Machine Learning and Artificial Intelligence are often used interchangeably but they are not the same. There are several distinctions between the two terms, which are crucial to understand.
Definitions
Artificial Intelligence is a broad field that encompasses all aspects of developing machines with cognitive abilities that were once attributed to humans. Machine Learning is the subset of AI that enables machines to learn from data without being explicitly programmed.
Goal
The primary goal of AI is to create machines that can perform tasks, which might require intelligence when done by humans. On the other hand, Machine Learning focuses on creating algorithms that can learn from data and make predictions.
Data Dependence
AI can operate without data input, but machine learning algorithms are entirely dependent on data to learn and improve. Therefore, machine learning algorithms require large data sets to learn, whereas AI does not necessarily need data for its operation.
Complexity
AI is far more complex than machine learning, as it requires reasoning, perception, and natural language processing. Machine learning algorithms can be broken down into simpler tasks, such as clustering and classification of data.
Application
AI is typically used for creating intelligent systems that can mimic human decision-making abilities, such as self-driving cars and digital assistants. Machine Learning is used in various applications such as image and speech recognition, fraud detection, and predictive maintenance.
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