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About

Machine Learning (ML) is a branch of artificial intelligence (AI) focused on building systems that learn from and make decisions based on data. This evolving field enables computers to improve their performance on tasks through experience, without being explicitly programmed.

What is Machine Learning?​

Machine Learning involves algorithms that can analyze patterns in data and make predictions or decisions. Unlike traditional programming, where humans explicitly define the rules, ML algorithms adjust their parameters based on the input data, leading to adaptive and data-driven decision-making.

Types of Machine Learning​

1. Supervised Learning​

In supervised learning, algorithms are trained on labeled datasets. They learn to predict outcomes based on input data. Common applications include image and speech recognition, and medical diagnosis.

2. Unsupervised Learning​

Unsupervised learning deals with unlabeled data. The algorithm tries to understand the underlying structure of the data, often used in clustering and association tasks like market basket analysis.

3. Reinforcement Learning​

Reinforcement learning involves training models to make a sequence of decisions. The model learns to achieve a goal in an uncertain, potentially complex environment. It is used in areas like robotics and gaming.

Applications of Machine Learning​

  • Healthcare: Diagnostic systems, personalized treatments, and drug discovery.
  • Finance: Fraud detection, algorithmic trading, and risk management.
  • Retail: Personalized customer experiences, inventory management, and recommendation systems.
  • Transportation: Autonomous vehicles and predictive maintenance.
  • Manufacturing: Quality control and supply chain optimization.