Here are some common AI terms used:

**Artificial Intelligence (AI)**: A field of computer science that aims to create intelligent machines that can perform tasks that normally require human intelligence, such as learning, problem-solving, and decision-making.**Machine Learning (ML)**: A field of computer science that involves the development of algorithms that can learn from data and improve their performance over time.**Deep Learning**: A subfield of machine learning that involves training artificial neural networks on large datasets to recognize patterns and make decisions.**Neural Networks**: A type of machine learning model inspired by the structure and function of the human brain, composed of interconnected layers of artificial neurons.**Natural Language Processing (NLP)**: A subfield of AI that focuses on enabling computers to understand, interpret, and generate human language.**Computer Vision**: A subfield of AI that involves the development of algorithms and systems that can analyze and understand visual data, such as images and video.**Supervised Learning**: An ML approach where the model is trained on labeled data, meaning each training example is paired with an output label.**Unsupervised Learning**: An ML approach where the model is trained on unlabeled data and must find patterns and relationships in the data on its own.**Reinforcement Learning**: A type of ML where an agent learns to make decisions by performing actions and receiving rewards or penalties.**Algorithm**: A set of rules or steps used to solve a problem or perform a computation in a finite number of steps.**Big Data**: Large and complex data sets that traditional data-processing software cannot manage or process efficiently.**Data Mining**: The practice of examining large databases to generate new information and find hidden patterns.**Chatbot**: An AI application that can conduct a conversation with a human user through text or voice interactions.**Cognitive Computing**: A branch of AI that aims to simulate human thought processes in a computerized model.**Robotics**: The field of engineering and science involving the design, construction, operation, and use of robots.**Predictive Analytics**: Techniques that use statistical algorithms and machine learning to identify the likelihood of future outcomes based on historical data.**Training Data**: The dataset used to train an AI or ML model, typically containing input-output pairs.**Model**: In ML, a mathematical representation of a real-world process created by training on data.**Feature Extraction**: The process of transforming raw data into a set of features that can be used in modeling.**Artificial Neural Network (ANN)**: A computing system designed to work like the human brain, processing data in a way similar to biological neurons.**Convolutional Neural Network (CNN)**: A type of deep neural network commonly used for analyzing visual imagery.**Recurrent Neural Network (RNN)**: A type of neural network where connections between nodes form a directed graph along a temporal sequence, useful for sequence prediction problems.**Generative Adversarial Network (GAN)**: A class of ML frameworks where two neural networks contest with each other to create more accurate outputs.**Transfer Learning**: A technique that involves using a pre-trained machine learning model as a starting point for a new task, and fine-tuning it on the new data.**Hyperparameter**: Configuration settings used to structure an ML model that must be set before the learning process begins.**Overfitting**: A modeling error that occurs when an ML model learns the details and noise in the training data to the extent that it negatively impacts the performance on new data.**Underfitting**: A modeling error that occurs when an ML model is too simple to capture the underlying trend of the data.**Gradient Descent**: An optimization algorithm used to minimize the loss function in ML models.**Stochastic Gradient Descent (SGD)**: An optimization algorithm that involves updating the parameters of a machine learning model using a randomly selected subset of the training data, rather than the full dataset.**Mini-batch Gradient Descent**: An optimization algorithm that involves updating the parameters of a machine learning model using a small batch of the training data, rather than the full dataset or a single sample.**Backpropagation**: An algorithm that is used to train artificial neural networks by propagating the error gradient back through the network to update the weights.**Loss Function**: A method of evaluating how well a specific algorithm models the given data, used in training ML models.**Epoch**: One complete pass through the entire training dataset in the context of ML model training.**Bias**: A systematic error in an ML model that causes it to consistently learn the wrong pattern.**Variance**: The model’s sensitivity to fluctuations in the training data, which can lead to overfitting.**Clustering**: An unsupervised ML technique that groups similar data points into clusters.**Principal Component Analysis (PCA)**: A dimensionality-reduction technique used to reduce the complexity of data while preserving as much variability as possible.**K-Nearest Neighbors (KNN)**: A type of machine learning model that involves classifying a sample based on its proximity to the k most similar samples in the training dataset, used for classification and regression tasks.**Support Vector Machine (SVM)**: A type of machine learning model that involves finding the hyperplane in a high-dimensional space that maximally separates different classes, used for classification and regression tasks.**Decision Tree**: A type of machine learning model that involves constructing a tree-like structure of decisions and their potential consequences, used for classification and prediction tasks.**Random Forest**: A type of machine learning model that involves training multiple decision trees on random subsets of the data and aggregating their predictions, used for classification and regression tasks.**Naive Bayes**: A type of machine learning model that involves calculating the probability of a sample belonging to each class based on the probability of each feature given the class, used for classification tasks.**Logistic Regression**: A type of machine learning model that involves predicting the probability of a binary outcome based on a linear combination of the input features, used for classification tasks.**Linear Regression**: A type of machine learning model that involves predicting a continuous outcome based on a linear combination of the input features, used for regression tasks.**Regularization**: A technique used to prevent overfitting by adding a penalty term to the objective function, which encourages the model to have simpler and more generalizable solutions.**Cross-Validation**: A technique used to evaluate the performance of a machine learning model by training it on different subsets of the data and averaging the results.**Feature Engineering**: The process of selecting, creating, and transforming the input features of a machine learning model to improve its performance.**Feature Selection**: The process of selecting a subset of the input features of a machine learning model based on their importance or relevance to the task.**Dimensionality Reduction**: The process of reducing the number of input features in a dataset, often to improve model performance and reduce computational cost.**Model Selection**: The process of choosing the most appropriate machine learning model for a given task, based on its performance on a validation dataset.**Ensemble Learning**: A technique that involves training multiple models and combining their predictions to improve the overall performance, such as through voting or averaging.**Activation Function**: A function used in neural networks to introduce non-linearity into the model, helping the network learn complex patterns.**Anomaly Detection**: The identification of rare items, events, or observations which raise suspicions by differing significantly from the majority of the data.**Recommendation System**: An ML system that provides personalized recommendations to users based on their behavior and preferences.**Turing Test**: A test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.**Perceptron**: A type of artificial neuron used in ML for binary classifiers.**Learning Rate**: A hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated.**TensorFlow**: An open-source ML framework developed by Google for building and deploying ML models.**PyTorch**: An open-source ML library developed by Facebook’s AI Research lab, used for applications such as computer vision and natural language processing.