Difference between revisions of "AWS/Machine Learning"
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This article will be about '''Amazon Web Services - Machine Learning''' (ML). | This article will be about '''Amazon Web Services - Machine Learning''' (ML). | ||
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| + | ==Machine Learning concepts== | ||
| + | ;What is Machine Learning (ML)? | ||
| + | * The basic concept of ML is to have computers or machines program themselves. | ||
| + | * Machines can analyze large and complex datasets and identify patterns to create models, which are then used to predict outcomes. | ||
| + | * Over time, these models can take into account new datasets and improve the accuracy of the predictions. | ||
| + | |||
| + | ;Examples of where ML is being used | ||
| + | * Recommendations when checking out on an e-commerce site (e.g., purchases on Amazon.com) | ||
| + | * Spam detection in email | ||
| + | * Any kind of image, speech, or text recognition | ||
| + | * Weather forecasts | ||
| + | * Search engines | ||
| + | |||
| + | ;What is Amazon ML? | ||
| + | * Amazon ML is supervised ML; learns from examples or historical data. | ||
| + | * An Amazon ML Model requires your dataset to have both the '''features''' and the '''target''' for each observation/record. | ||
| + | * A '''feature''' is an attribute of a record used to identify patterns; typically, there will be multiple features. | ||
| + | * A '''target''' is the outcome that the patterns are linked to and is the value the ML algorithm is going to predict. | ||
| + | * This linking is used to predict the outcomes | ||
| + | * Example: {Go to the grocery store} {on Monday} (attribute {feature}) => Buy milk (target) | ||
==External links== | ==External links== | ||
Revision as of 21:09, 14 March 2017
This article will be about Amazon Web Services - Machine Learning (ML).
Machine Learning concepts
- What is Machine Learning (ML)?
- The basic concept of ML is to have computers or machines program themselves.
- Machines can analyze large and complex datasets and identify patterns to create models, which are then used to predict outcomes.
- Over time, these models can take into account new datasets and improve the accuracy of the predictions.
- Examples of where ML is being used
- Recommendations when checking out on an e-commerce site (e.g., purchases on Amazon.com)
- Spam detection in email
- Any kind of image, speech, or text recognition
- Weather forecasts
- Search engines
- What is Amazon ML?
- Amazon ML is supervised ML; learns from examples or historical data.
- An Amazon ML Model requires your dataset to have both the features and the target for each observation/record.
- A feature is an attribute of a record used to identify patterns; typically, there will be multiple features.
- A target is the outcome that the patterns are linked to and is the value the ML algorithm is going to predict.
- This linking is used to predict the outcomes
- Example: {Go to the grocery store} {on Monday} (attribute {feature}) => Buy milk (target)
