What is Machine Learning – A Curated List

  • Machine learning is an interdisciplinary approach in building mathematical models from known inputs to make data-driven predictions and decisions.
  • Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.
  • Machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results.
  • Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range.
  • At its most simple, machine learning is about teaching computers to learn in the same way we do, by interpreting data from the world around us, classifying it and learning from its successes and failures. In fact, machine learning is a subset, or better, the leading edge of artificial intelligence
  • Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives “computers the ability to learn without being explicitly programmed.”Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach optical character recognition (OCR), learning to rank, and computer vision.
  • Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter sub-field focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.
  • Machine Learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to “produce reliable, repeatable decisions and results” and uncover “hidden insights” through learning from historical relationships and trends in the data.
  • Machine learning is concerned with minimizing the loss on unseen samples.
  • Machine learning is a general term for any machine learning method that identifies, learns, or evolves rules to store, manipulate or apply, knowledge. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[35] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.
  • Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
  • Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
  • Machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought before.
  • Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. Using machine learning, computers learn without being explicitly programmed.
  • Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solution.
  • Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed
  • Machine Learning is an incredibly powerful tool. In the coming years, it promises to help solve some of our most pressing problems, as well as open up whole new worlds of opportunity. The demand for ML engineers is only going to continue to grow, offering incredible chances to be a part of something big. I hope you will consider getting in on the action!
  • Machine Learning is the field that studies how to make computers learn. In other words, a Machine Learning algorithm is a computer program that teaches computers how to program themselves so that we don’t have to explicitly describe how to perform the task we want to achieve. The information that a Machine Learning algorithm needs in order to write its own program to solve a particular task is a set of known examples. For example, for the task of teaching a computer to identify animals, we will show to the computer a bunch of labeled pictures (e.g. this picture is a tiger, this pictures is a cat, etc.), the same way we do it when we teach children. The Machine Learning algorithm will use these samples to identify which are the features that differentiate one animal from another, and with this information it will write its own program to perform the task of identifying animals.
  • Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
  • 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.
  • Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.

One thought on “What is Machine Learning – A Curated List”

Comments are closed.