Artifical Intelligent and Machine Learning-AI is the overarching discipline of creating intelligent agents and systems that can perform tasks typically requiring human intelligence

          Artifical Intelligent and Machine Learning-AI is the overarching discipline of creating intelligent agents and systems that can perform tasks typically requiring human intelligence

 Artificial Intelligence (AI) is the broad field of creating systems that can perform tasks requiring human intelligence, while Machine Learning (ML) is a subfield of AI that enables these systems to learn from data and improve without explicit programming. Think of AI as the overarching concept of creating intelligent machines, and ML as one of the main methods, using statistical models and algorithms, to achieve it. 

Artificial Intelligence (AI):

AI is the overarching discipline of creating intelligent agents and systems that can perform tasks typically requiring human intelligence, such as reasoning, problem-solving, learning, perception, and natural language understanding. 

How it works: 

AI systems can use various approaches, including rule-based systems, expert systems, and data-driven methods like Machine Learning. 

Examples: 

       Siri, virtual assistants, AI-powered chat bots, and robotics are all examples of AI

. 

Machine Learning (ML):

ML is a subset of AI focused on developing algorithms that allow computers to learn from data and make predictions or decisions without being explicitly programmed for every task. 

How it works: 

ML algorithms identify patterns and extract knowledge from data, allowing the system to improve its performance on a given task over time through experience. 

Examples:

·         Image Recognition: Systems that learn to identify objects in images. 

·         Predictive Analytics: Predicting future outcomes based on historical data. 

·         Email Filtering: Learning to identify and filter spam emails.

 

Relationship between AI and ML

Ø  An Umbrella Term: AI is the larger, broader concept, encompassing many different approaches to creating intelligent machines. 

Ø  A Subset of AI: ML is one of the primary ways to build AI systems, allowing them to learn and adapt. All machine learning is AI, but not all AI is machine learning. 

Ø  Focus: AI aims to create intelligence, while ML focuses on enabling that intelligence to learn from data to perform specific tasks.

 

Applications of AI and ML:

§  Healthcare

§  Finance

§  Ecommerce and marketing

§  Autonomus Systems and Transportation

§  Cyber Security

§  Social Applications

§  Automatic Language Translation

Advantages of AI and ML:

v  Enhanced Efficiency and Productivity

v  Improved Decision Making

v  Personalized Customer Experiences

v  Cost Savings

v  Predictive Analysis

v  Reduced Human Error

v  Innovation

v  Scalability

v  Automation of Complex Tasks

Features of AI and ML:

Artifical Intelligent Features:

·         Human like Intelligent

·         Reasoning and Problem Solving

·         Decision Making

·         Adaption and Learning

·         Broad Applications

Machine Learning Features:

·         Learning from Data

·         Pattern recognition

·         Prediction and Classification

·         Statistical methods

·         Autonomus Learning