Machine learning is a field of artificial intelligence that gives computers the ability to learn and improve from experience without being explicitly programmed. This article provides a basic overview of machine learning concepts and techniques.
Key Concepts
- Supervised Learning: This is a type of machine learning where the algorithm learns from labeled training data. The goal is to learn a mapping from input to output.
- Unsupervised Learning: In this type of learning, the algorithm learns from unlabeled data. The goal is to find patterns and relationships in the data.
- Reinforcement Learning: This is a type of learning where an agent learns to make decisions by performing actions and receiving rewards or penalties.
Techniques
- Neural Networks: A series of algorithms that can recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
- Support Vector Machines (SVM): A supervised learning algorithm that creates a hyperplane in an N-dimensional space which distinctly classifies the data.
- Clustering: A technique for identifying groups of similar objects within a collection of data.
Resources
For more in-depth information on machine learning, you can check out our comprehensive guide on Machine Learning Fundamentals.
Common Applications
- Image Recognition: Used in applications like facial recognition and object detection.
- Natural Language Processing (NLP): Used in chatbots, language translation, and sentiment analysis.
- Medical Diagnosis: Used to analyze medical images and predict patient outcomes.
Neural Network
Further Reading
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