Introduction
So, What is Machine Learning and AI? Machine learning is a subfield of artificial intelligence that is specialised in teaching machines to understand things without explicitly programming. In the current era of digital change machines learning are the most important factor to making decisions based on data. No matter if you’re a student, or professional it is essential to understand the basics of machine learning and how it operates.
In this post, we’ll provide you with a few machine learning MCQ so that you are able to easily begin to learn about this method of learning. You can also begin your journey in machine learning by starting with machine learning best course.
1. What is Machine Learning and AI?
(A) The independent acquisition of knowledge via manual programs
(B) The acquisition of knowledge by the application of computer programs.
(C) The learning of information through using manual programs.
(D) The self-aware acquisition of knowledge by the application of computer programs
The correct choice is D.
2. Which of the factors affect the performance of the machine learner system does not include?
(A) Effective data structures
(B) Representation scheme used
(C) Training scenario
(D) The type of feedback
Correct choice is A.
3. What of these do not involve different learning methods?
(A) Analogy
(B) Introduction
(C) Memorization
(D) Deduction
The correct option is B.
4. Making a machine learning strategy includes:
(A) Selecting the kind of experience
(B) Selecting the function that is to be taught
(C) The choice of an appropriate representation of the target function
(D) Selecting the function approximation algorithm
(E) All the mentioned
The correct answer is E.
5. What of these isn’t a learning that is supervised?
(A) Naive Bayesian
(B) PCA
(C) Linear Regression
(D) Decision Tree
The correct choice is B.
6. What is the learning algorithm is used for “Facial identities or facial expressions”?
(A) Prediction
(B) Recognition Patterns
(C) Generating Patterns
(D) Recognizing Anomalies
The correct choice is B.
7. Which one of these isn’t a an aspect of symbolic representation in the representation of various functions in Machine Learning?
(A) The Rules of Proportional logic
(B) Hidden-Markov Models (HMM)
(C) First-order logic in predicate first order
(D) Decision Trees
The correct option is B.
8. What strategies can be used to reduce the risk of overfitting when designing decision trees?
(i) Set a maximum height for the tree
(ii) Institute a minimum amount of leaf nodes with samples
(iii) Pruning
(iv) Check that every leaf node is a pure class
(A) All
(B) (i), (ii) and (iii)
(C) (i), (iii), (iv)
(D) None
The correct option is B.
9. What do you mean byPerceptron?
(A) A single-layer feed-forward neural network that includes the capability of pre-processing (B) An neural network with feedback
(C) A double layer auto-associative neural network
(D) An auto-associative neural network
The correct answer is A.
10. What is the purpose of the backpropagation algorithm?
(A) to develop a the learning algorithm for a multilayer feedforward neural network to ensure that the network is taught to understand the mapping in the implicit manner
(B) To design and develop a learning algorithm for a multilayer feedforward neural network
(C) to develop a an algorithm for learning for a single-layer feedforward neural network
(D) The entire previously mentioned
The correct choice is A.
11. A 3-input neuron has the weights 1, 4 , and 3. Transfer function of the neuron is linear, with its constant proportionality equivalent to 3. The inputs are 4,8 and 5, respectively. What is the output?
(A) 139
(B) 153
(C) 612
(D) 160
The correct choice is B.
12. How do you define back propagation?
(A) It’s a different name that is used to describe the curvy function within the perceptron.
(B) It’s the transfer of error through the network , allowing adjustments to the amount of weight in order that the network can be taught
(C) It is a different term used to describe the curvy function of the perceptron.
(D) There is no one of these mentioned
The correct option is B.
13. For a specific learning task when the requirements for error parameter is changed between 0.1 up to 0.01. How many additional examples are required to complete PAC learning?
(A) Same
(B) 2 times
(C) 1000x
(D) Ten times to answer
The correct choice is D.
14. Genetic algorithm is
(A) Technique of Search is used in computing to determine the exact or approximate solutions to the optimisation problem and search issue
(B) Technique of Sorting is used in computing to discover the most accurate or approximate solutions to sort and optimise problem
(C) Both A & B
(D) None of them
The correct answer is A.
16. The space in which the Version is located is:
(A)The Subset that contains all hypothesis is known as the version space in relation towards the space of hypothesis H as well as those of the examples for training because it includes all possible variations of the concept being studied.
(B) The space of versions includes only hypotheses with specificity.
(C) (C) None of the above Answers
The correct answer is A.
17. The feature of ANN that ANN is able to create its own organisation or representation from the information it learns is called
(A)Adaptive Learning
(B) Self Organization
(C) What-If Analysis
(D)Supervised Learning
The correct option is B.
18. What is true about the machine learning kNN algorithm?
(A) It could be used to classify
(B) It is used to perform regression
(C) It is used for classification and regression
The correct choice is C.
19. What can be used to prevent overfitting within a test set?
(A) Overfitting set
(B) Training set
(C) Validation dataset
(D) Evaluation set
The correct choice is C.
20. Which among the following is not a necessary feature of a reinforcement learning solution to a learning problem?
(A) exploration versus exploitation dilemma
(B) trial and error approach to learning
(C) learning based on rewards
(D) representation of the problem as a Markov Decision Process
Correct option is D
Conclusion
The questions above are the basic machine learning mcq. You can also begin your journey in machine learning by starting with machine learning best course. Machine learning is developing rapidly, and new concepts are likely to emerge. Therefore, to stay up the latest, join a community and attend conferences, as well as study research papers and practice machine learning MCQ and do projects If you do this, you will be able to get through the most difficult ML interview.
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