1. Artificial Intelligence (AI) - the simulation of human intelligence in machines that are designed to think and act like humans.
2. Machine Learning (ML) - a subfield of AI that focuses on the development of algorithms that allow computers to learn from and make predictions on data.
3. Neural Network - a type of machine learning algorithm modeled after the structure and function of the human brain.
4. Deep Learning - a type of machine learning that uses neural networks with multiple layers to analyze and make decisions on complex data.
5. Natural Language Processing (NLP) - a branch of AI that focuses on the interaction between computers and humans using natural language.
6. Robotics - the branch of engineering that deals with the design, construction, and use of robots.
7. Computer Vision - a field of study focused on enabling computers to interpret and understand visual information from the world.
8. Expert System - a computer program that uses AI techniques to imitate the decision-making abilities of a human expert.
9. Big Data - large and complex datasets that traditional data processing techniques cannot handle.
10. Reinforcement Learning - a type of machine learning where an agent learns to make decisions by performing actions and receiving rewards or penalties.
11. Chatbot - a computer program designed to simulate conversation with human users.
12. Decision Tree - a type of algorithm used in machine learning to model decisions and their consequences.
13. Genetic Algorithm - a type of optimization algorithm that uses principles of natural selection and genetics to find solutions to problems.
14. Support Vector Machine (SVM) - a type of algorithm used in machine learning for classification and regression analysis.
15. K-Nearest Neighbor (KNN) - a type of algorithm used in machine learning for classification and regression analysis.
16. Naive Bayes - a type of algorithm used in machine learning for classification and prediction.
17. Gradient Descent - a optimization algorithm used in machine learning to minimize the cost function of a model.
18. Backpropagation - a process used in training neural networks to update the weights and biases of the network.
19. Convolutional Neural Network (CNN) - a type of neural network used for image and video recognition.
20. Recurrent Neural Network (RNN) - a type of neural network used for processing sequential data, such as time series or natural language.
21. Generative Adversarial Network (GAN) - a type of neural network used for generative modeling, such as creating realistic images or videos.
22. Autoencoder - a type of neural network used for unsupervised learning, such as dimensionality reduction or anomaly detection.
23. Transfer Learning - a technique in machine learning where a model trained on one task is fine-tuned for a different but related task.
24. Hyperparameter - a parameter in machine learning that is set before training a model and cannot be learned from the data.
25. Overfitting - a problem in machine learning where a model performs well on the training data but poorly on new data, due to memorization rather than generalization.
