AWS/Machine Learning

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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)

External links