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Machine Learning: 

 

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.

 

 

Machine Learning Sample Questions:

 

 

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

 

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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

 

 

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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.

 

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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.

 

 

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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 

 

 

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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.

 

 

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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 

 

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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

 

 

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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.

 

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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.


 

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