1. Machine Learning (ML) - Machine Learning is a core part of AI that enables systems to **learn from data and improve over time** without being explicitly programmed. Examples include recommendation systems (Netflix, YouTube), spam filters, and fraud detection.
2. Neural Networks - Neural networks are algorithms inspired by the human brain. They consist of layers of connected nodes (neurons) that process data. They are the foundation of deep learning, used in image recognition, speech recognition, and self-driving cars.
3. Natural Language Processing (NLP) - NLP allows machines to understand, interpret, and generate human language. Common applications include chatbots, voice assistants, language translation, and sentiment analysis.
4. Computer Vision - Computer Vision enables machines to see and interpret visual data like images and videos. It is widely used in facial recognition, medical imaging, object detection, and autonomous vehicles.
5. Training Data & Algorithms - AI systems rely heavily on high-quality data and well-designed algorithms. More accurate and diverse data leads to better AI performance, Algorithms define how models learn patterns and make decisions.
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