Machine Learning vs. Artificial Intelligence: Key Differences Explained
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Table of Content:
Difference between Machine Learning and Artificial intelligence
Here's a table that summarizes the differences between Machine Learning and Artificial Intelligence:
| Feature | Machine Learning | Artificial Intelligence |
|---|---|---|
| Definition | A subfield of AI that focuses on the development of algorithms and statistical models to enable computers to learn from data and make predictions or decisions. | A branch of computer science that focuses on building systems and algorithms that can perform tasks that typically require human intelligence, such as perception, reasoning, and learning. |
| Approach | Predominantly data-driven, relying on the analysis of large amounts of data to identify patterns and make predictions. | Can incorporate a variety of approaches, including rule-based systems, expert systems, and machine learning, to create intelligent systems. |
| Applications | Used in a wide range of applications, including image and speech recognition, natural language processing, and predictive analytics. | AI has a wide range of applications, including robotics, computer vision, natural language processing, and expert systems. |
| Examples | Linear regression, decision trees, neural networks. | Robotics, computer vision, natural language processing, expert systems. |
In summary, Machine Learning is a subfield of AI that focuses on developing algorithms to enable computers to learn from data and make predictions, while AI is a broader field that encompasses a variety of approaches to building intelligent systems. Both fields have a wide range of applications and are playing an increasingly important role in many industries.