National Level Quiz On Machine Learning || Free Quiz Certificate || Quiz Free Certificate
Hello everyone most welcome on my blog. Today in this post I will tell you about a new and most important free certification quiz. This is an online free quiz certification course on "Machine Learning". In this free certification exam anyone can participate. There is no any registration fee. There is total 25 MCQ. There is no any negative marking. There is no any time limit. All the questions are asked in this exam are based on basic machine learning . All the questions are same in this exam but questions order is different .
After completion of this exam and scoring more than 60 % each participants will receive a certificate on your email id.
Here You can sell all the questions which are asked in this Exam
1. It involves dividing the input values by the range (i.e. the maximum value minus the minimum value) of the input variable, resulting in a new range of just .
Feature scaling
Mean normalization
Correlating features
Covariance
2. Method to assure that our backpropagation works as intended
Gradient boosting
Gradient checking
Regularization
Gradient descent
3. You’re running a company, and you want to develop learning algorithms to address each of two problems.
Problem 1:You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months.
Problem 2: You’d like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised. Should you treat these as classification or as
regression problems?
Treat both as classification problems.
Treat problem 1 as a classification problem, problem 2 as a regression problem.
Treat problem 1 as a regression problem, problem 2 as a classification problem.
Treat both as regression problems.
4. Techniques to fix high variance
Trying smaller sets of features
Getting more training examples
Adding features
Adding polynomial features
Decreasing regularization parameter
Increasing regularization parameter
Free Online Certification Courses on Artificial Intelligence and Machine Learning Using Python
5. If the difference between human-level error and the training error is bigger than the difference between the training error and the development error. The focus should be on
bias enhancing
bias reducing
variance enchancing
variance reducing
6. a system design property that assures that modifying an instruction or a component of an algorithm will not create or propagate side effects to other components of the system
use case analysis
waterfall model
orthogonality
optimization
7. The field of study that gives computers the ability to learn without being explicitly learned.
Neural networks
Recurrent network
Convolutional Networks
Machine Learning
Dynamic Programming
8. When the form of our hypothesis function maps poorly to the trend of the data
Underfitting
Overfitting
Correlating features
Covariance
Low bias
High bias
9. Of the following examples, which would you address using an unsupervised learning algorithm? (Check all that apply.)
Given email labeled as spam/not spam, learn a spam filter.
Given a set of news articles found on the web, group them into set of articles about the same story
Given a database of customer data, automatically discover market segments and group customers into different market segments
Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not
10. Over time, as you keep training the algorithm, maybe bigger and bigger models on more and more data,
the performance approaches but never surpasses some theoretical limit, which is called the
landmark error
Goldilocks zone
Bayes error
Distribution error
Bernoullis error
11. You train a system, and its errors are as follows (error = 100%-Accuracy): Training set error 4.0% Dev set error 4.5% This suggests that one good avenue for improving performance is to train a bigger network so as to drive down the 4.0% training error. Do you agree?
Yes, because having 4.0% training error shows you have high bias.
Yes, because this shows your bias is higher than your variance.
No, because this shows your variance is higher than your bias.
No, because there is insufficient information to tell.
12. Techniques to fix high bias
Trying smaller sets of features
Getting more training examples
Adding features
Adding polynomial features
Decreasing regularization parameter
Increasing regularization parameter
13. It involves subtracting the average value for an input variable from the values for that input variable resulting in a new average value for the input variable of just zero
Feature scaling
Mean normalization
Correlating features
Covariance
14. If the difference between training error and the development error is bigger than the difference between the human-level error and the training error. The focus should be on
bias enhancing
bias reducing
variance enchancing
variance reducing
15. Structuring your data Before implementing your algorithm, you need to split your data into train/dev/test sets. Which of these do you think is the best choice?
Train - 3,333,334; Dev - 3,333,333 ; Test - 3,333,333
Train - 9,500,000; Dev - 250,000 ; Test - 250,000
16. Having one neural network do simultaneously several tasks
Transfer learning
Fine tuning
End-to-end deep learning
Weight shifting
Gradient boosting
Data synthesis
Multi-task learning
17. The λ, or lambda, is the regularization parameter. If lambda is chosen to be too large, it may
cause underfitting
cause overfitting
no effect
18. we are given an unlabeled data set and we would like to have an algorithm automatically group the data into coherent subsets or into coherent clusters for us
Classification
Regression
Clustering
Data mining
Exploratory data analysis
19. Your goal is to detect road signs (stop sign, pedestrian crossing sign, construction ahead sign) and traffic signals (red and green lights) in images. The goal is to recognize which of these objects appear in each image. You plan to use a deep neural network with ReLU units in the hidden layers. For the output layer, a SoftMax activation would be a good choice for the output layer because this is a multi-task learning problem. True/False?
true
False
20. A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E." Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting?
Classify emails as spam or not spam.
Watching you label emails as spam or not spam.
The number (or fraction) of emails correctly classified as spam/not spam.
None of the above, this is not a machine learning algorithm
21. You are carrying out error analysis and counting up what errors the algorithm makes. Which of these datasets do you think you should manually go through and carefully examine, one image at a time?
500 randomly chosen images
10,000 randomly chosen images
10,000 images on which the algorithm made a mistake
500 images on which the algorithm made a mistake
22. simplification of a processing or learning systems into one neural network
Transfer learning
Fine tuning
Weight shifting
Gradient boosting
End-to-end deep learning
Data synthesis
Multi-task learning
23. One of the most powerful ideas in deep learning is that sometimes you can take knowledge the neural network has learned from one task and apply that knowledge to a separate task
Transfer learning
Fine tuning
End-to-end deep learning
Weight shifting
Gradient boosting
Data synthesis
Multi-task learning
24. The main options to address the issue of overfitting
Reduce the number of features
Increase the number of features
Regularization
No Regularization
25. When a supervised learning system is design, these are the which assumptions that needs to be true
Fit training set well in cost function
Use of a bigger development set
Fit development set well in cost function
Regularization or using bigger training set
Fit test set well on cost function
For Answers of This Quiz Competition Click On The Following Video
0 Comments