Machine Learning (ML) is a branch of artificial intelligence (AI) that focuses on building systems that learn from data. These systems use algorithms to analyze and interpret data, learning from previous experiences to make decisions or predictions.
Key Concepts
- Supervised Learning: This is a type of ML where the model is trained on labeled data. The model learns to predict outcomes based on the input data.
- Unsupervised Learning: In this type, the model is trained on unlabeled data. The model tries to find patterns and insights in the data without any guidance.
- Reinforcement Learning: This involves an agent that learns to make decisions by performing actions in an environment to achieve a goal.
Applications
Machine Learning has a wide range of applications, including:
- Image Recognition: Used in applications like facial recognition and autonomous vehicles.
- Natural Language Processing (NLP): Used in chatbots, language translation, and sentiment analysis.
- Medical Diagnosis: Used to analyze medical images and predict diseases.
Further Reading
For more in-depth information about Machine Learning, you can explore our Machine Learning Resources section.
Visual Representation
Machine Learning Diagram