Machine Learning can be defined as the study of computer based algorithms or a method of data analysis that have the ability to improve automatically by experience and the use of the data.It is a part of Artificial Intelligence and is based on the concept of learning from data, identifying patterns and making decisions.
Question 1: Which of the following is not a learning method?
A) Analogy
B) Deduction
C) Memorization
D) Introduction
Answer: Introduction
Explanation: Different valid learning methods are:
Analogy
Deduction
Memorization
Question 2: Which of the following is not a level of knowledge in language understanding?
A) Syntactic
B) Pragmatic
C) Semantic
D) Empirical
Answer: Empirical
Explanation: Following are the valid level of knowledge in language understanding:
Syntactic
Pragmatic
Semantic
Morphological
Pragmatic
Discourse
World
Pattern matching, etc
Question 3: Which of the following is a valid way of handling corrupted or missing data in a dataset?
A) Unique values can be assigned a new category
B) Drop the corrupted/missing values (rows or columns)
C) Replace the missing values from other values (rows or columns) already present in the table.
D) All of these
Answer: All of these
Explanation: Below are the valid ways of handling corrupted or missing data in the dataset:
Removing the values (rows or columns) which are corrupted or missing.
A unique category can be assigned to the missing or corrupted data.
Missing values can be replaced by other values which are already present in the dataset.
Question 4: Which of the following machine learning algorithms works well with labelled data?
A) Regression
B) Association
C) Clustering
D) None of these
Answer: Regression
Explanation: Regression: the way regression is, it predicts the value of dependent variables using independent variables. So, it works well with labelled data.
Question 5: Which of the following can be a valid real world use of machine learning?
A) Chat bots
B) Weather prediction
C) Digital assistants
D) All of the above
Answer: All of the above
Explanation: Chat bots use natural language and training data
Weather prediction uses linear regression for predicting weather
Digital assistant use natural language processing and speech recognition
Question 6: Machine Language uses which of the following algorithms?
A) K-Nearest neighbors
B) Naive Bayes
C) Support Vector Machines
D) All of the above
Answer: All of the above
Explanation: K-Nearest neighbors is a supervised learning based machine learning algorithm
Naive Bayes is a supervised learning based machine learning algorithm
Support Vector Machines is a supervised learning model used for classification and regression analysis.
Question 7: Keyword normalization uses which of the following techniques?
A) Stemming
B) Lemmatization
C) Both A and B
D) None of these
Answer: Both A and B
Explanation: Stemming can be defined as a process of removing additional unnecessary characters (usually a suffix) from a word and reduce it to a root
Lemmatization is a text processing technique which is used in Natural Language Processing and Machine Learning
Question 8: Which of the following can be grouped as supervised learning problems?
A) Regression
B) Classification
C) Both of these
D) None of these
Answer: Both of these
Explanation: Regression can be defined as method which is used to predict continuous outcome
Classification can be defined as a predictive model which can predict class for a given input
Question 9: Which of the following can be grouped as supervised learning problems?
A) Association
B) Clustering
C) Both of these
D) None of these
Answer: Both of these
Explanation: Association can be defined as a method of finding relations in large databases between variables.
Clustering is the process of automatically discovering natural grouping in data.
Question 10: Which machine learning model uses rewards and feedback strategy?
A) Unsupervised Learning
B) Supervised Learning
C) Reinforcement Learning
D) None of these
Answer: Reinforcement Learning
Explanation: Reinforcement Learning includes training of models in machine learning using reward and feedback strategy.