1. Introduction to Machine Learning
What is Machine Learning?
Machine Learning (ML) is a subset of Artificial Intelligence that enables computers to
learn from data and improve their performance without being explicitly programmed.
In ML:
● You feed historical data →
● ML learns patterns →
● It makes predictions on new data.
,Real-life Examples
Situation How ML Works
Instagram Recommends reels based on what you watched
Reels earlier.
Google Maps Predicts travel time using past traffic patterns.
Spam Filter Learns patterns of spam emails and blocks them.
Loan Approval Banks predict your credit risk using past customer
data.
Key Points
● The more the data, the better the prediction.
● ML improves from experience over time.
How Does ML Work?
ML follows a simple cycle:
Historical Data → Train the Model → Build Pattern → Predict on New
Data
Simple Diagram (Text-Based)
DATA → TRAINING → MODEL → PREDICTION
(Past examples) (Learns) (New output)
Real Example
If you give ML past house prices & features (size, location, rooms), it will learn how price
changes and can predict the price of a new house.
2. Dataset & Types of Data
, What is a Dataset?
A dataset is a structured collection of data used by ML to learn patterns.
Real-life dataset examples:
● Netflix: Database of movies, ratings, watch history
● Flipkart: Product details, reviews, prices
● Hospitals: Patient symptoms, diagnosis, test results
Types of Data
1. Numerical Data
Quantitative, measurable values.
Examples:
● Temperature: 32°C
● Age: 21 years
● House price: ₹50,00,000
2. Categorical Data
Qualitative labels or categories.
Examples:
● Gender (Male/Female)
● Disease (Yes/No)
● Traffic Light (Red/Yellow/Green)
3. Types of Datasets: Training vs Testing
What is Machine Learning?
Machine Learning (ML) is a subset of Artificial Intelligence that enables computers to
learn from data and improve their performance without being explicitly programmed.
In ML:
● You feed historical data →
● ML learns patterns →
● It makes predictions on new data.
,Real-life Examples
Situation How ML Works
Instagram Recommends reels based on what you watched
Reels earlier.
Google Maps Predicts travel time using past traffic patterns.
Spam Filter Learns patterns of spam emails and blocks them.
Loan Approval Banks predict your credit risk using past customer
data.
Key Points
● The more the data, the better the prediction.
● ML improves from experience over time.
How Does ML Work?
ML follows a simple cycle:
Historical Data → Train the Model → Build Pattern → Predict on New
Data
Simple Diagram (Text-Based)
DATA → TRAINING → MODEL → PREDICTION
(Past examples) (Learns) (New output)
Real Example
If you give ML past house prices & features (size, location, rooms), it will learn how price
changes and can predict the price of a new house.
2. Dataset & Types of Data
, What is a Dataset?
A dataset is a structured collection of data used by ML to learn patterns.
Real-life dataset examples:
● Netflix: Database of movies, ratings, watch history
● Flipkart: Product details, reviews, prices
● Hospitals: Patient symptoms, diagnosis, test results
Types of Data
1. Numerical Data
Quantitative, measurable values.
Examples:
● Temperature: 32°C
● Age: 21 years
● House price: ₹50,00,000
2. Categorical Data
Qualitative labels or categories.
Examples:
● Gender (Male/Female)
● Disease (Yes/No)
● Traffic Light (Red/Yellow/Green)
3. Types of Datasets: Training vs Testing
