Machine Learning
Machine learning, a subset of AI, involves training algorithms on large datasets to make predictions or decisions without being explicitly programmed. It continuously improves by learning from new data, making it essential for automation and intelligent decision-making. Some common machine learning techniques include:
Supervised Learning: Models are trained on labeled data to predict outcomes (e.g., spam detection).
Unsupervised Learning: Models identify patterns in unlabeled data (e.g., customer segmentation).
Reinforcement Learning: Models learn through trial and error to maximize rewards (e.g., recommendation systems).
Machine Learning: The Power Behind AI
- Machine Learning Overview
Machine Learning (ML) is a branch of AI that enables systems to learn from data and improve performance without explicit programming. It helps in pattern recognition, predictions, and automation. - Types of Machine Learning:
ML is categorized into Supervised Learning (trained on labeled data), Unsupervised Learning (finds patterns in unlabeled data), and Reinforcement Learning (learns through trial and error). - Key Techniques
Popular ML techniques include Decision Trees, Neural Networks, and Deep Learning, which powers applications like image recognition and speech processing. - Prescriptive Analytics:
Prescriptive analytics provides recommendations on what actions should be taken to - Applications & Impact
ML is transforming industries like healthcare, finance, security, and automation, driving innovation and smarter decision-making.
Key Applications of Machine Learning
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