Q1.What is Artificial Intelligence (AI)? Discuss the three major domains of AI?
Answer:-Artificial Intelligence (AI)
🔹 Artificial Intelligence (AI)
Artificial Intelligence (AI) is a branch of computer science that enables machines to think, learn, and make decisions like humans.
AI allows computers and machines to perform tasks that normally require human intelligence, such as understanding language, recognizing images, solving problems, and making decisions.
Examples:
Voice assistants (Alexa, Siri)
Self-driving cars
Chatbots
Face recognition systems
🔹 Three Major Domains of AI
1️⃣ Data (Machine Learning)
This domain deals with collecting and analyzing data.
Machines learn from data and improve their performance without being explicitly programmed.
Example:
Netflix recommending movies
YouTube suggesting videos
2️⃣ Computer Vision (CV)
Computer Vision enables machines to see and interpret images and videos.
Example:
Face recognition
Medical image analysis
Self-driving cars detecting objects
Here are the answers in easy language (Class 10 CBSE level):
✅ Unit 1 – Introduction to Artificial Intelligence
1. What is AI? Three major domains
AI (Artificial Intelligence) is a technology that enables machines to think, learn and make decisions like humans.
Three Domains of AI:
Data (Machine Learning) – Machine learns from data.
Computer Vision (CV) – Machine understands images and videos.
Natural Language Processing (NLP) – Machine understands human language.
2. Difference between AI, ML and DL
AI
ML
DL
Makes machines intelligent
Subset of AI
Subset of ML
Works like human intelligence
Learns from data
Uses neural networks
Example: Robot
Example: Recommendation system
Example: Face recognition
Diagram:
AI
└── ML
└── DL
(Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence)
3. How to know if a machine is AI based?
If the machine:
Learns from data
Improves automatically
Makes decisions
Examples:
YouTube recommendations
Face unlock in mobile
4. Two examples of AI Bias
Face recognition not working properly for dark skin people.
Hiring AI preferring male candidates over female candidates.
5. How AI is affecting lives?
Advantage: Makes work faster and easier.
Disadvantage: May reduce human jobs.
✅ Unit 2 – AI Project Cycle
1. Importance of AI Project Cycle
It gives step-by-step method to solve AI problems.
Stages:
Problem Scoping
Data Acquisition
Data Exploration
Modelling
Evaluation
2. (i) 4Ws Problem Canvas
Who – Who is affected?
What – What is the problem?
Where – Where does it occur?
Why – Why is it important?
(ii) Problem Statement Template
Clear statement that defines problem, users and expected solution.
3. Data Acquisition vs Data Exploration
Data Acquisition
Data Exploration
Collecting data
Understanding data
Surveys, internet
Checking patterns
4. Classification of AI Modelling
Rule-based
Learning-based
5. Rule-based vs Learning-based
Rule-based
Learning-based
Works on fixed rules
Learns from data
No training needed
Needs training
6. Supervised vs Unsupervised
Supervised
Unsupervised
Labeled data
No labels
Predict result
Find patterns
7. Classification vs Regression
Classification
Regression
Output is category
Output is number
Yes/No
Price prediction
8. Clustering vs Dimensionality Reduction
Clustering
Dimensionality Reduction
Groups similar data
Reduces number of features
9. Importance of Evaluation
To check model accuracy and performance.
10. Neural Networks
Computer system inspired by human brain.
Three Layers:
Input Layer – Takes data
Hidden Layer – Processes data
Output Layer – Gives result
11. Features of Neural Network
Learns from data
Handles complex problems
Improves accuracy
✅ Unit 4 – Data Science
1. What is Data Science?
Field that studies data to get useful information.
Applications:
Healthcare, banking, education, business.
2. Sources of Data
Surveys
Internet
Sensors
Government records
3. Points while collecting data
Data should be accurate
No bias
Enough quantity
4. Formats of Tabular Data
CSV
Excel
Google Sheets
✅ Unit 5 – Computer Vision
1. What is CV?
Technology that helps computers understand images.
Applications:
Face recognition, medical imaging, self-driving cars.
2. Types of CV Tasks
Image classification
Object detection
Image segmentation
3.
(i) Pixel – Smallest part of image
(ii) Resolution – Total number of pixels
(iii) Pixel Value – Brightness or color value
4. Greyscale vs RGB
Greyscale
RGB
Black & White
Colored
1 channel
3 channels
✅ Unit 6 – Natural Language Processing
1. What is NLP?
Technology that helps machines understand human language.
Applications:
Chatbots, Google Translate, Voice assistants.
2. Script-bot vs Smart-bot
Script-bot
Smart-bot
Fixed replies
Learns and responds smartly
3.
(i) Sentence Segmentation – Breaking paragraph into sentences.
(ii) Tokenization – Breaking sentence into words.
(iii) Stopword Removal – Removing words like “is, the”.
(iv) Common Case – Converting to lowercase.
(v) Stemming – Removing word ending (playing → play).
(vi) Lemmatization – Converting to root word (better → good).
4. Bag of Words returns
It returns word frequency (how many times words appear).
5. Bag of Words Implementation
Vocabulary:
Ram, Shyam, friends, plays, cricket, football
Word
Doc1
Doc2
Doc3
Ram
1
1
0
Shyam
1
0
1
friends
1
0
0
plays
0
1
1
cricket
0
1
0
football
0
0
1
