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Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.

What is Machine Learning?

Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc. Machine Learning is often used on large datasets to help draw predictive relationships between underlying features within the data.

Modern applications of Machine Learning are wide ranging including those in Bioinformatics, Astronomy, Computational Physics, Economics, Natural Language Processing, Image Recognition/Object Detection, Robotics, Recommendation Systems, etc.


Tag usage

When posting questions about Machine Learning, please make sure to take the following into consideration:

  • All questions should include both sufficient detail and clarity to be able to solve the problem at hand. This includes links to original data sources, code used for model construction, links to tutorials/other resources used, etc.

  • Questions should generally be more specific than "which model should I use" or "how can I achieve this" and explain what has been attempted/done so far.

  • Unless directly related to the problem, all questions regarding where to get data (sources, APIs, datasets, etc.) should not be posted on Stack Exchange Data Science, but rather on: Open Data Stack Exchange.


Types

Please see below for a (non-exhaustive) list of the types of Machine Learning:


External Resources


Machine Learning Journals