What is Machine Learning (ML)?

Machine Learning is a subset of Artificial Intelligence (AI) that focuses on building systems capable of learning patterns and making decisions or predictions directly from data, rather than following static, explicitly programmed instructions.

The Formal Definition

A widely accepted, formal definition of Machine Learning was provided by computer scientist Tom M. Mitchell in 1997:

A computer program is said to learn from Experience ($E$) with respect to some Task ($T$) and some Performance measure ($P$), if its performance on $T$, as measured by $P$, improves with experience $E$.

Let's break down this concept with a simple example: Spam Filtering.

Component	Description	Spam Filtering Example
Task ($T$)	The problem the ML system is trying to solve.	Classifying an email as "Spam" or "Not Spam (Ham)".
Experience ($E$)	The data the ML system uses to train itself.	A large dataset of historical emails labeled as either spam or ham.
Performance ($P$)	A metric used to evaluate the system's success.	Accuracy: The percentage of emails correctly classified.

tip

The core idea is that the program's ability to classify new, unseen emails gets better the more labeled examples it processes. The program learns the rules itself.

ML vs. Traditional Programming

This is the most crucial concept when starting out. Machine Learning fundamentally shifts the paradigm of software development.

Traditional Programming

In traditional programming, you (the programmer) write explicit Rules (algorithms, logic, conditions) that process Data to produce an Answer.

Example (Temperature Conversion): You explicitly write the formula: Fahrenheit = (Celsius * 9/5) + 32. The computer executes this static rule.

Machine Learning

In Machine Learning, you feed the system the Data and the desired Answers (Labels), and the system autonomously generates the Rules (the Model/Algorithm) that maps the input to the output.

Example (Predicting House Price): You feed it past house data (size, location) and the final sale price. The ML algorithm creates a complex mathematical model (the "Rule") that predicts the price of a new house based on its features.

Key Characteristics of Machine Learning

• Data-Driven: ML models require vast amounts of high-quality data to learn effectively.
• Automatic Pattern Discovery: The system discovers hidden patterns, correlations, and rules in the data without human intervention.
• Generalization: A good ML model can accurately predict or classify data it has never seen before (its performance improves with experience $E$).
• Iterative Process: Developing an ML model is a cyclical process of data collection, training, evaluation, and refinement.

Where is ML Used?

Machine Learning is the engine behind many everyday technologies:

Domain	Application	ML Task
E-commerce	Recommendation Systems (e.g., "People who bought X also bought Y")	Classification / Ranking
Healthcare	Tumor detection in X-rays or MRIs	Image Segmentation / Classification
Finance	Fraud detection in credit card transactions	Anomaly Detection / Classification
Speech	Voice assistants (Siri, Alexa)	Natural Language Processing (NLP)
Transportation	Self-driving cars	Computer Vision / Reinforcement Learning

In English

In Hindi