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.