Unraveling the Power of Machine Learning (ML)

Glossary/M/Machine Learning (ML)

What is Machine Learning (ML)?

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that focuses on developing algorithms and models that enable computers to learn from data and make predictions or take actions without being explicitly programmed. ML algorithms are designed to automatically identify patterns, relationships, and insights within large datasets, allowing machines to improve their performance over time. Through various algorithms and techniques, ML has the potential to revolutionize industries and solve complex problems, making it an essential tool in today's data-driven world.

The fundamental concept behind ML is to train a model using historical or labeled data, and then use that model to make predictions or decisions on new, unseen data. The process involves several key steps: data collection, data preprocessing, model training, and model evaluation. During training, the ML algorithm learns from the data by adjusting its internal parameters to minimize errors or optimize a specific objective function.

Related terms

Artificial Intelligence (AI)

Artificial Intelligence (AI) is a rapidly advancing field of computer science that focuses on develo...

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

Not to be confused with:

Artificial Intelligence (AI)

Artificial Intelligence (AI) is a rapidly advancing field of computer science that focuses on develo...

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

Artificial Intelligence (AI)

Artificial Intelligence (AI) is a rapidly advancing field of computer science that focuses on develo...

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

Artificial Intelligence (AI)

Artificial Intelligence (AI) is a rapidly advancing field of computer science that focuses on develo...

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

ML algorithms can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm learns from labeled data, where each example is associated with a known target or output. It uses this labeled data to make predictions or classify new, unseen instances. Unsupervised learning, on the other hand, involves training the algorithm on unlabeled data. The algorithm identifies patterns, clusters, or structures within the data without any predefined targets. It aims to discover hidden relationships and insights in the data. Reinforcement learning involves training an agent to interact with an environment and learn from the feedback it receives. The agent takes actions in the environment to maximize a reward signal. Over time, it learns to make better decisions and optimize its behavior.

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