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From tillrohrmann <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-2072] [ml] [docs] Add a quickstart guid...
Date Thu, 11 Jun 2015 07:52:53 GMT
Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/792#discussion_r32197018
  
    --- Diff: docs/libs/ml/quickstart.md ---
    @@ -24,4 +25,214 @@ under the License.
     * This will be replaced by the TOC
     {:toc}
     
    -Coming soon.
    +## Introduction
    +
    +FlinkML is designed to make learning from your data a straight-forward process, abstracting
away
    +the complexities that usually come with having to deal with big data learning tasks.
In this
    +quick-start guide we will show just how easy it is to solve a simple supervised learning
problem
    +using FlinkML. But first some basics, feel free to skip the next few lines if you're
already
    +familiar with Machine Learning (ML).
    +
    +As defined by Murphy [1] ML deals with detecting patterns in data, and using those
    +learned patterns to make predictions about the future. We can categorize most ML algorithms
into
    +two major categories: Supervised and Unsupervised Learning.
    +
    +* **Supervised Learning** deals with learning a function (mapping) from a set of inputs
    +(features) to a set of outputs. The learning is done using a *training set* of (input,
    +output) pairs that we use to approximate the mapping function. Supervised learning problems
are
    +further divided into classification and regression problems. In classification problems
we try to
    +predict the *class* that an example belongs to, for example whether a user is going to
click on
    +an ad or not. Regression problems one the other hand, are about predicting (real) numerical
    +values, often called the dependent variable, for example what the temperature will be
tomorrow.
    +
    +* **Unsupervised Learning** deals with discovering patterns and regularities in the data.
An example
    +of this would be *clustering*, where we try to discover groupings of the data from the
    +descriptive features. Unsupervised learning can also be used for feature selection, for
example
    +through [principal components analysis](https://en.wikipedia.org/wiki/Principal_component_analysis).
    +
    +## Linking with FlinkML
    +
    +In order to use FlinkML in you project, first you have to
    +[set up a Flink program](http://ci.apache.org/projects/flink/flink-docs-master/apis/programming_guide.html#linking-with-flink).
    +Next, you have to add the FlinkML dependency to the `pom.xml` of your project:
    +
    +{% highlight xml %}
    +<dependency>
    +  <groupId>org.apache.flink</groupId>
    +  <artifactId>flink-ml</artifactId>
    +  <version>{{site.version }}</version>
    --- End diff --
    
    Nicely done with the site version :+1: 


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