Advanced Concepts of Modelling in AI

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 AI is the ability of a computer to perform tasks that normally require human intelligence.

Examples:

• Chatbots
• Voice assistants
• Face recognition
• Self-driving cars

 ML is a subset of AI where computers learn from data and improve automatically without being explicitly programmed.

Examples:

• Email spam detection
• Movie recommendations
• Fraud detection
• Weather prediction

 DL is a subset of ML that uses artificial neural networks with many layers to learn complex patterns from large amounts of data.

Examples:

• Image recognition
• Speech recognition
• Language translation
• Autonomous vehicles

•  AI = Making machines intelligent.
• ML = Machines learn from data. 
• DL = Advanced ML using deep neural networks. 

• AI: Machines that can mimic human intelligence.
• ML: Machines that learn from data.
• DL: Machines that learn using deep neural networks. 

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 A label is the correct answer or output that the model is trying to predict.

Example:
For house price prediction:

• House Price = ₹50 lakh

This is the label. 

 Data that contains both features and their correct labels.

Example:

Size (sq ft)

Rooms

Price (₹ lakh)

1000

2

50

1500

3

75

Here, Price is the label, so this is labelled data.

Data that contains features only, without the correct output.

Example:

Size (sq ft)

Rooms

1200

2

1800

4

Since the price is missing, this is unlabelled data.

Term

Meaning

Feature

Input information used for prediction

Label

Correct output/answer

Labelled Data

Data with features + labels

Unlabelled Data

Data with features only

• Features: Study hours, attendance
• Label: Exam result (Pass/Fail)
• Labelled Data: Study hours + attendance + result

Unlabelled Data: Study hours + attendance only