Machine Learning and
Artificial Intelligence
Introduction
Artificial Intelligence (AI) and Machine Learning (ML) are two of the most important
technologies in today’s world. They are changing the way people live, study, work,
and communicate. From smartphones and social media to hospitals and self-driving
cars, AI and ML are used almost everywhere. Although these terms are often used
together, they do not mean exactly the same thing. This assignment explains what
AI and Machine Learning are, how they work, where they are used, and their
advantages and disadvantages.
What is Artificial Intelligence?
The capacity of a computer or machine to carry out activities that typically
necessitate human intelligence is known as artificial intelligence. These tasks
include thinking, learning, problem-solving, understanding language, and making
decisions. AI systems are made to behave in certain situations like humans.
Examples of Artificial Intelligence include voice assistants like Siri or Google
Assistant, chatbots that answer questions online, facial recognition systems, and
recommendation systems used by YouTube or Netflix. These systems do not have
emotions or real thinking abilities, but they are programmed to behave intelligently
based on rules and data.
Machine Learning is a part of Artificial Intelligence. It focuses on teaching
machines how to learn from data instead of being programmed step by step. In
Machine Learning, computers study patterns in data and improve their performance
over time without direct human instructions.
For instance, Machine Learning is working in the background when you search for
something online and later see related ads. Another example is email spam filters,
which learn to recognize spam messages by analyzing past emails. The more data
a machine learning system receives, the better it becomes at making accurate
decisions.
Difference Between AI and Machine Learning?
The primary distinction between artificial intelligence (AI) and machine learning
