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Posted to issues@commons.apache.org by "elena (Jira)" <ji...@apache.org> on 2021/01/05 21:18:00 UTC
[jira] [Updated] (GEOMETRY-113) Applications of machine learning
[ https://issues.apache.org/jira/browse/GEOMETRY-113?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
elena updated GEOMETRY-113:
---------------------------
Description:
Machine learning plays an important role in our daily life. It is an idea that allows the machine to learn from examples. It uses the data to detect patterns in a dataset and adjust programs accordingly.[ML analysis |http://example.com/] algorithms are used primarily for many types of output.
We are surrounded by a lot of examples of machine learning and a lot of which is something that you cannot live without.
*Google maps*
The first one is google maps. Google maps are the app we use whenever we go out and require assistance in the direction and traffic. Google map is a combination of people currently using this service, the historical data of the route collected over time, and few tricks acquired from other companies; everyone who is using google maps providing their location, average speed, the route in which they are traveling which in turn has google collect massive data about the traffic which makes them predict the upcoming traffic and adjusts your route accordingly.
*Products recommendation*
Another application is the product recommendation. If you check an item on Amazon, but you do not buy it, then and there, but the next day you are watching videos on youtube, and suddenly you see an ad for the same item. You switch to Facebook, and again there, you see the same ad. This happens because Google tracks your search history and recommends ads based on search history.
*Self-driving cars*
Machine learning plays an important role in self-driving cars.
h2. *Machine learning steps*
There are various steps in machine learning. The first one is collecting data; this stage involves collecting all the relevant data from various sources. The second step after collecting data is data wrangling. Data wrangling is the process of cleaning and converting the raw data into a format that allows convenient consumption. After the data have been cleaned and converted into a format, the data is analyzed to select and filter the data required to prepare them all. After selecting features, the algorithm is trained on the training dataset through which the algorithm understands the patterns and the rules which govern the data; after this, the testing dataset determines the accuracy of the model, and if there is a dip in the model, the model is retrained. **
was:
Machine learning plays an important role in our daily life. It is an idea that allows the machine to learn from examples. It uses the data to detect patterns in a dataset and adjust programs accordingly.[ *ML analysis*|https://serokell.io/blog/machine-learning-text-analysis] algorithms are used primarily for many types of output.
We are surrounded by a lot of examples of machine learning and a lot of which is something that you cannot live without.
*Google maps*
The first one is google maps. Google maps are the app we use whenever we go out and require assistance in the direction and traffic. Google map is a combination of people currently using this service, the historical data of the route collected over time, and few tricks acquired from other companies; everyone who is using google maps providing their location, average speed, the route in which they are traveling which in turn has google collect massive data about the traffic which makes them predict the upcoming traffic and adjusts your route accordingly.
*Products recommendation*
Another application is the product recommendation. If you check an item on Amazon, but you do not buy it, then and there, but the next day you are watching videos on youtube, and suddenly you see an ad for the same item. You switch to Facebook, and again there, you see the same ad. This happens because Google tracks your search history and recommends ads based on search history.
*Self-driving cars*
Machine learning plays an important role in self-driving cars.
h2. *Machine learning steps*
There are various steps in machine learning. The first one is collecting data; this stage involves collecting all the relevant data from various sources. The second step after collecting data is data wrangling. Data wrangling is the process of cleaning and converting the raw data into a format that allows convenient consumption. After the data have been cleaned and converted into a format, the data is analyzed to select and filter the data required to prepare them all. After selecting features, the algorithm is trained on the training dataset through which the algorithm understands the patterns and the rules which govern the data; after this, the testing dataset determines the accuracy of the model, and if there is a dip in the model, the model is retrained. **
> Applications of machine learning
> --------------------------------
>
> Key: GEOMETRY-113
> URL: https://issues.apache.org/jira/browse/GEOMETRY-113
> Project: Apache Commons Geometry
> Issue Type: Task
> Reporter: elena
> Priority: Major
>
> Machine learning plays an important role in our daily life. It is an idea that allows the machine to learn from examples. It uses the data to detect patterns in a dataset and adjust programs accordingly.[ML analysis |http://example.com/] algorithms are used primarily for many types of output.
> We are surrounded by a lot of examples of machine learning and a lot of which is something that you cannot live without.
> *Google maps*
> The first one is google maps. Google maps are the app we use whenever we go out and require assistance in the direction and traffic. Google map is a combination of people currently using this service, the historical data of the route collected over time, and few tricks acquired from other companies; everyone who is using google maps providing their location, average speed, the route in which they are traveling which in turn has google collect massive data about the traffic which makes them predict the upcoming traffic and adjusts your route accordingly.
> *Products recommendation*
> Another application is the product recommendation. If you check an item on Amazon, but you do not buy it, then and there, but the next day you are watching videos on youtube, and suddenly you see an ad for the same item. You switch to Facebook, and again there, you see the same ad. This happens because Google tracks your search history and recommends ads based on search history.
> *Self-driving cars*
> Machine learning plays an important role in self-driving cars.
> h2. *Machine learning steps*
> There are various steps in machine learning. The first one is collecting data; this stage involves collecting all the relevant data from various sources. The second step after collecting data is data wrangling. Data wrangling is the process of cleaning and converting the raw data into a format that allows convenient consumption. After the data have been cleaned and converted into a format, the data is analyzed to select and filter the data required to prepare them all. After selecting features, the algorithm is trained on the training dataset through which the algorithm understands the patterns and the rules which govern the data; after this, the testing dataset determines the accuracy of the model, and if there is a dip in the model, the model is retrained. **
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