What is Machine Learning (ML)

The machine learning (e.g., ML) is the part of AI, that can learn and improve learning automatically using the data. Some of the real world examples of the ML include virtual personal assistants, video surveillance, malware filtering, etc.

ML is a subset of AI that trains algorithms to make predictions and decisions based on input data, rather than being explicitly programmed to perform a specific task.

A subset of artificial intelligence aimed at enabling computers to analyze and learn from huge amounts of data with a view to making forecasts.

ML is a branch of Artificial Intelligence that uses statistical techniques or algorithms to allow a computer to become better at what it does. The computer classifies patterns in behavior or speech, such as, it notes those differences to “learn” more about a specific user or set of users.

A type of AI that enables machines to learn from data without being explicitly programmed, and improve their performance over time.

Automated learning concept that uses different methods and models to learn from past experience through the analysis of historical data.

A field in computer science that focuses on developing algorithms that can learn and improve from data without being explicitly programmed (Mahesh, 2020 AU133: The in-text citation "Mahesh, 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Bertolini et al., 2021 AU134: The in-text citation "Bertolini et al., 2021" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). The aim is to create intelligent machines that can learn from experience and make predictions or decisions.

Learning is a very broad term that describes the process by which humans or machines can increase their knowledge. Machine learning relies on the application of inductive, deductive, abductive, or analogous techniques. To learn is to reason, to discover analogies and similarities, to generalize or particularize an experience, to take advantage of past failures and errors for subsequent reasoning. New learning is used to solve new problems, accomplish a new task, or increase performance in accomplishing an existing task, explain a situation or predict behaviour. ML facilitates the transfer of knowledge, in particular from experimental examples. It contributes to the development of knowledge bases of Knowledge Base Systems while reducing the intervention of the knowledge engineer (cognitician). In our approach, the learning exploits the historical database (accident or incident scenarios) to generate new knowledge that can help certification experts to assess the degree of safety of a new transport system.

Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn and make predictions or decisions based on data.

ML an application of AI it gives devices the ability to learn from their experiences and improve their self without doing any coding.

A subfield of artificial intelligence (AI) that enables machine systems to automatically learn, self-improvement from past experience, and make decisions without being explicitly programmed.

It is a subset of artificial intelligence. The main characteristic of this discipline is related to the study of algorithms (including statistical models) that can be used by computerized systems in order to perform learning and recognition tasks however without using extensive and explicit instructions, instead learning of patterns and inference is used.

Machine Learning is the field of Artificial Intelligence that studies developing algorithms that help AI machines learn and enhance from experience.

Allows computers to develop algorithms, learn from data, and make predictions without explicit human programming.

Subset of AI, that is the development of algorithms and statistical models used by computer systems in order to effectively perform a specific task without using explicit instructions.

Machine learning is an area of artificial intelligence that allows computer systems to learn and enhance their performance via experience, without explicit being programmed.

Computer algorithms which improve through the use of data, without following explicit instructions. Part of artificial intelligence.

ML is a subset of artificial intelligence (AI) that focuses on developing algorithms and statistical models enabling computer systems to improve their performance on a specific task through learning from data and experiences. ML algorithms enable computers to identify patterns, make predictions, and improve their decision-making without being explicitly programmed for each task. It finds applications in various fields, including data analysis, pattern recognition, natural language processing, and autonomous systems.

A subfield of Artificial Intelligence that involves the development of systems with autonomous learning. Machine learning implies that computers accumulate experience and improve their performance using data.

A branch of AI that focuses on the use of statistical models and techniques to enable computers to become better at tasks over time.

Scientific discipline that investigates algorithms and methods aiming at giving machines the ability to learn from experience (without being explicitly programed), in order to respond autonomously on specific tasks and automate various data-handling processes.

Machine Learning techniques may be used for a variety of image processing tasks including feature extraction, scene analysis, object detection, hypothesis generation, model building and model instantiation.

The development of algorithms that enable machines to mimic human intelligence.

A subfield of AI, machine learning focuses on algorithms that can learn from data without explicit programming. Machine learning algorithms are trained on large datasets to identify patterns and relationships within the data.

A technology that induces the learning capabilities within the machines through algorithms and computing power for generating predictive capability.

A branch of AI that involves the development of algorithms that can learn from and make predictions based on data. ML techniques are increasingly used in medical diagnostics to analyze imaging and pathology data for disease detection and classification.

A subset of AI, Machine Learning involves algorithms that enable computers to learn and adapt from experience without being explicitly programmed. ML focuses on developing systems that can access and use data to learn for themselves.

It is subset of AI that involves development of algorithms and models that enable computers to perform specific tasks.

A type of artificial intelligence that learns from data without being explicitly programmed (Binkhonain & Zhao, 2019 AU98: The in-text citation "Binkhonain & Zhao, 2019" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Bunod et al., 2022 AU99: The in-text citation "Bunod et al., 2022" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Mahesh, 2020 AU100: The in-text citation "Mahesh, 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

It is the study of certain rigorous techniques which escalates the performance by gaining experience and via utilization of several data.

Is a computer science field that enables computers to learn without explicit programming.

An application of AI that enables systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves.

ML is a branch of Artificial Intelligence (AI) that helps machines to learn from the collected data and past experiences. In ML, machines make predictions recognizing the similar pattern from the data.

Is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

A subset of AI where algorithms use data to learn and make predictions or decisions. Instead of being explicitly programmed, these algorithms improve automatically through experience.

Machine learning is a subset of artificial intelligence that involves algorithms and statistical models allowing computer systems to improve their performance on a specific task over time without explicit programming.

Machine Learning is a subset of AI that focuses on the development of algorithms and statistical models that enable computers to improve their performance on specific tasks through learning from data.

A subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models enabling computer systems to improve their performance on tasks without explicit programming. ML involves the automatic learning of patterns and insights from data, allowing systems to make predictions, classifications, or decisions based on experience and training rather than explicit programming.

ML commonly used alongside AI and is a subset of AI. ML refers to systems that can learn from data, i.e., systems that get smarter by learning over time without direct human intervention.

A powerful tool for pattern classification. It uses the theory of statistics in building mathematical models, and programs computers to optimize a performance criterion using example data or past experience.

A branch of AI and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

A computer program having the capability to learn and adapt to new data without human assistance.

A subset of AI that uses algorithms and statistical models to enable computers to perform tasks without explicit instructions by relying on patterns and inference.

Subset of Artificial Intelligence field focused on the acquisition and process of data aiming to produce knowledge and reasonable explanations.

Type of AI that enables self-learning from data and applies that learning without human intervention by identifying patterns in data, especially diverse and high-dimensional data. In this model, data is usually aggregated from several edge devices in a centralized server and trained.

A branch of computer science that uses data and specific algorithms to imitate how humans think and learn.

It refers to the concept of training the machine with a given dataset to automate the system's decision-making and imitate the ways humans learn.

ML is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead.

Software algorithms that enable the application of AI techniques as the employ the processing of data to add value and incrementally improve automatically as they learn from the extracted information. The learning process is through the analysis of the masses of data available and identifying patterns while performing decisions based on the algorithm programmed by the AI developer.

ML is a branch of artificial intelligence. ML is a way to realize AI. By using ML, users may solve AI problems. ML theory is mainly about designing and analyzing algorithms that enable computers to “learn” automatically. ML algorithm, as a means, allow users to predict unknown data.

It is a sub field of artificial intelligence that identifies patterns in human conversations and can be used to create chatbots.

A set of activities which leverage modern computer algorithms to process data and solve vast numbers of applicable or scientific tasks.

AI subfield and evolving branch of computational algorithms that are designed to emulate human intelligence by learning from the surrounding environment.

Technology is a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. ML algorithms use historical data as input to predict new output values.

It is the subset of Artificial Intelligence which helps the system to learn from the dataset without having any specific programs.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed and in the process developing computer programs that can access data and use it to learn for themselves.

ML is a subset of AI that involves training algorithms to make predictions or decisions based on input data, rather than being explicitly programmed to perform a specific task.

A part of artificial intelligence that is the study of computer algorithms that improve automatically through experience. Machine learning algorithms build a model based on sample data, known as “training data,” in order to make predictions or decisions without being explicitly programmed to do so ( Wikipedia, 2021b ).

Is a subset of AI dedicated to crafting algorithms and statistical models that enable computers to execute tasks without explicit programming. ML algorithms leverage data to discern patterns, make forecasts, and enhance performance progressively

A branch of AI that automates data-driven analytical modelling.

is a subset of artificial intelligence (AI) that involves the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through learning from data, without being explicitly programmed.

It is an artificial intelligence branch enabling systems to learn and improve from data without explicit programming. By identifying patterns and making decisions, ML applications span from recommendation systems to autonomous driving. It emphasizes minimal human intervention, evolving through experience to enhance decision-making processes across various domains.

The development of algorithms that enable machines to mimic human intelligence.

A subset of AI involving the use of algorithms and statistical models to enable systems to improve their performance on a specific task through experience and data analysis without being explicitly programmed.

Algorithms and statistical models allow computers to learn and improve without being programmed.

Machine learning is a field of artificial intelligence (AI) that involves developing algorithms and models that enable computers to learn and improve their performance on tasks from data, rather than being explicitly programmed. In essence, it's the science of enabling computers to make predictions, recognize patterns, and make decisions based on data and experience.

A subset of artificial intelligence that enables systems to learn from data, improving their performance over time without explicit programming.

A type of artificial intelligence that enables self-learning from data and then applies that learning without the need for human intervention.

Is a subset of artificial intelligence (AI) that empowers computers to learn and improve from experience without being explicitly programmed. It enables systems to analyze data, identify patterns, and make informed decisions, allowing them to evolve and adapt to new information over time. ML algorithms leverage statistical techniques to enable machines to perform tasks and make predictions or decisions without explicit programming for each task.

Machine learning is a new technology where machine learns from the past data in order to decide and work for future data, thus helps in process of automation.

A set of AI techniques to develop computer systems that learn statistical regularities between inputs and outputs, thereby generating outputs from a set of inputs alone.

Machine Learning (ML) is a subset of AI that involves the development of algorithms and statistical models that enable computers to learn and improve from experience without being explicitly programmed. ML systems analyze large amounts of data to identify patterns and make predictions or decisions based on that data. Applications of ML include recommendation systems, fraud detection, and image recognition.

The machine learning field is a branch of artificial intelligence (AI) that focuses on using data and algorithms to mimic how people learn, gradually increasing the accuracy of its predictions.

A subset of artificial intelligence where algorithms are programmed to learn from data and improve themselves over time without explicit programming. Machine learning models can detect patterns in data and make predictions. Libraries can leverage ML for tasks like personalized book recommendations based on borrowing history.

A subset of AI that learns the input dataset using different algorithms for a variety of tasks.

Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.

Is a class of methods of Artificial Intelligence, whose characteristic feature is not a direct solution to the problem, but learning in the process of applying solutions to many similar tasks.

It is a study of algorithms and statistical model to perform specific tasks.

Machine learning is a branch of artificial intelligence (AI) that enables computers to learn from data and improve their performance on tasks without being explicitly programmed.

Computer algorithms that mimic human intelligence capable of learning from the patterns of events happening in the surrounding environment.

A powerful tool for pattern classification. It uses the theory of statistics in building mathematical models, and programs computers to optimize a performance criterion using example data or past experience.

ML is a subset of AI that often uses statistical techniques to give machines the ability to “learn” from data without being explicitly given instructions for how to do so. This process is known as “training” a “model” using a learning “algorithm” that progressively improves model performance on a specific task.