Top 101+ Introduction to Artificial Intelligence Class 10 MCQ

Teachers and Examiners (CBSESkillEduction) collaborated to create the Introduction to Artificial Intelligence Class 10 MCQ. All the important MCQs are taken from the NCERT Textbook Artificial Intelligence ( 417 ) class X.

Introduction to Artificial Intelligence Class 10 MCQ

introduction to artificial intelligence class 10 mcq

1. What is the full form of “AI”?
a. Artificially Intelligent
b. Artificial Intelligence 
c. Advanced Intelligence
d. Artificially Intelligence

Show Answer ⟶
b. Artificial Intelligence

2. What is Artificial Intelligence?
a. The goal of the field of artificial intelligence is to increase human intelligence.
b. The field of artificial intelligence helps to strengthen security.
c. Artificial Intelligence is a field that aims to develop intelligent machines 
d. None of the above

Show Answer ⟶
c. Artificial Intelligence is a field that aims to develop intelligent machines

3. Who is the inventor of Artificial Intelligence?
a. John McCarthy 
b. Andrew Ng
c. Geoffrey Hinton
d. None of the above

Show Answer ⟶
a. John McCarthy

4. Which one of the following is an artificial intelligence subfield?
a. Machine Learning 
b. Stack Developer
c. Network Design
d. None of the above

Show Answer ⟶
a. Machine Learning

5. What is the goal of Artificial Intelligence?
a. To extract scientific causes
b. To solve real-world problems 
c. To solve artificial problems
d. None of the above

Show Answer ⟶
b. To solve real-world problems

6. Humans have been developing machines which can make their lives easier?
a. AI Based Machine 
b. AI based TV
c. AI based Washing Machine
e. None of the above

Show Answer ⟶
a. AI Based Machine

7. AI applications used in Mobile phones and Mobile phones help us in _______________.
a. Navigation
b. Recommend the Songs
c. Which movies you can watch according to you like
d. All of the above 

Show Answer ⟶
d. All of the above

8. __________ is something which is man-made, which does not occur naturally.
a. Artificial 
b. Intelligence
c. Artificial Intelligence
d. None of the above

Show Answer ⟶
a. Artificial

9. ____________ is the ‘ability to perceive or infer information, and to retain it as knowledge to be applied towards adaptive behaviors with an environment or context.’
a. Artificial
b. Intelligence 
c. Artificial Intelligence
d. None of the above

Show Answer ⟶
b. Intelligence

10. What are the different intelligences available in AI?
a. Mathematical Logic Intelligence
b. Musical Intelligence
c. Linguistic Intelligence
d. All of the above 

Show Answer ⟶
d. All of the above

11. A person’s ability to regulate, measure, and understand numerical symbols, abstraction and logic.
a. Mathematical Logical Reasoning 
b. Linguistic Intelligence
c. Spatial Visual Intelligence
d. Kinesthetic Intelligence

Show Answer ⟶
a. Mathematical Logical Reasoning

12. Language processing skills both in terms of understanding or implementation in writing or verbally.
a. Mathematical Logical Reasoning
b. Linguistic Intelligence 
c. Spatial Visual Intelligence
d. Kinesthetic Intelligence

Show Answer ⟶
b. Linguistic Intelligence

13. It is defined as the ability to perceive the visual world and the relationship of one object to another.
a. Mathematical Logical Reasoning
b. Linguistic Intelligence
c. Spatial Visual Intelligence 
d. Kinesthetic Intelligence

Show Answer ⟶
c. Spatial Visual Intelligence

14. Ability that is related to how a person uses his limbs in a skilled manila.
a. Mathematical Logical Reasoning
b. Linguistic Intelligence
c. Spatial Visual Intelligence
d. Kinesthetic Intelligence 

Show Answer ⟶
d. Kinesthetic Intelligence

15. As the name suggests, this intelligence is about a person’s ability to recognize and create sounds, rhythms, and sound patterns.
a. Mathematical Logical Reasoning
b. Musical Intelligence 
c. Spatial Visual Intelligence
d. Kinesthetic Intelligence

Show Answer ⟶
b. Musical Intelligence

16. Describes how high the level of self-awareness someone has is. Starting from realizing weakness, strength, to his own feelings.
a. Mathematical Logical Reasoning
b. Linguistic Intelligence
c. Intrapersonal Intelligence 
d. Kinesthetic Intelligence

Show Answer ⟶
c. Intrapersonal Intelligence

17. An additional category of intelligence relating to religious and spiritual awareness.
a. Mathematical Logical Reasoning
b. Linguistic Intelligence
c. Spatial Visual Intelligence
d. Existential Intelligence 

Show Answer ⟶
d. Existential Intelligence

18. An additional category of intelligence relating to the ability to process information on the environment around us.
a. Naturalist Intelligence 
b. Linguistic Intelligence
c. Spatial Visual Intelligence
d. Kinesthetic Intelligence

Show Answer ⟶
a. Naturalist Intelligence

19. Interpersonal intelligence is the ability to communicate with others by understanding other people’s feelings & influence of the person.
a. Mathematical Logical Reasoning
b. Linguistic Intelligence
c. Interpersonal Intelligence 
d. Kinesthetic Intelligence

Show Answer ⟶
c. Interpersonal Intelligence

20. We may define intelligence as _____________.
a. Ability to interact with the real world
b. Reasoning and planning
c. Learning and adaptation
d. All of the above 

Show Answer ⟶
d. All of the above

21. In the term intelligence which one is the crucial part of ____________.
a. Decision Making 
b. Manual Learning
c. Both a) and b)
d. None of the above

Show Answer ⟶
a. Decision Making

22. _________ depends upon the availability of information and how we experience and understand it.
a. Decision making 
b. Manual Machine
c. Both a) and b)
d. None of the above

Show Answer ⟶
a. Decision making

23. When a machine possesses the ability to mimic human traits, i.e., make decisions, predict the future, learn and improve on its own, it is said to have ______________.
a. Artificial Intelligence 
b. Data Science
c. Data Integrated
d. None of the above

Show Answer ⟶
a. Artificial Intelligence

24. ___________ is artificially intelligent when it can accomplish tasks by itself – collect data, understand it, analyze it, learn from it, and improve it.
a. Data
b. Machine 
c. Information
d. None of the above

Show Answer ⟶
b. Machine

25. Which one of the following is Application of Artificial Intelligence.
a. Google Search Engine
b. Cortana, Google Assistant & Siri
c. Amazon
d. All of the above 

Show Answer ⟶
d. All of the above

26. AI is also integrated to Social media platforms which helps to _______________.
a. Customized notification
b. Online shopping details
c. Auto-create playlist according to the requests
d. All of the above 

Show Answer ⟶
d. All of the above

27. Which one is the example of humanoid smart devices _____________.
a. Sophia
b. Ocean One
c. Atlas
d. All of the above 

Show Answer ⟶
d. All of the above

28. Which one is not an AI based application.
a. Washing Machine 
b. Television
c. Both a) and b)
d. None of the above

Show Answer ⟶
c. Both a) and b)

29. What is the KWLH chart?
a. Graphic Organizer
b. Tracks what a student knows
c. How a student will find the information
d. All of the above 

Show Answer ⟶
d. All of the above

30. What do you mean by KWLH chart?
a. What I know? & What I Want to Know?
b. What have I learned?
c. How I learnt this?
d. All of the above 

Show Answer ⟶
d. All of the above

31. ML stands for __________.
a. Machine Learning 
b. Main Learning
c. Mechanical Learning
d. None of the above

Show Answer ⟶
a. Machine Learning

32. DL stands for ___________.
a. Data Learning
b. Deep Learning 
c. Domain Learning
d. None of the above

Show Answer ⟶
b. Deep Learning

33. Which one is part of Artificial Intelligence?
a. Machine Learning
b. Deep Learning
c. Both a) and b) 
d. None of the above

Show Answer ⟶
c. Both a) and b)

34. _____________ is a subset of Artificial Intelligence which enables machines to improve at tasks with experience (data).
a. Deep Learning
b. Machine Learning 
c. Artificial Intelligence
d. None of the above

Show Answer ⟶
b. Machine Learning

35. ___________ enable machines to learn by themselves using the provided data and make accurate Predictions/ Decisions.
a. Deep Learning
b. Machine Learning 
c. Artificial Intelligence
d. None of the above

Show Answer ⟶
b. Machine Learning

36. ___________ machine is trained with huge amounts of data which helps it in training itself around the data. Such machines are intelligent enough to develop algorithms for themselves.
a. Deep Learning 
b. Machine Learning
c. Artificial Intelligence
d. None of the above

Show Answer ⟶
a. Deep Learning

37. Which one is the correct domain of AI?
a. Data Science
b. Computer Vision
c. Natural Language Processing
d. All of the above 

Show Answer ⟶
d. All of the above

38. __________ is a domain of AI related to data systems and processes, in which the system collects
numerous data, maintains data sets and derives meaning/sense out of them.
a. Data Science 
b. Computer Vision
c. Natural Language Processing
d. All of the above

Show Answer ⟶
a. Data Science

39. __________ , is a domain of AI that depicts the capability of a machine to get and analyze visual information and afterwards predict some decisions about it.
a. Data Science
b. Computer Vision 
c. Natural Language Processing
d. All of the above

Show Answer ⟶
b. Computer Vision

40. Give an example of a Computer Vision program?
a. Self-Driving cars
b. Face lock in Smartphones
c. Both a) and b) 
d. None of the above

Show Answer ⟶
c. Both a) and b)

41. __________is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language.
a. Natural Language Processing 
b. New Language Processing
c. Next Generation Language Processing
d. None of the above

Show Answer ⟶
a. Natural Language Processing

42. ___________ attempts to extract information from the spoken and written word using algorithms.
a. Natural Language Processing 
b. New Language Processing
c. Next Generation Language Processing
d. None of the above

Show Answer ⟶
a. Natural Language Processing

43. Example of Natural Language Processing?
a. Email filters
b. Smart Assistants
c. Both a) and b) 
d. None of the above

Show Answer ⟶
c. Both a) and b)

44. The purpose of __________ is to do jobs that are either too time-consuming or boring for people.
a. Devices
b. Machine 
c. Gadgets
d. None of the above

Show Answer ⟶
b. Machine

45. When something is man-made, it doesn’t happen naturally and is known as _________.
a. Artificial 
b. Intelligence
c. Naturally
d. None of the above

Show Answer ⟶
a. Artificial

46. Mr. Ajit is capable of making calculations and using logic in their project. This skill relates to _________.
a. Linguistic Intelligence
b. Mathematical Intelligence 
c. Spatial Visual Intelligence
d. Existential Intelligence

Show Answer ⟶
b. Mathematical Intelligence

47. The ability to notice or interpret the information and retain it as knowledge to apply toward adaptive behaviors within an environment or context is referred to as ___________.
a. Artificial
b. Intelligence 
c. Naturally
d. None of the above

Show Answer ⟶
b. Intelligence

48. Which of the following intelligences is related to the capacity for religious and spiritual awareness?
a. Linguistic Intelligence
b. Mathematical Intelligence
c. Spatial Visual Intelligence
d. Existential Intelligence 

Show Answer ⟶
d. Existential Intelligence

49. When a machine can replicate human abilities like making judgments, predicting the future, learning, and improving on its own known as ___________.
a. Artificial Intelligence 
b. Emotional Intelligence
c. Knowledge Intelligence
d. None of the above

Show Answer ⟶
a. Artificial Intelligence

50. The capacity to interact with others through recognizing their emotions and control over them is referred to as __________.
a. Linguistic Intelligence
b. Mathematical Intelligence
c. Spatial Visual Intelligence
d. Interpersonal Intelligence 

Show Answer ⟶
d. Interpersonal Intelligence

51. How can we determine whether a machine has artificial intelligence?
a. Analyses the Data
b. Collect the Data
c. Understand the Data
d. All of the above 

Show Answer ⟶
d. All of the above

52. The Machine is considered intelligent when __________.
a. Analyses the Data
b. Collect the Data
c. Understand the Data
d. All of the above 

Show Answer ⟶
d. All of the above

53. To develop into an intelligent machine, ____________ is essential.
a. Training 
b. Analyzing
c. Human Brain
d. None of the above

Show Answer ⟶
a. Training

54. Machine learning’s purpose is ___________.
a. Trained the Human
b. Allowing machines to self-learn using available data 
c. Think like animal
d. None of the above

Show Answer ⟶
b. Allowing machines to self-learn using available data

55. Building machines and algorithms to help humans with AI is the main goal to ____________.
a. Ability to do computational operations and work like human brain 
b. Walk like human
e. Both a) and b)
d. None of the above

Show Answer ⟶
a. Ability to do computational operations and work like human brain

56. Which of the following forms of AI is the most advanced?
a. Machine Learning
b. Deep Learning 
c. Neural Network
d. None of the above

Show Answer ⟶
b. Deep Learning

57. Which of the following is a Data Science based website that offers pricing comparisons?
a. Shopzilla
b. Junglee
c. PriceRunner
d. All of the above 

Show Answer ⟶
d. All of the above

58. Which of the following artificial intelligence domains gathers data, maintains datasets, and generalizes meaning from it?
a. Data Science 
b. Neural Network
c. Computer Vision
d. Natural Language Processing

Show Answer ⟶
a. Data Science

59. What technique below does computer vision do?
a. Object detection
b. Face recognition
c. Pattern detection
d. All of the above 

Show Answer ⟶
d. All of the above

60. Which of the following tasks can an automatic car perform on its own?
a. Maintain a map of their surroundings
b. Detect traffic lights
c. Read road signs
d. All of the above 

Show Answer ⟶
d. All of the above

61. Which could serve as a computer vision input source?
a. Thermal Sensors
b. Infrared Sensors
c. Indicators
d. All of the above 

Show Answer ⟶
d. All of the above

62. Which of the following AI tasks is important in mobile phones?
a. Taking Images
b. Features of face reading and comparing while unlocking the phone 
c. Storing Files and Folder based on AI
d. None of the above

Show Answer ⟶
b. Features of face reading and comparing while unlocking the phone

63. How do email filters identify spam messages?
a. Uncovering certain words
b. Suspicious word patterns
c. User’s preferences based on the emails
d. All of the above 

Show Answer ⟶
d. All of the above

64. Smart assistants like Apple Siri and Amazon Alexa’s main objective is ____________.
a. Voice Interaction and Recognition
b. Music Playback
c. Making to-do list
d. All of the above 

Show Answer ⟶
d. All of the above

65. Which of the following AI fields takes the opportunity to use algorithms to extract information from spoken and written words?
a. Data Science
b. Computer Vision
c. Natural Language Process 
d. None of the above

Show Answer ⟶
c. Natural Language Process

66. Purpose of Natural Language Generation ______________.
a. Produce written or spoken narrative from a data set 
b. Face recognition
c. Arranging Files and Folders
d. None of the above

Show Answer ⟶
a. Produce written or spoken narrative from a data set

67. The central element of each AI system is which of the following?
a. Testing
b. Training
c. Data 
d. None of the above

Show Answer ⟶
c. Data

68. Which of the following aspects of natural language processing includes linking to natural language?
a. Reading Language Reading
b. Natural Language Understanding 
c. Natural Language Knowing
d. None of the above

Show Answer ⟶
b. Natural Language Understanding

69. Which of the following AI domains best describes product advertising?
a. Natural Language Processing
b. Data Science 
c. Computer Vision
d. None of the above

Show Answer ⟶
b. Data Science

70. The detection of illegal or questionable behavior by law enforcement agencies is the example of _________.
a. Environment Perception 
b. Content-Based Image Retrieval
c. Face Recognition
d. All of the above

Show Answer ⟶
a. Environment Perception

71. Which of the following techniques is used by search engines like Google and Bing with the aid of computer vision?
a. Smart Interactions
b. Content-Based Image Retrieval 
c. Face Recognition
d. All of the above

Show Answer ⟶
b. Content-Based Image Retrieval

72. Systems for people with disabilities are an example of _________.
a. Face recognition
b. Content-Based Image retrieval
c. Smart Interactions
d. Environment Perception

Show Answer ⟶
c. Smart Interactions 

73. Which of the following is an AI-based vacuum cleaner?
a. Mitra
b. Roomba 
c. Nao
d. Robear

Show Answer ⟶
b. Roomba

74. Which of the following languages was developed to construct programmes for artificial intelligence?
a. C++
b. PHP
c. Python 
d. BASIC

Show Answer ⟶
c. Python

75. Which of the following statements best describes the importance of human intelligence?
a. Learning via feedback or data is not necessary.
b. Everyone is born intelligent in different ways.
c. For humans to learn something, iterations are necessary.
d. None of the above

Show Answer ⟶
c. For humans to learn something, iterations are necessary.

76. Which of the following was the first language created specifically for artificial intelligence programming?
a. LISP 
b. Python
c. C++
d. C

Show Answer ⟶
a. LISP

77. Find the game based on computer vision –
a. Scrooby
b. AI Pictionary
c. Doom
d. All of the above 

Show Answer ⟶
d. All of the above

78. Choose the odd one from the following computer vision examples –
a. Roomba 
b. Interpol
c. MS Kinnect
d. Snapchat

Show Answer ⟶
a. Roomba

79. What are the different principles for AI ethics?
a. Individual Need
b. Individual effort
c. Societal contribution
d. All of the above

Show Answer ⟶
d. All of the above

80. A collection of rules called __________ offers guidance on the creation and results of artificial intelligence.
a. AI ethics
b. AI Bias
c. AI Access
d. None of the above

Show Answer ⟶
a. AI ethics

81. Computer programmes that take decision-making abilities are known as __________.
a. Expert System 
b. Algorithm
c. Turing Test
d. None of the above

Show Answer ⟶
a. Expert System

82. Complex processing device Watson developed by ___________.
a. Apple
b. IBM
c. Accenture
d. Google

Show Answer ⟶
b. IBM

83. Which year did the expert systems introduce ___________.
a. 1975
b. 1978
c. 1980
d. 1982

Show Answer ⟶
c. 1980

84. ________of the following, which makes use of data science?
a. Email Spam Filter
b. Fraud and Risk Detection
c. Internet Search
d. All of the above

Show Answer ⟶
d. All of the above

85. Rajesh is studying various AI fields. Which field is related to data mining and machine learning. Select the ideal domain to illustrate his confusion ?
a. Computer Vision
b. Natural Language Processing
c. Data Science 
d. None of the above

Show Answer ⟶
c. Data Science 

86. In order for stakeholders to understand data and apply it to make strategic business decisions, the __________ acts as a gatekeeper for the organization’s data.
a. AI engineer
b. Data Analyst 
c. Software Engineer
d. None of the above

Show Answer ⟶
b. Data Analyst 

87. Which area of AI gathers knowledge from various forms of data using scientific procedures, procedures, algorithms, and systems?
a. Computer Vision
b. Natural Language Processing
c. Data Science
d. None of the above

Show Answer ⟶
c. Data Science

88. What skills are required for making a career in AI?
a. Mathematical Skill
b. Statistical Skill
c. Computer Skill
d. All of the above

Show Answer ⟶
d. All of the above

89. Which of the following is NOT a part of data privacy?
a. Data collection is ethical and only done with the user’s permission.
b. Data can be collected without permission
c. Data should be collected, stored, and shared with third parties
d. All of the above

Show Answer ⟶
a. Data collection is ethical and only done with the user’s permission.

90. Which of the following is not related to AI?
a. Computer Vision
b. Machine Learning
c. Data Science
d. Data Structure

Show Answer ⟶
d. Data Structure

91. Humans can comprehend intricate mathematical formulas and algorithms. This procedure is called_________.
a. Black Box
b. Data Scientist
c. Algorithm Designer
d. None of the above

Show Answer ⟶
a. Black Box

92. AI bais usually reflects ________.
a. Culture
b. Gender
c. Age group
d. All of the above

Show Answer ⟶
d. All of the above

93. What are the demerits of Artificial Intelligence?
a. Create Unemployment t
b. Decrease the work quality
c. Unstructured Data collection
d. All of the above

Show Answer ⟶
a. Create Unemployment

94. A little child in class 3 has some math homework that needs to be completed. He is struggling with his homework while seated at a table that has the Google chatbot Alexa on it. He soon begins asking Alexa to respond to all of his inquiries. The child merely notes the answers that Alexa provides on his notebook as she responds. Alexa is an example of __________.
a. Data & Machine Learning
b. Internet
c. Automated Data Collection
d. None of the above

Show Answer ⟶
a. Data & Machine Learning

95. The youngster is proficient with technology, but he uses it to finish his schoolwork without actually learning anything because he isn’t using his head to answer. So, while he is ____________.
a. Smarter
b. Might not be getting educated properly
c. Intelligent Boy
d. None of the above

Show Answer ⟶
b. Might not be getting educated properly

96. The first few salon searches on Google are primarily for female salons. This is predicated on the notion that a person looking for a salon is almost always a female. This is a problem of ___________.
a. Bias
b. Access
c. Ethic
d. None of the above

Show Answer ⟶
a. Bias

97. You inform your friend that you want to buy new shoes and you are trying to search on the internet, after some time the online retailers start sending you reminders to buy shoes. What type of technology is used?
a. Artificial Intelligence
b. Auto Search technology
c. Google Automation
d. None of the above

Show Answer ⟶
a. Artificial Intelligence

98. The phone will eventually start delivering notifications about related books or messages about the same book once you operate it, even if you are not using it and talking to someone face-to-face about a book you recently read while the phone is maintained in a locked state nearby.
a. Artificial Intelligence
b. Auto Search technology
c. Google Automation
d. None of the above

Show Answer ⟶
a. Artificial Intelligence

99. Data working around numeric and alpha-numeric data is known as ___________.
a. Data Science
b. Computer Vision
c. Natural Language Processing
d. None of the above

Show Answer ⟶
a. Data Science

100. Working around image and visual data is known as ___________.
a. Data Science
b. Computer Vision
c. Natural Language Processing
d. None of the above

Show Answer ⟶
b. Computer Vision

101. Working around textual and speech-based data is known as __________.
a. Data Science
b. Computer Vision
c. Natural Language Processing
d. None of the above

Show Answer ⟶
c. Natural Language Processing

102. Application of Data Science ____________.
a. Fraud and Risk Detection
b. Genetics & Genomics
c. Targeted Advertising
d. All of the above

Show Answer ⟶
d. All of the above

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Employability skills Class 10 MCQ

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Artificial Intelligence Class 10 Notes

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Advanced Python Class 10 Notes

Teachers and Examiners (CBSESkillEduction) collaborated to create the Advanced Python Class 10 Notes. All the important Information are taken from the NCERT Textbook Artificial Intelligence (417).

Advanced Python Class 10 Notes

advanced python class 10

Recap 1: Jupyter Notebook

For interactively creating and presenting projects linked to AI, the Jupyter Notebook is a really powerful tool. The open source web application Jupyter Notebook allows users to create and share documents with live code, equations, visualisations, and text.

Through Anaconda, Jupyter Notebook may be installed and used most easily. The most popular Python distribution for data science, Anaconda, comes pre-installed with all the most used tools and libraries.

Introduction to Virtual Environments

By creating separate Python virtual environments for each project, a virtual environment is a tool that aids in maintaining the separation of dependencies needed by various projects. The majority of Python developers utilise this as one of their most important tools.

Steps for creating Virtual Environments 

Step 1 : Open Anaconda prompt

Step 2 : As we open the Anaconda prompt, we can see that in the beginning of the prompt message, the
term (base) is written. 

Step 3 : Type “conda create -n env python=3.7” in command prompt

Step 4 : After processing, the prompt will ask if we wish to proceed with installations or not. Type Y
on it and press Enter.

Step 5 : Packages will start getting installed in the environment.

Step 6 : Once an environment has been successfully created.

Step 7 : Type “conda activate env” in command prompt

Advanced Python Class 10 Notes

Recap 2: Introduction to Python

Guido Van Rossum, of Centrum Wiskunde & Informatica, is the inventor of the Python programming language. The language, which took its name from the 1970s BBC comedy series “Monty Python’s Flying Circus,” was made available to the general public in 1991. It can be used to programme in both an object-oriented and procedural manner. Because it offers so many features, Python is very popular.

Easy to learn, read and maintain

Python has few keywords, simple structure and a clearly defined syntax. Python allows anyone to learn the language quickly. A program written in Python is fairly easy-to-maintain.

A Broad Standard library

Python has a huge bunch of libraries with plenty of built-in functions to solve a variety of problems.

Interactive Mode

Python has support for an interactive mode which allows interactive testing and debugging of snippets of code.

Portability and Compatibility

Python can run on a wide variety of operating systems and hardware platforms, and has the same interface on all platforms.

Extendable

We can add low-level modules to the Python interpreter. These modules enable programmers to add
to or customize their tools to be more efficient.

Databases and Scalable

Python provides interfaces to all major open source and commercial databases along with a better structure and support for much larger programs than shell scripting.

Applications of Python

There exist a wide variety of applications when it comes to Python. Some of the applications are:

1. Application of Python
2. Web and Internet Development
3. Desktop GUI Applications
4. Business Application
5. Software Development
6. Games and 3d Graphics
7. Database Access

Advanced Python Class 10 Notes

Recap 3: Python Basics

Keywords & Identifiers

Some terms in Python have predefined meanings that the computer automatically assigns to them. These phrases are referred to as keywords. In order to avoid misunderstanding and unclear results, keywords should only be used in the default manner and cannot be changed at any point in time. The following list includes a few of the keywords:

Example of Keywords –

False, class, finally, is, return, None, continue, for lambda, try, True, def, from, nonlocal, while, and, del, global, not, with, as, elif, if, or, yield, assert, else, import, pass, break, except, in, raise etc.

Variables & Datatypes

Variables 

A variable is a memory location where you store a value in a programming language. In Python, a variable is formed when a value is assigned to it. Declaring a variable in Python does not require any further commands.

There are a certain rules and regulations we have to follow while writing a variable

  1. A number cannot be used as the first character in the variable name. Only a character or an underscore can be used as the first character.
  2. Python variables are case sensitive.
  3. Only alpha-numeric characters and underscores are allowed.
  4. There are no special characters permitted.

Advanced Python Class 10 Notes

Constants

A constant is a kind of variable that has a fixed value. Constants are like containers that carry information that cannot be modified later.

Declaring and assigning value to a constant 

NAME = “Rajesh Kumar” 

AGE = 20

Datatype 

In Python, each value has a datatype. Data types are basically classes, and variables are instances (objects) of these classes, because everything in Python programming is an object.

Python has a number of different data types. The following are some of the important datatypes.

  1. Numbers
  2. Sequences
  3. Sets
  4. Maps

a. Number Datatype

Numerical Values are stored in the Number data type. There are four  categories of number datatype –

  1. Int – Int datatype is used to store the whole number values. Example : x=500
  2. Float – Float datatype is used to store decimal number values. Example : x=50.5
  3. Complex – Complex numbers are used to store imaginary values. Imaginary values are denoted with ‘j’ at the end of the number. Example : x=10 + 4j
  4. Boolean – Boolean is used to check whether the condition is True or False. Example : x = 15 > 6      type(x)

Advanced Python Class 10 Notes

 b. Sequence Datatype

A sequence is a collection of elements that are ordered and indexed by positive integers. It’s made up of both mutable and immutable data types. In Python, there are three types of sequence data types:

  1. String – Unicode character values are represented by strings in Python. Because Python does not have a character data type, a single character is also treated as a string. Single quotes (‘ ‘) or double quotes (” “) are used to enclose strings. These single quotes and double quotes merely inform the computer that the beginning of the string and end of the string. They can contain any character or symbol, including space. Example : name = ”Rakesh kumar”
  2. List – A list is a sequence of any form of value. The term “element” or “item” refers to a group of values. These elements are indexed in the same way as an array is. List is enclosed in square brackets. Example : dob = [19,”January”,1995] 
  3. Tuples – A tuple is an immutable or unchanging collection. It is arranged in a logical manner, and the values can be accessed by utilizing the index values. A tuple can also have duplicate values. Tuples are enclosed in (). Example : newtuple = (15,20,20,40,60,70)

c. Sets Datatype

A set is a collection of unordered data and does not have any indexes. In Python, we use curly brackets to declare a set. Set does not have any duplicate values. To declare a set in python we use the curly brackets.

Example : newset = {10, 20, 30}

d. Mapping

This is an unordered data type. Mappings include dictionaries.

Dictionaries 

In Python, Dictionaries are used generally when we have a huge amount of data. A dictionary is just like any other collection array. A dictionary is a list of strings or numbers that are not in any particular sequence and can be changed. The keys are used to access objects in a dictionary. Curly brackets are used to declare a dictionary.  Example : d = {1:’Ajay’,’key’:2} 

Advanced Python Class 10 Notes

Operators 

Operators are symbolic representations of computation. They are used with operands, which can be either values or variables. On different data types, the same operators can act differently. When operators are used on operands, they generate an expression.

 Operators are categorized as –

  • Arithmetic operators
  • Assignment operators
  • Comparison operators
  • Logical operators
  • Identity operators
  • Membership operators
  • Bitwise operators

Arithmetic Operators

Mathematical operations such as addition, subtraction, multiplication, and division are performed using arithmetic operators.

Operator 

Meaning 

Expression 

Result

+

Addition

20 + 20

40

Subtraction

30 – 10 

20

*

Multiplication

10 * 100

1000

/

Division

30 / 10

20

//

Integer Division

25 // 10 

2

Remainder

25 % 10

5

** 

Raised to power

3 ** 2

9

Assignment Operator

When assigning values to variables, assignment operators are used.

Operator 

Expression 

Equivalent to

=

x=10

x = 10

+=

x += 10

x = x + 10

-=

x -= 10

x = x – 10

*=

x *= 10

x = x * 10

/=

x /= 10

x = x / 10

Advanced Python Class 10 Notes

Comparison Operator

The values are compared using comparison operators or relational operators. Depending on the criteria, it returns True or False.

Operator

Meaning

Expression

Result

>

Greater Than

20 > 10

True

  

20 < 50

False 

<

Less Than

20 < 10

False 

  

10 < 40

True

==

Equal To

5 == 5

True

  

5 == 6

False

!=

Not Equal to

67 != 45

True

  

35 != 35

False

Logical Operator 

Logical operators are used to combine the two or more then two conditional statements –

Operator

Meaning

Expression

Result

And

And Operator

True and True

True

  

True and False

False

Or

Or Operator

True or False

True

  

False or False

False

Not

Not Operator

Not False

True

  

Not True

False

Employability skills Class 10 Notes

Employability skills Class 10 MCQ

Employability skills Class 10 Questions and Answers

Artificial Intelligence Class 10 Notes

Artificial Intelligence Class 10 MCQ

Artificial Intelligence Class 10 Questions and Answers

Computer Vision Class 10 Notes

Teachers and Examiners (CBSESkillEduction) collaborated to create the Computer Vision Class 10 Notes. All the important Information are taken from the NCERT Textbook Artificial Intelligence (417).

Computer Vision Class 10 Notes

computer vision class 10

Computer vision is a branch of artificial intelligence (AI) that enables computers and systems to extract useful information from digital photos, videos, and other visual inputs and to execute actions or make recommendations based on that information.

Applications of Computer Vision

In the 1970s, computer vision as a concept was first introduced. Everyone was excited by the new uses for computer vision. However, a considerable technological advance in recent years has elevated computer vision to the top of many companies’ priority lists. Let’s examine a few of them:

Facial Recognition

Computer vision is essential to the advancement of the home in the era of smart cities and smart homes. The most crucial application of computer vision is facial recognition in security. Either visitor identification or visitor log upkeep is possible.

Face Filters

Many of the functionality in today’s apps, including Instagram and Snapchat, rely on computer vision. One of them is the usage of facial filters. The computer or algorithm may recognise a person’s facial dynamics through the camera and apply the chosen facial filter.

Google’s Search by Image

The majority of data that is searched for using Google’s search engine is textual information, but it also has the intriguing option of returning search results via an image. This makes use of computer vision since it examines numerous attributes of the input image while also comparing them to those in the database of images to provide the search result.

Computer Vision in Retail

One of the industries with the quickest growth is retail, which is also utilising computer vision to improve the user experience. Retailers can analyse navigational routes, find walking patterns, and track customer movements through stores using computer vision techniques.

Self-Driving Cars

Computer Vision is the fundamental technology behind developing autonomous vehicles. Most leading car manufacturers in the world are reaping the benefits of investing in artificial intelligence for developing on-road versions of hands-free technology.

Medical Imaging

A reliable resource for doctors over the past few decades has been computer-supported medical imaging software. It doesn’t just produce and analyse images; it also works as a doctor’s helper to aid in interpretation.
The software is used to interpret and transform 2D scan photos into interactive 3D models that give medical professionals a thorough insight of a patient’s health.

Google Translate App

To read signs written in a foreign language, all you have to do is point the camera on your phone at the text, and the Google Translate software will very immediately translate them into the language of your choice. This is a useful application that makes use of Computer Vision, utilising optical character recognition to view the image and augmented reality to overlay an accurate translation.

Computer Vision Tasks

The many Computer Vision applications are based on a variety of tasks that are carried out to extract specific information from the input image that may be utilised for prediction or serves as the foundation for additional analysis. A computer vision application performs the following tasks:

computer vision task
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Classification

Image Classification problem is the task of assigning an input image one label from a fixed set of categories. This is one of the core problems in CV that, despite its simplicity, has a large variety of practical applications.

Classification + Localisation

This is the task which involves both processes of identifying what object is present in the image and at the same time identifying at what location that object is present in that image. It is used only for single objects.

Object Detection

Finding occurrences of real-world items like faces, bicycles, and buildings in pictures or movies is a process known as object detection. To identify occurrences of a certain object category, object identification algorithms frequently employ extracted features and learning techniques. Applications like image retrieval and automatic car parking systems frequently employ it.

Instance Segmentation

The process of identifying instances of the items, categorising them, and then assigning each pixel a label based on that is known as instance segmentation. An image is sent into a segmentation algorithm, which produces a list of regions (or segments).

Basics of Images

We all see a lot of images around us and use them daily either through our mobile phones or computer system. But do we ask some basic questions to ourselves while we use them on such a regular basis.

Basics of Pixels

A picture element is referred to as a “pixel.” In digital form, pixels make up each and every image.
They are the tiniest piece of data that go into a picture. They are normally structured in a 2-dimensional grid and are either circular or square.

Resolution

The resolution of an image is occasionally referred to as the number of pixels. One approach is to define resolution as the width divided by the height when the phrase is used to describe the number of pixels, for example, a monitor resolution of 1280×1024. Accordingly, there are 1280 pixels from side to side and 1024 pixels from top to bottom.

Pixel value

Each of the pixels that make up an image that is stored on a computer has a pixel value that specifies its brightness and/or intended colour. The byte image, which stores this number as an 8-bit integer with a possible range of values from 0 to 255, is the most popular pixel format.
Zero is typically used to represent no colour or black, and 255 is used to represent full colour or white.

Grayscale Images

Grayscale images are images which have a range of shades of gray without apparent colour. The darkest possible shade is black, which is the total absence of colour or zero value of pixel. The lightest possible shade is white, which is the total presence of colour or 255 value of a pixel . Intermediate shades of gray are represented by equal brightness levels of the three primary colours.

RGB Images

Every image we encounter is a coloured image. Three main colors—Red, Green, and Blue—make up these graphics. Red, green, and blue can be combined in various intensities to create all the colours that are visible.

Image Features

In computer vision and image processing, a feature is a piece of information which is relevant for solving the computational task related to a certain application. Features may be specific structures in the image such as points, edges or objects.

Introduction to OpenCV

OpenCV or Open Source Computer Vision Library is that tool which helps a computer extract these features from the images. It is used for all kinds of images and video processing and analysis. It is capable of processing images and videos to identify objects, faces, or even handwriting.

What is a Kernel?

A Kernel is a matrix, which is slid across the image and multiplied with the input such that the output is enhanced in a certain desirable manner. Each kernel has a different value for different kind of effects that we want to apply to an image.

Convolution Neural Networks (CNN)

A Convolutional Neural Network (CNN) is a Deep Learning algorithm that can take in an image as input, assign importance (learnable weights and biases) to various elements and objects in the image, and be able to distinguish between them.

neural network diagram

A convolutional neural network consists of the following layers:
1) Convolution Layer
2) Rectified linear Unit (ReLU)
3) Pooling Layer
4) Fully Connected Layer

Convolutional Neural Network
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Convolution Layer

The Convolution Operation’s goal is to take the input image’s high-level characteristics, such edges, and extract them. There is no requirement that CNN use just one Convolutional Layer.

There are several kernels that are used to produce several features. The output of this layer is called the feature map. A feature map is also called the activation map. We can use these terms interchangeably.
There’s several uses we derive from the feature map:
• We reduce the image size so that it can be processed more efficiently.
• We only focus on the features of the image that can help us in processing the image further.

Rectified Linear Unit Function

The next layer in the Convolution Neural Network is the Rectified Linear Unit function or the ReLU layer. After we get the feature map, it is then passed onto the ReLU layer. This layer simply gets rid of all the negative numbers in the feature map and lets the positive number stay as it is.

Pooling Layer

Similar to the Convolutional Layer, the Pooling layer is responsible for reducing the spatial size of the Convolved Feature while still retaining the important features.
There are two types of pooling which can be performed on an image.
1) Max Pooling : Max Pooling returns the maximum value from the portion of the image covered by the Kernel.
2) Average Pooling: Max Pooling returns the maximum value from the portion of the image covered by the Kernel.

Fully Connected Layer

The final layer in the CNN is the Fully Connected Layer (FCP). The objective of a fully connected layer is to take the results of the convolution/pooling process and use them to classify the image into a label (in a simple classification example).

Employability skills Class 10 Notes

Employability skills Class 10 MCQ

Employability skills Class 10 Questions and Answers

Artificial Intelligence Class 10 Notes

Artificial Intelligence Class 10 MCQ

Artificial Intelligence Class 10 Questions and Answers

Evaluation Class 10 Notes

Teachers and Examiners (CBSESkillEduction) collaborated to create the Evaluation Class 10 Notes. All the important Information are taken from the NCERT Textbook Artificial Intelligence (417).

Evaluation Class 10 Notes

evaluation class 10 notes

What is evaluation?

Evaluation is the process of understanding the reliability of any AI model, based on outputs by feeding test dataset into the model and comparing with actual answers. There can be different Evaluation techniques, depending of the type and purpose of the model.

The various terms which are very important to the evaluation process.

Model Evaluation Terminologies

There are various new terminologies which come into the picture when we work on evaluating our
model. Let’s explore them with an example of the Forest fire scenario.

Consider developing an AI-based prediction model and deploying it in a forest that is prone to forest fires. The model’s current goal is to make predictions about whether or not a forest fire has started. We need to consider the two circumstances of prediction and reality. The reality is the actual situation in the forest at the time of the prediction, while the prediction is the machine’s output.

forest fire

Here, we can see in the picture that a forest fire has broken out in the forest. The model predicts a Yes which means there is a forest fire. The Prediction matches with the Reality. Hence, this condition is termed as True Positive.

forest fire 1

Here there is no fire in the forest hence the reality is No. In this case, the machine too has predicted it correctly as a No. Therefore, this condition is termed as True Negative.

forest fire 2

Here the reality is that there is no forest fire. But the machine has incorrectly predicted that there is a forest fire. This case is termed as False Positive.

forest fire 3

Here, a forest fire has broken out in the forest because of which the Reality is Yes but the machine has
incorrectly predicted it as a No which means the machine predicts that there is no Forest Fire.
Therefore, this case becomes False Negative.

Confusion matrix

The result of comparison between the prediction and reality can be recorded in what we call the confusion matrix. The confusion matrix allows us to understand the prediction results. Note that it is not an evaluation metric but a record which can help in evaluation. Let us once again take a look at
the four conditions that we went through in the Forest Fire example:

confusion matrix

Prediction and Reality can be easily mapped together with the help of this confusion matrix.

forest fire confusion matrix

Evaluation Methods

Accuracy, precision, and recall are the three primary measures used to assess the success of a classification algorithm.

Accuracy 

Accuracy allows you to count the total number of accurate predictions made by a model. The accuracy calculation is as follows: How many of the model predictions were accurate will be determined by accuracy. True Positives and True Negatives are what accuracy considers.

accuracy formula in ai

Here, total observations cover all the possible cases of prediction that can be True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN).

Precision

Precision is defined as the percentage of true positive cases versus all the cases where the prediction is true. That is, it takes into account the True Positives and False Positives.

Precision formula in ai

Recall

It can be described as the percentage of positively detected cases that are positive. The scenarios where a fire actually existed in reality but was either correctly or incorrectly recognised by the machine are heavily considered. That is, it takes into account both False Negatives (there was a forest fire but the model didn’t predict it) and True Positives (there was a forest fire in reality and the model anticipated a forest fire). 

recall formula in ai

Which Metric is Important?

Depending on the situation the model has been deployed in, choosing between Precision and Recall is necessary. A False Negative can cost us a lot of money and put us in danger in a situation like a forest fire. Imagine there is no need for a warning, even in the case of a forest fire. The entire forest might catch fire.

Viral Outbreak is another situation in which a False Negative might be harmful. Consider a scenario in which a fatal virus has begun to spread but is not being detected by the model used to forecast viral outbreaks. The virus may infect numerous people and spread widely.

Consider a model that can determine whether a mail is spam or not. People would not read the letter if the model consistently predicted that it was spam, which could lead to the eventual loss of crucial information.
The cost of a False Positive condition in this case (predicting that a message is spam when it is not) would be high.

ai model predicts

F1 Score

F1 score can be defined as the measure of balance between precision and recall.

f1 score formula in ai

Take a look at the formula and think of when can we get a perfect F1 score?
An ideal situation would be when we have a value of 1 (that is 100%) for both Precision and Recall. In that case, the F1 score would also be an ideal 1 (100%). It is known as the perfect value for F1 Score. As the values of both Precision and Recall ranges from 0 to 1, the F1 score also ranges from 0 to 1.

Let us explore the variations we can have in the F1 Score:

f1 score in ai

Employability skills Class 10 Notes

Employability skills Class 10 MCQ

Employability skills Class 10 Questions and Answers

Artificial Intelligence Class 10 Notes

Artificial Intelligence Class 10 MCQ

Artificial Intelligence Class 10 Questions and Answers

Natural Language Processing Class 10 Notes

Teachers and Examiners (CBSESkillEduction) collaborated to create the Natural Language Processing Class 10 Notes. All the important Information are taken from the NCERT Textbook Artificial Intelligence (417).

Natural Language Processing Class 10 Notes

natural language processing class 10 notes

NLP (Natural Language Processing), is dedicated to making it possible for computers to comprehend and process human languages. Artificial intelligence (AI) is a subfield of linguistics, computer science, information engineering, and artificial intelligence that studies how computers interact with human (natural) languages, particularly how to train computers to handle and analyze massive volumes of natural language data.

Applications of Natural Language Processing

Most people utilize NLP apps on a regular basis in their daily lives. Following are a few examples of real-world uses for natural language processing:

Automatic Summarization – Automatic summarization is useful for gathering data from social media and other online sources, as well as for summarizing the meaning of documents and other written materials.

Sentiment Analysis – To better comprehend what internet users are saying about a company’s goods and services, businesses use natural language processing tools like sentiment analysis to understand the customer requirement.

Indicators of their reputation – Sentiment analysis goes beyond establishing simple polarity to analyse sentiment in context to help understand what is behind an expressed view. This is very important for understanding and influencing purchasing decisions.

Text classification – Text classification enables you to classify a document and organise it to make it easier to find the information you need or to carry out certain tasks. Spam screening in email is one example of how text categorization is used.

Virtual Assistants – These days, digital assistants like Google Assistant, Cortana, Siri, and Alexa play a significant role in our lives. Not only can we communicate with them, but they can also facilitate our life.

Chatbots

A chatbot is one of the most widely used NLP applications. Many chatbots on the market now employ the same strategy as we did in the instance above. Let’s test out a few of the chatbots to see how they function.

• Mitsuku Bot*
https://www.pandorabots.com/mitsuku/

• CleverBot*
https://www.cleverbot.com/

• Jabberwacky*
http://www.jabberwacky.com/

• Haptik*
https://haptik.ai/contact-us

• Rose*
http://ec2-54-215-197-164.us-west-1.compute.amazonaws.com/speech.php

• Ochatbot*
https://www.ometrics.com/blog/list-of-fun-chatbots/

There are 2 types of chatbots 

  1. Scriptbot
  2. Smart-bot.
Scriptbot Smart-bot
Script bots are easy to makeSmart-bots are flexible and powerful
Script bots work around a script which is
programmed in them
Smart bots work on bigger databases and other
resources directly
Mostly they are free and are easy to integrate
to a messaging platform
Smart bots learn with more data
No or little language processing skillsCoding is required to take this up on board
Limited functionalityWide functionality

Human Language VS Computer Language

Humans need language to communicate, which we constantly process. Our brain continuously processes the sounds it hears around us and works to make sense of them. Our brain continuously processes and stores everything, even as the teacher is delivering the lesson in the classroom.

The Computer Language is understood by the computer, on the other hand. All input must be transformed to numbers before being sent to the machine. And if a single error is made while typing, the machine throws an error and skips over that area. Machines only use extremely simple and elementary forms of communication.

Data Processing

Data Processing is a method of manipulation of data. It means the conversion of raw data into meaningful and machine-readable content. It basically is a process of converting raw data into meaningful information.

Since human languages are complex, we need to first of all simplify them in order to make sure that the understanding becomes possible. Text Normalisation helps in cleaning up the textual data in such a way that it comes down to a level where its complexity is lower than the actual data. Let us go through Text Normalisation in detail.

Text Normalisation

The process of converting a text into a canonical (standard) form is known as text normalisation. For instance, the canonical form of the word “good” can be created from the words “gooood” and “gud.” Another illustration is the reduction of terms that are nearly identical, such as “stopwords,” “stop-words,” and “stop words,” to just “stopwords.”

Sentence Segmentation

Under sentence segmentation, the whole corpus is divided into sentences. Each sentence is taken as a different data so now the whole corpus gets reduced to sentences.

Tokenisation

Sentences are first broken into segments, and then each segment is further divided into tokens. Any word, number, or special character that appears in a sentence is referred to as a token. Tokenization treats each word, integer, and special character as a separate entity and creates a token for each of them.

Removing Stopwords, Special Characters and Numbers

In this step, the tokens which are not necessary are removed from the token list. What can be the possible words which we might not require?

Stopwords are words that are used frequently in a corpus but provide nothing useful. Humans utilise grammar to make their sentences clear and understandable for the other person. However, grammatical terms fall under the category of stopwords because they do not add any significance to the information that is to be communicated through the statement. Stopwords include a, an, and, or, for, it, is, etc.

Converting text to a common case

After eliminating the stopwords, we change the text’s case throughout, preferably to lower case. This makes sure that the machine’s case-sensitivity does not treat similar terms differently solely because of varied case usage.

Stemming

The remaining words are boiled down to their root words in this step. In other words, stemming is the process of stripping words of their affixes and returning them to their original forms.

Lemmatization

Stemming and lemmatization are alternate techniques to one another because they both function to remove affixes. However, lemmatization differs from both of them in that the word that results from the elimination of the affix (also known as the lemma) is meaningful. 

lemmatization
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Bag of Words

A bag-of-words is a textual illustration that shows where words appear in a document. There are two components: a collection of well-known words. a metric for the amount of well-known words.

A Natural Language Processing model called Bag of Words aids in the extraction of textual information that can be used by machine learning techniques. We gather the instances of each term from the bag of words and create the corpus’s vocabulary.

bag of word
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Here is the step-by-step approach to implement bag of words algorithm:

1. Text Normalisation: Collect data and pre-process it
2. Create Dictionary: Make a list of all the unique words occurring in the corpus. (Vocabulary)
3. Create document vectors: For each document in the corpus, find out how many times the word from the unique list of words has occurred.
4. Create document vectors for all the documents.

Term Frequency

The measurement of a term’s frequency inside a document is called term frequency. The simplest calculation is to count the instances of each word. However, there are ways to change that value based on the length of the document or the frequency of the term that appears the most often.

Inverse Document Frequency

A term’s frequency inside a corpus of documents is determined by its inverse document frequency. It is calculated by dividing the total number of documents in the corpus by the number of documents that contain the phrase.

Applications of TFIDF

TFIDF is commonly used in the Natural Language Processing domain. Some of its applications are:

Document
Classification
Topic ModellingInformation
Retrieval System
Stop word filtering
Helps in classifying the
type and genre of a
document.
It helps in predicting
the topic for a corpus.
To extract the
important information
out of a corpus.
Helps in removing the
unnecessary words
out of a text body.

Employability skills Class 10 Notes

Employability skills Class 10 MCQ

Employability skills Class 10 Questions and Answers

Artificial Intelligence Class 10 Notes

Artificial Intelligence Class 10 MCQ

Artificial Intelligence Class 10 Questions and Answers

AI Project Cycle Class 10 Notes

Teachers and Examiners (CBSESkillEduction) collaborated to create the AI Project Cycle Class 10 Notes. All the important Information are taken from the NCERT Textbook Artificial Intelligence (417).

AI Project Cycle Class 10

ai project cycle class 10

The AI Project Cycle is a step-by-step process that a company must follow in order to derive value from an AI project and to solve the problem.

The AI Project Cycle offers us a suitable framework that can guide us in the right direction. The AI project cycle consists of five main stages:

stage of ai project cycle

Starting with problem scoping, you define the objective of your AI project by identifying the issue that you hope to address. When problem scoping, we consider several factors that have an impact on the issue we’re trying to address to make the situation more evident.

● You need to acquire data which will become the base of your project as it will help you in understanding what the parameters that are related to problem scoping are.

● You go for data acquisition by collecting data from various reliable and authentic sources. Since the data you collect would be in large quantities, you can try to give it a visual image of different types of representations like graphs, databases, flow charts, maps, etc. This makes it easier for you to interpret the patterns which your acquired data follows.

● After exploring the patterns, you can decide upon the type of model you would build to achieve the goal. For this, you can research online and select various models which give a suitable output.

● You can test the selected models and figure out which is the most efficient one.

● The most efficient model is now the base of your AI project and you can develop your algorithm around it.

● Once the modelling is complete, you now need to test your model on some newly fetched data. The results will help you in evaluating your model and improving it.

● Finally, after evaluation, the project cycle is now complete and what you get is your AI project.

Problem Scoping

It is a fact that we are surrounded by problems. They could be small or big, sometimes ignored or sometimes even critical. Many times, we become so used to a problem that it becomes a part of our life. Identifying such a problem and having a vision to solve it, is what Problem Scoping is about.

The Sustainable Development Goals are a set of 17 objectives that the United Nations has stated. These objectives are meant to be accomplished by the year 2030. Every UN member state has made a commitment to doing this.

17 sustainable development goals

These sustainable development objectives line up with the issues that we might also see in our immediate environment. Such issues should be sought out and addressed because doing so would improve the lives of many people and advance the objectives of our nation.

4Ws Problem Canvas

A problem can be difficult to scope since we need to have a deeper understanding of it in order to have a clearer image when trying to solve it. Hence, we use the 4Ws Problem Canvas to help us out. 

The 4 W’s of Problem Scoping are Who, What, Where, and Why. This 4 W’s helps to identify and understand the problem in a better manner.

4ws problem canvas

a. Who – The “Who” element helps us to understand and categorize who is directly and indirectly affected by the problem, and who are known as Stakeholders.

b. What – The “What” section aids us in analyzing and recognizing the nature of the problem, and you may also gather evidence to establish that the problem you’ve chosen exists under this block.

c. Where – What is the situation, and where does the problem arise.

d. Why – Refers to why we need to address the problem and what the advantages will be for the stakeholders once the problem is solved.

Problem Statement Template with space to fill details according to your Goal:

statement of the problem template

Data Acquisition

The method of collecting accurate and trustworthy data to work with is referred to as data acquisition. Data can be acquired from a variety of sources, including websites, journals, newspapers, and other media, such as text, video, photographs, and audio.

Dataset

Dataset is a collection of data in tabular format. Dataset contains numbers or values that are related to a specific subject. For example, students’ test scores in a class is a dataset.

The dataset is divided into two parts

a. Training dataset – Training dataset is a large dataset that teaches a machine learning model. Machine learning algorithms are trained to make judgments or perform a task through training datasets. Maximum part of the dataset comes under training data (Usually 80%)

b. Test dataset – Data that has been clearly identified for use in tests, usually of a computer program, is known as test data. 20% of data used in test data

Data Features

Recheck your problem description and try to identify the data attributes needed to solve this challenge. The type of data you want to collect is referred to as data features. The salary amount, increase percentage, increment time, bonus, etc. are examples of data features.

There can be various ways in which you can collect data. Some of them are:

acquiring data

Data Exploration

You must have noted while collecting data that it is a complicated thing; it is full of numbers, and in order to make sense of it, one needs identify certain patterns in it.

For Example, if you go to the library and choose a random book, you try to rapidly go through its content by turning pages and reading the description before you decide to borrow it for yourself. This helps you determine whether or not the book is suitable for your requirements and interests this is data exploration.

To analyze the data, you need to visualize it in some user-friendly format so that you can:
● Quickly get a sense of the trends, relationships and patterns contained within the data.
● Define strategy for which model to use at a later stage.
● Communicate the same to others effectively. To visualize data, we can use various types of visual representations.

Modelling

An AI model is a program that has been trained to recognize patterns using a set of data. AI modeling is the process of creating algorithms, also known as models, that may be educated to produce intelligent results. This is the process of programming code to create a machine artificially.

Generally, AI models can be classified as follows:

ai model

Rule Based Approach

AI modelling in which the developer sets the rules. The machine executes its duty in accordance with the rules or instructions specified by the developer.

Learning Based Approach

AI modelling in which the computer learns on its own. The AI model is trained on the data provided to it under the Learning Based technique, and after that, it is able to create a model that is flexible to the change in data.

The learning-based approach can further be divided into three parts:

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

Supervised Learning – In a supervised learning model, the dataset which is fed to the machine is labelled. In
other words, we can say that the dataset is known to the person who is training the machine only then he/she is able to label the data.

Unsupervised Learning – Such models work on continuous data. For example, if you wish to predict your next salary, then you would put in the data of your previous salary, any increments, etc., and would train the
model. Here, the data which has been fed to the machine is continuous. 

Unsupervised Learning – An unsupervised learning model works on unlabelled dataset. This means that the data which is fed to the machine is random and there is a possibility that the person who is training the model does not have any information regarding it. 

Unsupervised learning models can be further divided into two categories:

Clustering – Refers to the unsupervised learning algorithm which can cluster the unknown data according to the patterns or trends identified out of it. The patterns observed might be the ones which are known to the developer or it might even come up with some unique patterns out of it.

Dimensionality Reduction – We humans are able to visualise upto 3-Dimensions only but according to a lot of theories and algorithms, there are various entities which exist beyond 3-Dimensions. For example, in Natural language Processing

Evaluation

Once a model has been created and trained, it must undergo appropriate testing in order to determine the model’s effectiveness and performance. Thus, the model is evaluated using Testing Data (which was taken from the dataset generated at the Data Acquisition stage), and the effectiveness of the model is determined using the parameters listed below –

ai evaluation

Neural Networks

Neural networks are based in part on how neurons function in the human brain. The main benefit of neural networks is their ability to automatically extract data features without the assistance of a programmer.

Employability skills Class 10 Notes

Employability skills Class 10 MCQ

Employability skills Class 10 Questions and Answers

Artificial Intelligence Class 10 Notes

Artificial Intelligence Class 10 MCQ

Artificial Intelligence Class 10 Questions and Answers

Basics of AI Class 10

Teachers and Examiners (CBSESkillEduction) collaborated to create the Basics of AI Class 10. All the important Information are taken from the NCERT Textbook Artificial Intelligence (417).

Basics of AI Class 10

basics of ai class 10

People all around the world have long been fascinated by the concept of artificial intelligence. Many organizations have come up with their own definitions of artificial intelligence. Following are a few of them:

NITI Aayog: National Strategy for Artificial Intelligence

AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making. Initially conceived as a technology that could mimic human intelligence, AI has evolved in ways that far exceed its original conception.

World Economic Forum

Artificial intelligence (AI) is the software engine that drives the Fourth Industrial Revolution. Its impact can already be seen in homes, businesses and political processes. In its embodied form of robots, it will soon be driving cars, stocking warehouses and caring for the young and elderly.

European Artificial Intelligence (AI) leadership, the path for an integrated vision

AI is not a well-defined technology and no universally agreed definition exists. It is rather a cover term for techniques associated with data analysis and pattern recognition. AI is not a new technology, having existed since the 1950s.

Encyclopedia Britannica

Artificial intelligence (AI), is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.

AI, ML & DL

You must have encountered a very common conflict between artificial intelligence (AI) and machine learning while you worked to develop your AI readiness (ML). These phrases are frequently used synonymously, but are they the same? Is there no distinction between artificial intelligence and machine learning? Is Deep Learning (DL) an instance of artificial intelligence? What is Deep Learning, exactly?

artificial intelligence venn diagram

Artificial Intelligence (AI)

Refers to any method that makes it possible for computers to simulate intelligence. Machines can now detect human faces, move and manipulate items, comprehend human voice commands, and perform a variety of other jobs thanks to this technology. The AI-enabled machines operate in an intelligent manner and think algorithmically.

Machine Learning (ML)

It is a branch of artificial intelligence that allows robots to get better at tasks over time (data). The goal of machine learning is to give computers the ability to learn on their own utilizing the supplied data and arrive at reliable predictions and decisions.

Deep Learning (DL)

Software can use it to teach itself how to carry out tasks using enormous volumes of data. Massive amounts of data are used to train the machine in deep learning, allowing it to learn from the data. These devices possess the intelligence to create algorithms on their own.

Introduction to AI Domains

AI models can be broadly categorized into three domains:

  1. Data Science
  2. Computer Vision
  3. Natural Language Processing

Data Science 

Data sciences is an area of AI that deals with data systems and processes. In this area, a system gathers a lot of data, maintains data sets, and extrapolates meaning from the data. A decision can be made based on the data science-extracted information.

Example of Data Science

Price Comparison Websites – Price comparison websites include PriceGrabber, PriceRunner, Junglee, Shopzilla, and DealTime, to name a few. There is a huge amount of data powering these websites. If you have ever used one of these websites, you are aware of how convenient it is to compare a product’s price among various merchants in one location.

Computer Vision

The field of artificial intelligence known as computer vision, or CV for short, demonstrates the ability of a machine to gather and analyze visual data before making predictions about it. Image acquisition, screening, analysis, identification, and information extraction are all part of the process.

Example of Computer Vision
  1. Self-Driving cars/ Automatic Cars
  2. Face Lock in Smartphones

Natural Language Processing

NLP, or natural language processing, is a subfield of artificial intelligence that deals with the use of natural language in communication between machines and people. Natural language processing (NLP) aims to use algorithms to extract information from spoken and written words in natural language, which is language spoken by people.

Example of Natural Language Processing 
  1. Email filters
  2. Smart assistants like – Apple Siri and Amazon Alexa 

AI Ethics

AI ethics is a set of moral guidelines and methods meant to guide the creation and ethical application of artificial intelligence technologies. Organizations are beginning to create AI codes of ethics as AI has become ingrained in goods and services.

These days, the Information Age is giving way to the Age of Artificial Intelligence. We now construct solutions using intelligence gathered from the data rather than data or information. Even Netflix TV and movie recommendations can be made using these technologies.

Data Privacy

Data is the center of the artificial intelligence. Every business, no matter how big or small, is collecting data from as many sources as they can. The fact that more than 70% of the data acquired to date was just gathered in the previous three years demonstrates how crucial data has grown in recent years. The adage that data is the new gold is not untrue.

AI Bias

When outcomes in AI cannot be broadly generalised, bias occurs. We frequently imagine bias as the product of preferences or exclusions in training data, but bias can also be introduced through the methods used to collect data, the algorithms used to process it, and the methods used to interpret the results of AI.

AI Access

Not everyone can use artificial intelligence because it is still a developing technology. People who can afford AI-enabled technology benefit from it to the fullest, while those who cannot are left behind. Due to this, a gap between these two social strata has developed, and it is becoming wider due to the rapid growth of technology.

AI creates unemployment

The use of AI is improving people’s lives. Nowadays, most tasks can be completed with a few clicks. AI will soon be able to complete all the difficult activities that humans have been performing for a long time. Maybe all of the laborer’s will be replaced by AI-enabled machines in the near future.

AI for kids

Kids today are intelligent enough to grasp technology from a young age. As their capacity for thought grows, they begin to become tech-savvy and eventually pick everything up more quickly than an adult. But should technology be introduced to young children?

Employability skills Class 10 Notes

Employability skills Class 10 MCQ

Employability skills Class 10 Questions and Answers

Artificial Intelligence Class 10 Notes

Artificial Intelligence Class 10 MCQ

Artificial Intelligence Class 10 Questions and Answers

Artificial Intelligence Class 10 Notes

Teachers and Examiners (CBSESkillEduction) collaborated to create the Artificial Intelligence Class 10 Notes. All the important Information are taken from the NCERT Textbook Artificial Intelligence (417).

Artificial Intelligence Class 10 Notes

What is Intelligence?

Humans have been creating machines that can simplify their lives. The purpose of machines is to complete jobs that are either too time-consuming or tedious for people to complete. Therefore, by doing our work for us, machines lighten our weight and assist us in achieving our objectives.

Artificial Intelligence Class 10 Notes

What is Artificial Intelligence?

Artificial is something which is man-made, which does not occur naturally. But what about Intelligence, how do we define that?

According to researchers, intelligence is the ‘ability to perceive or infer information, and to retain it as knowledge to be applied towards adaptive behaviors within an environment or context.’ 

artificial intelligence class 10 notes

Let us define each term mentioned above to get a proper understanding:

class 10 artificial intelligence notes

Artificial Intelligence can be defined:

  1. Ability to interact with the real world 
    1. To perceive, understand and act 
      1. Example: Speech Recognition – Understanding and synthesis 
      2. Example: Image Recognition 
      3. Example: Ability to take action: to have an effect 
  2. Reasoning and planning 
    1. Modelling the external world, given input 
      1. Solving new problems, planning and making decisions 
      2. Ability to deal with unexpected problems, uncertainties 
  3. Learning and adaptation 
    1. Continuous learning and adapting graph 
      1. Our internal models are always being updated 
      2. Example: Baby learning to categorize and recognize animals

How AI make decisions?

The basis for decision-making depends on the information that is available and how we perceive and comprehend it. Information in this page refers to our current knowledge, self-awareness, intuition, and past experiences.

Artificial Intelligence Class 10 Notes

What is Artificial Intelligence?

Artificial intelligence is the ability of a machine to mimic human characteristics, such as decision-making, future prediction, self-improvement, and learning.

In other words, a machine is artificially intelligent when it can do activities on its own, including collecting data, comprehending it, analyzing it, learning from it, and improving it.

How do machines become Artificially Intelligent?

Humans become more and more intelligent with time as they gain experiences during their lives. machines also become intelligent once they are trained with some information which helps them achieve their tasks. AI machines also keep updating their knowledge to optimise their output.

Artificial Intelligence Class 10 Notes

Applications of Artificial Intelligence

AI-powered machines are all around us. They are quickly taking over our daily lives and give us the convenience of having even the most difficult and time-consuming activities completed at the push of a button or with the help of a sensor.

  1. Google – We use Google to search the internet without realising how effectively it always provides us with precise responses. It not only quickly returns the results of our search, but it also advises and corrects the grammar of our written words.
  2. Hey Shiri – These days, we have personal assistants that respond to a single command and perform numerous tasks. Several popular voice assistants that are an integral component of our digital devices are Alexa, Google Assistant, Cortana, and Siri.
  3. Google Map – Apps like Uber and Google Maps come in handy for helping us find our way about. Consequently, it is no longer necessary to stop and ask for directions constantly.
  4. FIFA – For its users, AI has significantly improved the gaming experience. Many modern video games are supported by artificial intelligence (AI), which improves graphics,  reates new challenges for players, etc.
  5. Amazon – AI has taken care of our habits, likes, and dislikes in addition to making our lives easier. Because of this, services like Netflix, Amazon, Spotify, and YouTube, among others, display recommendations based on our preferences.
  6. Social Media – The recommendations, however, go beyond simply reflecting our preferences; they also take into account our desire to interact with friends via social media networks like Facebook and Instagram. Additionally, they provide us personalised notifications about our online buying information, construct playlists automatically based on our demands, and more. Selfies have never been more enjoyable because of Snapchat’s amazing filters.
  7. Health App – That’s not everything. Our health is also being tracked by AI. There are many chatbots and other health apps available that continuously track their users’ physical and emotional wellbeing.
  8. Humanoid – These applications range from smart devices, such as Sophia, the first humanoid robot intelligent enough to obtain citizenship, to humanoids, biometric security systems, such as the face locks we have on our phones, real-time language translators, weather predictions, and other things. If we keep adding things up, this module would never end because the list is so long. Take some time, have a conversation with a friend, and start noticing more and more AI uses in your environment!

What is not AI?

Today, because there are so many different technologies all around us, we frequently mistake any other technology for artificial intelligence. Because of this, we must clearly define what constitutes AI and what does not.

Washing Machine – A fully automatic washing machine can function on its own, but choosing the washing parameters and making the necessary preparations before each wash require human participation, making the machine an example of automation rather than AI.

Air Conditioner – The internet can be used to remotely turn on and off an air conditioner, but it still requires a human touch.

Employability skills Class 10 Notes

Employability skills Class 10 MCQ

Employability skills Class 10 Questions and Answers

Artificial Intelligence Class 10 Notes

Artificial Intelligence Class 10 MCQ

Artificial Intelligence Class 10 Questions and Answers

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