Machine Learning MCQ-Part 1

1. What is true about Machine Learning?

A. Machine Learning (ML) is that field of computer science
B. ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method.
C. The main focus of ML is to allow computer systems learn from experience without being explicitly programmed or human intervention.
D. All of the above

Ans : D

Explanation: All statement are true about Machine Learning.

2. ML is a field of AI consisting of learning algorithms that?

A. Improve their performance
B. At executing some task
C. Over time with experience
D. All of the above

Ans : D

Explanation: ML is a field of AI consisting of learning algorithms that : Improve their performance (P), At executing some task (T), Over time with experience (E).

3.  p → 0q is not a?

A. hack clause
B. horn clause
C. structural clause
D. system clause

Ans : B

Explanation: p → 0q is not a horn clause.

4. The action _______ of a robot arm specify to Place block A on block B.

A. STACK(A,B)
B. LIST(A,B)
C. QUEUE(A,B)
D. ARRAY(A,B)

Ans : A

Explanation: The action 'STACK(A,B)' of a robot arm specify to Place block A on block B.

5. A__________ begins by hypothesizing a sentence (the symbol S) and successively predicting lower level constituents until individual preterminal symbols are written.

A. bottow-up parser
B. top parser
C. top-down parser
D. bottom parser

Ans : C

Explanation: A top-down parser begins by hypothesizing a sentence (the symbol S) and successively predicting lower level constituents until individual preterminal symbols are written.

6. A model of language consists of the categories which does not include ________.

A. System Unit
B. structural units.
C. data units
D. empirical units

Ans : B

Explanation: A model of language consists of the categories which does not include structural units.

7. Different learning methods does not include?

A. Introduction
B. Analogy
C. Deduction
D. Memorization

Ans : A

Explanation: Different learning methods does not include the introduction.

8. The model will be trained with data in one single batch is known as ?

A. Batch learning
B. Offline learning
C. Both A and B
D. None of the above

Ans : C

Explanation: we have end-to-end Machine Learning systems in which we need to train the model in one go by using whole available training data. Such kind of learning method or algorithm is called Batch or Offline learning.

9. Which of the following are ML methods?

A. based on human supervision
B. supervised Learning
C. semi-reinforcement Learning
D. All of the above

Ans : A

Explanation: The following are various ML methods based on some broad categories : Based on human supervision, Unsupervised Learning, Semi-supervised Learning and Reinforcement Learning

10. In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called ?

A. mini-batches
B. optimizedparameters
C. hyperparameters
D. superparameters

Ans : C

Explanation: In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called hyperparameters.

11. Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

A. Decision Tree
B. Regression
C. Classification
D. Random Forest

Ans : D

Explanation: The Radom Forest algorithm builds an ensemble of Decision Trees, mostly trained with the bagging method.

12. To find the minimum or the maximum of a function, we set the gradient to zero because:

A. The value of the gradient at extrema of a function is always zero
B. Depends on the type of problem
C. Both A and B
D. None of the above

Ans : A

Explanation: The gradient of a multivariable function at a maximum point will be the zero vector of the function, which is the single greatest value that the function can achieve.

13. Which of the following is a disadvantage of decision trees?

A. Factor analysis
B. Decision trees are robust to outliers
C. Decision trees are prone to be overfit
D. None of the above

Ans : C

Explanation: Allowing a decision tree to split to a granular degree makes decision trees prone to learning every point extremely well to the point of perfect classification that is overfitting.

14. How do you handle missing or corrupted data in a dataset?

A. Drop missing rows or columns
B. Replace missing values with mean/median/mode
C. Assign a unique category to missing values
D. All of the above

Ans : D

Explanation: All of the above techniques are different ways of imputing the missing values.

15. When performing regression or classification, which of the following is the correct way to preprocess the data?

A. Normalize the data -> PCA -> training
B. PCA -> normalize PCA output -> training
C. Normalize the data -> PCA -> normalize PCA output -> training
D. None of the above

Ans : A

Explanation: You need to always normalize the data first. If not, PCA or other techniques that are used to reduce dimensions will give different results.

16. Which of the following statements about regularization is not correct?

A. Using too large a value of lambda can cause your hypothesis to underfit the data.
B. Using too large a value of lambda can cause your hypothesis to overfit the data
C. Using a very large value of lambda cannot hurt the performance of your hypothesis.
D. None of the above

Ans : D

Explanation: A large value results in a large regularization penalty and therefore, a strong preference for simpler models, which can underfit the data.

17. Which of the following techniques can not be used for normalization in text mining?

A. Stemming
B. Lemmatization
C. Stop Word Removal
D. None of the above

Ans : C

Explanation: Lemmatization and stemming are the techniques of keyword normalization.

18. In which of the following cases will K-means clustering fail to give good results?
1) Data points with outliers
2) Data points with different densities
3) Data points with nonconvex shapes

A. 1 and 2
B. 2 and 3
C. 1 and 3
D. All of the above

Ans : D

Explanation: K-means clustering algorithm fails to give good results when the data contains outliers, the density spread of data points across the data space is different, and the data points follow nonconvex shapes.

19. Which of the following is a reasonable way to select the number of principal components "k"?

A. Choose k to be the smallest value so that at least 99% of the varinace is retained.
B. Choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer).
C. Choose k to be the largest value so that 99% of the variance is retained.
D. Use the elbow method.

Ans : A

Explanation: This will maintain the structure of the data and also reduce its dimension.

20. What is a sentence parser typically used for?

A. It is used to parse sentences to check if they are utf-8 compliant.
B. It is used to parse sentences to derive their most likely syntax tree structures.
C. It is used to parse sentences to assign POS tags to all tokens.
D. It is used to check if sentences can be parsed into meaningful tokens.

Ans : B

Explanation: Sentence parsers analyze a sentence and automatically build a syntax tree.

FAQ:

1. How do I start a career in machine learning?

There is no fixed or definitive guide through which you can start your machine learning career. The first step is to understand the basic principles of the subject and learn a few key concepts such as algorithms and data structures, coding capabilities, calculus, linear algebra, statistics. The next step would be to take up a ML course, or read the top books for self-learning. You can also work on projects to get a hands-on experience.

2. What is the best way to learn machine learning?

Any way that suits your style of learning can be considered as the best way to learn. Different people may enjoy different methods. Some of the common ways would be through taking up a Machine Learning Course, watching YouTube videos, reading blogs with relevant topics, read books which can help you self-learn.

3. What degree do you need for machine learning?

Most hiring companies will look for a masters or doctoral degree in the relevant domain. The field of study includes computer science or mathematics. But having the necessary skills even without the degree can help you land a ML job too.

4. How do you break into machine learning?

The most common way to get into a machine learning career is to acquire the necessary skills. Learn programming languages such as C, C++, Python, and Java. Gain basic knowledge about various ML algorithms, mathematical knowledge about calculus and statistics. This will help you go a long way.

5. How difficult is machine learning?

Machine Learning is a vast concept that contains a lot different aspects. With the right guidance and with consistent hard-work, it may not be very difficult to learn. It definitely requires a lot of time and effort, but if you’re interested in the subject and are willing to learn, it won’t be too difficult.

6. What is machine learning for beginners?

Machine Learning for beginners will consist of the basic concepts such as types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning). Each of these types of ML have different algorithms and libraries within them, such as, Classification and Regression. There are various classification algorithms and regression algorithms such as Linear Regression. This would be the first thing you will learn before moving ahead with other concepts.

7. What level of math is required for machine learning?

You will need to know statistical concepts, linear algebra, probability, Multivariate Calculus, Optimization. As you go into the more in-depth concepts of ML, you will need more knowledge regarding these topics.

8. Does machine learning require coding?

Programming is a part of Machine Learning. It is important to know programming languages such as Python.