What is the Difference Between Machine Learning and Artificial Intelligence?

The main difference between machine learning and artificial intelligence lies in their scope and approach. Here's a breakdown of the differences:

• Artificial Intelligence (AI): AI is a broad field of computer science that focuses on creating intelligent machines capable of perceiving, reasoning, learning, and making decisions. It encompasses various subfields and techniques, including machine learning and deep learning. AI aims to create machines that can mimic or exceed human intelligence in specific domains.
• Machine Learning (ML): ML is a subset of AI that focuses on algorithms and models that allow computers to learn from data. ML algorithms can analyze and interpret data, identify patterns, and make predictions or decisions. It involves training models on large datasets to recognize patterns and generalize from examples. ML can be categorized into supervised, unsupervised, and reinforcement learning.

In summary, AI is the overarching concept of enabling a machine or system to sense, reason, act, or adapt like a human, while ML is an application of AI that allows machines to extract knowledge from data and learn from it.

Comparative Table: Machine Learning vs Artificial Intelligence

Machine learning and artificial intelligence are related but distinct concepts within the field of computer science. Here is a table highlighting the key differences between the two:

In summary, machine learning is a subfield of artificial intelligence that focuses on developing algorithms that can learn from and make predictions or decisions based on data. Artificial intelligence, on the other hand, is a broader discipline that encompasses machine learning and other techniques to create intelligent systems capable of performing tasks that would normally require human intelligence.