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Glossary

Machine Learning – ML

What is machine learning?

Machine learning is a branch of artificial intelligence (AI) and computer science that involves the use of data and algorithms to enable computers to learn and make predictions or decisions without being explicitly programmed for each task.

The fundamental idea behind machine learning is to create algorithms and models that can analyze and learn from data, identify patterns, and make informed predictions or take actions based on that learning. This learning process is inspired by the way humans learn from experience, which is why it’s often referred to as “imitating the way humans learn.”

Machine learning algorithms can be broadly categorized into supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm learns from labeled examples, where the desired output or prediction is provided along with the input data. In unsupervised learning, the algorithm learns patterns and structures from unlabeled data, without explicit output labels. Reinforcement learning involves an agent learning through interaction with an environment and receiving feedback or rewards for its actions.

Why use machine learning?

Instead of explicitly programming a computer with specific instructions, machine learning algorithms are designed to automatically learn and improve their performance over time as they are exposed to more input data. They can generalize from past examples to handle new, unseen data and make predictions or decisions based on learned patterns.

Machine learning has found applications in various fields, including image and speech recognition, natural language processing, recommendation systems, autonomous vehicles, fraud detection, and many others. By leveraging large amounts of data and powerful algorithms, machine learning has enabled significant advancements in AI and has the potential to solve complex problems and make intelligent decisions.

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