Product Brochure:
AI (Artificial Intelligence) machine learning is a subset of AI that allows computers and systems to learn from data, identify patterns, and make decisions with minimal human intervention. Instead of being explicitly programmed to perform specific tasks, machine learning algorithms improve their performance as they are exposed to more data.
There are several types of machine learning:
Supervised Learning: In this type, the model is trained on labeled data. The algorithm learns to map inputs to known outputs and improves its accuracy by minimizing errors. Examples include classification (like spam detection) and regression (like predicting house prices).
Unsupervised Learning: This method involves training on data without explicit labels. The model seeks to identify hidden patterns or groupings, often used in clustering (like customer segmentation) or anomaly detection.
Reinforcement Learning: In reinforcement learning, an agent learns by interacting with an environment and receiving feedback in the form of rewards or penalties. Over time, it learns strategies to maximize cumulative rewards. It's commonly used in areas like robotics and game-playing AI.
Deep Learning: A subset of machine learning that utilizes neural networks with many layers to handle complex tasks, such as image recognition, natural language processing, and more.
Machine learning is a powerful tool that underpins many AI applications today, from recommendation engines and voice assistants to self-driving cars and fraud detection systems.
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