7 August 2024, 04:17 PM
(This post was last modified: 7 August 2024, 07:10 PM by ruhiparveen.)
Artificial Intelligence (AI) and Machine Learning (ML) are closely related fields, with AI serving as the broad goal and ML as a specific method to achieve that goal. Here’s a simple explanation of their roles and relationship:
Artificial Intelligence (AI) is a field of computer science aimed at creating machines or systems that can perform tasks typically requiring human intelligence. These tasks include understanding natural language, recognizing patterns, solving problems, and making decisions. The ultimate goal of AI is to build systems that can think, learn, and adapt like humans.
Machine Learning (ML) is a subset of AI focused on teaching computers to learn from data and improve over time without being explicitly programmed. Instead of following a set of pre-defined rules, ML algorithms analyze patterns in data and use these patterns to make predictions or decisions. For example, an ML algorithm can be trained to recognize spam emails by analyzing examples of spam and non-spam messages.
The purpose of AI in ML is to create intelligent systems that can solve complex problems. ML provides the tools and techniques for AI systems to learn from experience and become better at tasks. In practical terms, this means AI systems can perform tasks like recommending products based on past purchases, identifying faces in photos, or translating languages more effectively as they process more data.
In summary, AI aims to replicate human-like intelligence, while ML is the method used to achieve this by enabling systems to learn from data and improve their performance over time. Together, they enable the development of smart systems that can adapt and respond to new information in a way that mimics human learning and decision-making.
If you want to learn AI course so visit here: AI course in Delhi
Artificial Intelligence (AI) is a field of computer science aimed at creating machines or systems that can perform tasks typically requiring human intelligence. These tasks include understanding natural language, recognizing patterns, solving problems, and making decisions. The ultimate goal of AI is to build systems that can think, learn, and adapt like humans.
Machine Learning (ML) is a subset of AI focused on teaching computers to learn from data and improve over time without being explicitly programmed. Instead of following a set of pre-defined rules, ML algorithms analyze patterns in data and use these patterns to make predictions or decisions. For example, an ML algorithm can be trained to recognize spam emails by analyzing examples of spam and non-spam messages.
The purpose of AI in ML is to create intelligent systems that can solve complex problems. ML provides the tools and techniques for AI systems to learn from experience and become better at tasks. In practical terms, this means AI systems can perform tasks like recommending products based on past purchases, identifying faces in photos, or translating languages more effectively as they process more data.
In summary, AI aims to replicate human-like intelligence, while ML is the method used to achieve this by enabling systems to learn from data and improve their performance over time. Together, they enable the development of smart systems that can adapt and respond to new information in a way that mimics human learning and decision-making.
If you want to learn AI course so visit here: AI course in Delhi
