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| Variance | An error introduced in your model due to the complex machine learning algorithm. It can lead to overfitting. |
| Unsupervised Learning | A type of machine learning where the AI is trained using unlabeled data. |
| Underfitting | A modeling error that occurs when a function is too simplistic to accurately represent the underlying data. |
| Transfer Learning | A machine learning method where a model developed for a task is reused as the starting point for a model on a second task. |
| Training Data | The dataset from which the machine learning algorithm learns the model. |
| Text Analysis | The process of converting unstructured text data into meaningful data for analysis. |
| Test Data | The dataset used to provide an unbiased evaluation of a final model fit on the training dataset. |
| Supervised Learning | A type of machine learning where the AI is trained using labeled data. |
| Speech Recognition | The ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format. |
| Sentiment Analysis | The use of natural language processing to identify, extract, quantify, and study affective states and subjective information. |
| Semantic Analysis | The process of relating syntactic structures and objects in the world to computational representations of meaning. |
| Reinforcement Learning | A type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal. |
| Regression | A process in machine learning where a model is trained to predict a continuous or semi-continuous outcome. |
| Recommendation Engine | A system that predicts the preferences or ratings a user would give to a product. |
| Programmatic Advertising | Automated bidding on advertising inventory in real time, for the opportunity to show an ad to a specific customer, in a specific context. |
| Predictive Modeling | A statistical technique using machine learning and data mining to predict and forecast likely future outcomes. |
| Predictive Analytics | The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. |
| Personalization | The use of technology and customer information to tailor digital experiences to the individual. |
| Overfitting | A modeling error that occurs when a function is too closely aligned to a limited set of data points. |
| Neural Networks (NN) | Computational models inspired by the human brain. They are used to model complex patterns and prediction problems. |
| Natural Language Processing (NLP) | A branch of AI that helps computers understand, interpret, and respond to human language in a valuable way. |
| Machine Learning (ML) | A subset of AI that involves the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something. |
| Image Recognition | The process of identifying and detecting an object or a feature in a digital image or video. |
| Generative Adversarial Networks (GANs) | A class of AI algorithms used in unsupervised machine learning, implementing two neural networks contesting with each other in a game. |
| Feature Selection/Extraction | The process of selecting a subset of relevant features for use in model construction. |
| Explainable AI (XAI) | Techniques in AI which can be trusted and easily understood by humans. |
| Evaluation Metrics | Metrics used to measure the quality of the machine learning model such as accuracy, precision, recall, F1 score, ROC, etc. |
| Ensemble Methods | Machine learning paradigm where multiple models are trained to solve the same problem and combined to get better results. |
| Deep Learning (DL) | A subset of machine learning that makes the computation of multi-layer neural networks feasible. It is responsible for advancements in image and speech recognition. |
| Data Set | A collection of related sets of information composed of separate elements but can be manipulated as a unit by a computer. |
| Data Mining | The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. |
| Computer Vision | The ability for a computer to understand and interpret visual information from the world. |
| Classification | A process in machine learning where a model is trained to predict the category of a given input. |
| Chatbots | Programs that can have a conversation with a human, often used in customer service. |
| Big Data | Extremely large data sets that may be analyzed computically to reveal patterns, trends, and associations, especially relating to human behavior and interactions. |
| Bias | An error introduced in your model due to oversimplification of the machine learning algorithm. It can lead to underfitting. |
| AutoML (Automated Machine Learning) | The process of automating the process of applying machine learning to real-world problems. |
| Anomaly Detection | The identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. |
| Algorithm | A set of rules to be followed in calculations or problem-solving operations, often by a computer. |
| AI (Artificial Intelligence) | A branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that responds in a manner similar to human intelligence. |