1. Machine Learning (ML)
Machine Learning is a subset of AI that focuses on building systems that can learn and improve from data without explicit programming.
- Supervised Learning: Training models on labeled data.
- Unsupervised Learning: Discovering patterns in unlabeled data.
- Reinforcement Learning: Learning through trial and error with rewards and penalties.
2. Deep Learning
Deep Learning is a specialized branch of ML that uses neural networks with multiple layers to analyze complex patterns in large datasets.
- Inspired by the structure and function of the human brain.
- Used in applications like image recognition, natural language processing, and autonomous vehicles.
3. Natural Language Processing (NLP)
NLP enables machines to understand, interpret, and generate human language.
- Key Tasks: Sentiment analysis, translation, speech recognition.
- Used in chatbots, virtual assistants, and text summarization.
4. Computer Vision
Computer Vision enables machines to interpret and make decisions based on visual data.
- Applications include object detection, facial recognition, and medical imaging.
- Involves techniques like image processing, feature extraction, and deep learning.
5. Robotics
Robotics combines AI with mechanical engineering to design intelligent machines that can perform tasks autonomously.
- Used in manufacturing, healthcare, and exploration.
- Involves motion planning, perception, and decision-making algorithms.
6. Expert Systems
Expert Systems use rule-based reasoning to simulate the decision-making ability of a human expert.
- Composed of a knowledge base and an inference engine.
- Applications include medical diagnosis and financial forecasting.
7. Reinforcement Learning
A learning paradigm where agents learn by interacting with an environment to achieve goals.
- Uses concepts of rewards and penalties to shape behavior.
- Applied in robotics, game playing, and autonomous systems.