Initial commit of Amazon Machine Learning Samples

.gitignore

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+*.pyc
+*.swp
+.DS_Store
+*.class

LICENSE

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+Amazon Machine Learning Samples
+Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+
+Licensed under the Amazon Software License (the "License"). You may not use
+this file except in compliance with the License. A copy of the License is
+located at
+
+    http://aws.amazon.com/asl/
+
+or in the "license" file accompanying this file. This file is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express or
+implied. See the License for the specific language governing permissions and
+limitations under the License.

README.md

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+# Machine Learning Samples
+
+Each subdirectory contains sample code for using Amazon Machine Learning.
+Refer to the `README.md` file in each sub-directory for details on using
+each sample.
+
+## Targeted Marketing Samples
+
+These samples show how to use the Amazon Machine Learning API for a 
+targeted marketing application.  This follows the "banking" dataset example
+described in the Developer Guide.  There are two versions available:
+
+* [Targeted Marketing with Machine Learning in Java](targeted-marketing-java/)
+* [Targeted Marketing with Machine Learning in Python](targeted-marketing-python/)
+
+
+## Social Media and Amazon Mechanical Turk
+
+This sample application shows how to use Amazon Mechanical Turk to create a
+labeled dataset from raw tweets, and then build a machine learning model
+using the Amazon Machine Learning API that predicts whether or not new
+tweets should be acted upon by customer service.  The sample shows how to
+set up an automated filter using AWS Lambda that monitors tweets on an
+Amazon Kinesis stream and sends notifications whenever the ML Model
+predicts that a new tweet is actionable.  Notifications go to Amazon SNS,
+allowing delivery to email, SMS text messages, or other software services.
+
+* [Machine-Learning based Social Media Filtering (Python & JavaScript)](social-media/)
+
+
+## Mobile Prediction Samples
+
+These samples show how to use the Amazon Machine Learning API to make 
+real-time predictions from a mobile device.  There are two versions available:
+
+* [Real-time Machine Learning Predictions from iOS](mobile-ios/)
+* [Real-time Machine Learning Predictions from Android](mobile-android/)
+
+
+## Other tools
+
+A collection of simple scripts to help with common tasks.
+
+* [Machine Learning Tools (python)](ml-tools-python/)
+

ml-tools-python/README.md

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+# ML Tools
+
+These are utilities that we've found helpful when working with
+Amazon Machine Learning.
+
+## Schema Guesser
+
+This script examines the first 1,000 lines of a local CSV file, and
+uses them to generate a JSON schema.  
+
+Amazon Machine Learning's API requires you to specify exactly what type of
+data is in each column of your CSV in a schema file.  The web console makes
+guesses for you to simplify this process.  This utility makes similar
+guesses.
+
+For usage information run:
+
+    python guess_schema.py
+
+
+## Wait For Entity
+
+This script polls the status of an entity (data source, ML model, evaluation, 
+or batch prediction) waiting for it to reach a terminal state.
+
+Many operations in Amazon Machine Learning are asynchronous, including all
+of the Create... operations.  Most of these can be chained together so that
+they will wait until their dependencies are complete before starting.
+However some operations (like setting the score threshold on a model)
+require the entity to be in a COMPLETED state before they will run.  This
+utility provides a simple way to watch the progress of your entities.
+
+For usage information run:
+
+    python wait_for_entity.py
+
+
+## Realtime Prediction Tool
+
+This script enables realtime predictions through a simple command line 
+interface.  It will automatically create a realtime endpoint if one is 
+needed, and lets you delete the endpoint when you are done.
+
+NOTE: Your account will be charged an hourly realtime reservation 
+fee for every ML model that has a realtime endpoint.  So remember to 
+delete the endpoints when you are done using them.
+
+For usage information run:
+
+    python realtime.py
+
+
+## AWSPyML library
+
+This is a set of classes and functions that might be useful in developing
+predictive applications with Amazon Machine Learning.  There are utilities
+for testing the configuration of a connection to Amazon Machine Learning,
+for generating friendly identifiers, and classes for working with Schema
+files.  The `guess_schema.py` script relies on this library.
+