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Machine Learning
Ryan J. Guda
College of Nursing and Health Care Professions, Grand Canyon University
HIM-650: Health Care Data Management
Valerie Doebler
June 9, 2021
This study source was downloaded by 100000790716548 from CourseHero.com on 08-19-2022 14:27:17 GMT -05:00
https://www.coursehero.com/file/132741598/Machine-Learning-R-GUDAdocx/
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Machine Learning
A subset of Artificial Intelligence (AI), Machine Learning (ML), gives the machine the
ability to learn automatically and progress without being overtly programmed.
Machine learning allows for increased data generation by structuring, analyzing, and
drawing valuable insights from data to solve intricate tasks of an organization. The algorithms in
ML help make better business decisions and solve complex problems. Using predictive models
and statistical techniques, ML can uncover hidden patterns and extract critical insights from data.
The Difference Between Unlabeled and Labeled Data
There are two types of data used in Machine Learning, unlabeled and labeled data. Both
are effective and valuable for developing AI.
Sydorenko (2020) quoted Techopedia in defining unlabeled data as "pieces of data that
have not been tagged with labels identifying characteristics, properties or classifications'. She
differentiated this to labeled data as raw data with meaningful labels, tags, or classes.
To further discuss the differences between the two: unlabeled data are easy to obtain and
store compared to labeled data which can be difficult and time-consuming to attain but offers a
much more comprehensive range of potentials. Unlabeled data can be obtained by observing and
collecting, while labeled data needs humans to annotate it. There are also differences in the usage
of these data. Unlabeled data is often used to preprocess data sets in unsupervised machine
learning, while labeled data is used to predict tasks in supervised machine learning (Sydorenko
(2020).
Machine Learning Models
There are three types of ML models: Supervised, Unsupervised, and Reinforcement
Learning.
This study source was downloaded by 100000790716548 from CourseHero.com on 08-19-2022 14:27:17 GMT -05:00
https://www.coursehero.com/file/132741598/Machine-Learning-R-GUDAdocx/
Machine Learning
Ryan J. Guda
College of Nursing and Health Care Professions, Grand Canyon University
HIM-650: Health Care Data Management
Valerie Doebler
June 9, 2021
This study source was downloaded by 100000790716548 from CourseHero.com on 08-19-2022 14:27:17 GMT -05:00
https://www.coursehero.com/file/132741598/Machine-Learning-R-GUDAdocx/
, 2
Machine Learning
A subset of Artificial Intelligence (AI), Machine Learning (ML), gives the machine the
ability to learn automatically and progress without being overtly programmed.
Machine learning allows for increased data generation by structuring, analyzing, and
drawing valuable insights from data to solve intricate tasks of an organization. The algorithms in
ML help make better business decisions and solve complex problems. Using predictive models
and statistical techniques, ML can uncover hidden patterns and extract critical insights from data.
The Difference Between Unlabeled and Labeled Data
There are two types of data used in Machine Learning, unlabeled and labeled data. Both
are effective and valuable for developing AI.
Sydorenko (2020) quoted Techopedia in defining unlabeled data as "pieces of data that
have not been tagged with labels identifying characteristics, properties or classifications'. She
differentiated this to labeled data as raw data with meaningful labels, tags, or classes.
To further discuss the differences between the two: unlabeled data are easy to obtain and
store compared to labeled data which can be difficult and time-consuming to attain but offers a
much more comprehensive range of potentials. Unlabeled data can be obtained by observing and
collecting, while labeled data needs humans to annotate it. There are also differences in the usage
of these data. Unlabeled data is often used to preprocess data sets in unsupervised machine
learning, while labeled data is used to predict tasks in supervised machine learning (Sydorenko
(2020).
Machine Learning Models
There are three types of ML models: Supervised, Unsupervised, and Reinforcement
Learning.
This study source was downloaded by 100000790716548 from CourseHero.com on 08-19-2022 14:27:17 GMT -05:00
https://www.coursehero.com/file/132741598/Machine-Learning-R-GUDAdocx/
