"Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed." (Arthur Samuel, 1959) [attributed]
"Computer methods for accumulating, changing, and updating knowledge in an AI computer system." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)
"A term often used to denote the application of generic model-fitting or classification algorithms for predictive data mining. This differs from traditional statistical data analysis, which is usually concerned with the estimation of population parameters by statistical inference and p-values. The emphasis in data mining machine learning algorithms is usually on the accuracy of the prediction as opposed to discovering the relationship and influences of different variables." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)
"A discipline grounded in computer science, statistics, and psychology that includes algorithms that learn or improve their performance based on exposure to patterns in data, rather than by explicit programming." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)
"Machine learning is the intersection between theoretically sound computer science and practically noisy data. Essentially, it’s about machines making sense out of data in much the same way that humans do." (Matthew Kirk, "Thoughtful Machine Learning", 2015)
"Computer programs that have the ability to learn over time as new data becomes available. This type of analytical programming can learn more about a customer’s online shopping behavior over time and start to predict which items the customer will likely click on and purchase." (Brittany Bullard, "Style and Statistics", 2016)
"Machine learning is home to numerous techniques for creating classifiers by training them with already correctly categorized examples. This training is called supervised learning; it is supervised because it starts with instances labeled by category, and it involves learning because over time the classifier improves its performance by adjusting the weights for features that distinguish the categories. But strictly speaking, supervised learning techniques do not learn the categories; they implement and apply categories that they inherit or are given to them." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)
"A subdiscipline of computer science that addresses similar challenges to traditional statistical modeling, but with different techniques and a stronger focus on predictive accuracy." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)
"Machine learning describes a broad set of methods for extracting meaningful patterns from existing data and applying those patterns to make decisions or predictions on future data." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)
"Machine learning is a method of designing systems that can learn, adjust, and improve based on the data fed to them. Machine learning works based on predictive and statistical algorithms that are provided to these machines. The algorithms are designed to learn and improve as more data flows through the system." (Amar Sahay, "Business Analytics" Vol. I, 2018)
"The field of computer science research that focuses on developing and evaluating algorithms that can extract useful patterns from data sets. A machine learning algorithm takes a data set as input and returns a model that encodes the patterns the algorithm extracted from the data." (John D Kelleher & Brendan Tierney, "Data science", 2018)
[In-Database Machine Learning:] "Using machine-learning algorithms that are built into the database solution. The benefit of in-database machine learning is that it reduces the time spent on moving data in and out of databases for analysis." (John D Kelleher & Brendan Tierney, "Data science", 2018)
"The science of developing techniques to give the computer inference and deduction capabilities to achieve diverse processing tasks autonomously." (Jorge Manjarrez-Sanchez, "In-Memory Analytics", 2018)
"A facet of AI that focuses on algorithms, allowing machines to learn without being programmed and change when exposed to new data." (Kirti R Bhatele et al, "The Role of Artificial Intelligence in Cyber Security", 2019)
"A field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn." (Nil Goksel & Aras Bozkurt, "Artificial Intelligence in Education: Current Insights and Future Perspectives", 2019)
"A method of designing a sequence of actions to solve a problem that optimizes automatically through experience and with limited or no human intervention." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2019)
"The methods used to understand the patterns in the data and to obtain results from these patterns using various algorithms." (Tolga Ensari et al, "Overview of Machine Learning Approaches for Wireless Communication", 2019)
"A branch of artificial intelligence that focuses on data analysis methods that allow for automation of the process of analytical model building." (Timofei Bogomolov et al, "Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques", 2020)
"A discipline focused on the development and evaluation of algorithms that permit computers to use patterns, trends, and associations in data to perform tasks without being programmed by a human." (Osman Kandara & Eugene Kennedy, "Educational Data Mining: A Guide for Educational Researchers", 2020)
"A field of study of algorithms and statistical methods that allows software application to predict the accurate result." (S Kayalvizhi & D Thenmozhi, "Deep Learning Approach for Extracting Catch Phrases from Legal Documents", 2020)
"Is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed." (Rajandeep Kaur & Rajneesh Rani, "Comparative Study on ASD Identification Using Machine and Deep Learning", 2020)
"Is one of many subfields of artificial intelligence concerning the ways that computers learn from experience to improve their ability to think, plan, decide and act." (Lejla Banjanović-Mehmedović & Fahrudin Mehmedović, "Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0", 2020)
"It is an application of the artificial intelligence in which machines can automatically learn and solve problems using the learned experience." (Shouvik Chakraborty & Kalyani Mali, "An Overview of Biomedical Image Analysis From the Deep Learning Perspective", 2020)
"It refers to an application of artificial intelligence focusing on algorithms which can be used for building models (e.g., based on statistics) from input data. Such automatic analytical models need to provide outputs based on the learning relations between input and output values. The algorithms are often categorized as supervised, semi-supervised or unsupervised." (Ana Gavrovska & Andreja Samčović, "Intelligent Automation Using Machine and Deep Learning in Cybersecurity of Industrial IoT", 2020)
"Machine learning, in the simplest terms, is the analysis of
statistics to help computers make decisions base on repeatable characteristics
found in the data." (Vardhan K Agrawal, "Mastering Machine Learning with Core ML
and Python", 2020)
"Machine learning is a field of computer science and mathematics that focuses on algorithms for building and using models “learned” from data." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)
"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." (Mohammad Haroon et al, Application of Machine Learning In Forensic Science, 2020)
"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. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves." (R Murugan, "Implementation of Deep Learning Neural Network for Retinal Images", 2020)
"Machine learning is branch of data science which has concern with the design and development of algorithm to develop a system that can learn from data, identify the complex patterns and provide intelligent, reliable, repeatable decisions and results with minimal human interaction based on the provided input." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)
"A computer program having the capability to learn and adapt to new data without human assistance." (Sue Milton, "Data Privacy vs. Data Security", 2021)
"A rising area in computer science, where the computer systems are programmed to learn information from rich data sets to produce reliable results to a given problem." (Jinnie Shin et al, "Automated Essay Scoring Using Deep Learning Algorithms", 2021)
"Ability of a machine to learn from the data it is presented using different techniques that are supervised or non-supervised." (Sujata Ramnarayan, "Marketing and Artificial Intelligence: Personalization at Scale", 2021)
"Is a type of artificial intelligence where computer teaches itself the solution to a query discovering patterns in sets of data and matching fresh parts of data the based on probability." (James O Odia & Osaheni T Akpata, "Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection", 2021)
"It is again a sub set of AI in which we classify the data with the help of input data set, ANN, SVM, Random Forest are some of the algorithm used in this case." (Ajay Sharma, "Smart Agriculture Services Using Deep Learning, Big Data, and IoT", 2021)
"It refers to developing the ability in computers to use available data to train themselves automatically, and to learn from its own experiences without being explicitly programmed." (Shatakshi Singhet al, "A Survey on Intelligence Tools for Data Analytics", 2021)
"Machine learning is a scientific approach to analyse available data using algorithms and statistical models to accomplish a specific task by utilizing the patterns evolved." (Vandana Kalra et al, "Machine Learning and Its Application in Monitoring Diabetes Mellitus", 2021)
"Machine Learning is a statistical or mathematical model that performs data analysis, prediction, and clustering. This science is a subfield of Artificial Intelligence." (Sayani Ghosal & Amita Jain, "Research Journey of Hate Content Detection From Cyberspace", 2021)
"Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed." (Sercan Demirci et al, "Detection of Diabetic Retinopathy With Mobile Application Using Deep Learning", 2021)
"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." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)
"Set of knowledge discovery techniques for intelligent data analysis in order to find hidden patterns and associations, devise rules and make predictions." (Nenad Stefanovic, "Big Data Analytics in Supply Chain Management", 2021)
"The study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as 'training data', to make predictions or decisions without being explicitly programmed to do so." (Jan Bosch et al, "Engineering AI Systems: A Research Agenda", Artificial Intelligence Paradigms for Smart Cyber-Physical Systems, 2021)
"This can be regarded as a subset of AI which refers to analyzing structured data and identifying trends (correlations) for specific outcomes and using that information to predict future values (causation)." (Vijayaraghavan Varadharajan & Akanksha Rajendra Singh, "Building Intelligent Cities: Concepts, Principles, and Technologies", 2021)
"A discipline that studies methods and algorithms of automated learning from data through which computer systems can adjust their operations according to feedback they receive. A term strongly related to artificial intelligence, data mining, statistical methods." (KDnuggets)
"A process where a computer uses an algorithm to gain understanding about a set of data, then makes predictions based on its understanding." (KDnuggets)
"A type of artificial intelligence that provides computers with the ability to learn without being specifically programmed to do so, focusing on the development of computer applications that can teach themselves to change when exposed to new data." (Solutions Review)
"is a type of artificial intelligence that enable systems to learn patterns from data and subsequently improve from experience. It is an interdisciplinary field that includes information theory, control theory, statistics, and computer science. As it gathers and sorts more information, machine learning constantly gets better at identifying types and forms of data with little or no hard coded rules." (Accenture)
"Machine learning is a branch of artificial intelligence that deals with self-improving algorithms. The algorithms 'learn' by recording the results of vast quantities of data processing actions. Over time, the algorithm improves its functionality without being explicitly programmed." (Xplenty) [source]
"Machine learning is a subset of artificial intelligence (AI) that deals with the extracting of patterns from data, and then uses those patterns to enable algorithms to improve themselves with experience. This type of learning can be used to help computers recognize patterns and associations in massive amounts of data, and make predictions and forecasts based on its findings." (RapidMiner) [source]
"Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values." (Techtarget) [source]
"Machine Learning is a type of artificial intelligence that enable systems to learn patterns from data and subsequently improve from experience. It is an interdisciplinary field that includes information theory, control theory, statistics, and computer science. As it gathers and sorts more information, machine learning constantly gets better at identifying types and forms of data with little or no hard coded rules." (Accenture)
"Machine learning is a cutting-edge programming technique used to automate the construction of analytical models and enable applications to perform specified tasks more efficiently without being explicitly programmed. Machine learning allows system to automatically learn and increase their accuracy in task performance through experience." (Sumo Logic) [source]
"Machine learning is the field of study that enables computers the ability to learn without being explicitly programmed." (Adobe)
"Machine learning is the subset of artificial intelligence (AI) that focuses on building systems that learn - or improve performance - based on the data they consume." (Oracle)
"Part of artificial intelligence where machines learn from what they are doing and become better over time." (Analytics Insight)
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