AI/ML
AI & Machine Learning Mastery Quiz
Test your knowledge in artificial intelligence and machine learning with this engaging quiz designed for enthusiasts and professionals alike. From foundational concepts to advanced algorithms, challenge yourself and see how well you understand the world of AI/ML.
- 20 carefully crafted questions
- Covers various topics including algorithms, statistics, and practical applications
- Perfect for learners at all levels
1)Study the following program: print(int(6 == 6.0) * 3 + 4 % 5) What will be the output of this program?
23
22
7
10
2)What kind of learning algorithm for "Facial identities or facial expressions"?
(A) Prediction
(B) Recognition Patterns
(C) Generating Patterns
(D) Recognizing Anomalies
3)Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?
A)Decision Tree
B)Random Forest
C)Regression
D)Classification
4) The Central Limit Theorem says that the mean of the sampling distribution of the sample means is
A)close to the population mean if the sample size is large.
B)Close to Median of population
C)exactly equal to the population mean
D)None of the above
5)If machine learning model output doesn’t involves target variable then that model is called as_______________
A)predictive model
B)descriptive model
C)reinforcement learning
D)all of the above
6)Which of the following is/are one of the important step(s) to pre-process the text in NLP based projects? 1.Stemming 2.Stop word removal 3.Object Standardization
A) 1 and 2
B) 1 and 3
C) 2 and 3
D) 1,2 and 3
7)What does "Naive" in naive classifier refers to?
A)Strong independence assumption between the features/variable.
B)dependence assumption between the features/variable.
C)Strong independence assumption between the features and target variable
D)None
9) Which of the following metrics can be used for evaluating regression models? i) R Squared ii) Adjusted R Squared iii) F Statistics iv) RMSE / MSE / MAE
A) ii and iv
B) I and ii
C) ii, iii and iv
D) I, ii, iii and iv
10)Impact of high variance on the training Data set ?
A)underfitting
B)overfitting
C)both underfitting & overfitting
D)d)depends upon the dataset
11) A Pearson correlation between two variables is zero but, still their values can still be related to each other.
True
False
12)Some telecommunication company wants to segment their customers into distinct groups ,this is an example of_______________
A)supervised learning
B)unsupervised learning
C)NLP
D)reinforcement learning
13)11) Which of the following statement(s) is / are true for Gradient Decent (GD) and Stochastic Gradient Decent (SGD)? 1. In GD and SGD, you update a set of parameters in an iterative manner to minimize the error function. 2. In SGD, you have to run through all the samples in your training set for a single update of a parameter in each iteration. 3. In GD, you either use the entire data or a subset of training data to update a parameter in each iteration.
A) Only 1
B) Only 2
C) Only 3
D) 1 and 2
E) 1,2 and 3
14)Application of Machine learning is __________.
A)email filtering
B)sentimental analysis
C)face recognition
D)Text classification
E) Trace the Location
F) All the above
15)PCA is_________________
A)backward feature selection
b)forward feature selection
C)feature extraction
D)None of these
17)The Face Recognition system is based on?
A) Strong Artificial Intelligence approach
B) Weak Artificial Intelligence approach
C) Cognitive Artificial Intelligence approach
D) Applied Artificial Intelligence approach
18) Which of the following is not a supervised learning?
A)k-Means
B)Naive Bayesian
C)Linear Regression
D)Decision Tree Answer
E)SVM
F)K-NN
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