AWS/Machine Learning
From Christoph's Personal Wiki
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)
