Github user thvasilo commented on a diff in the pull request:
https://github.com/apache/flink/pull/792#discussion_r32198054
--- 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>
+</dependency>
+{% endhighlight %}
+
+## Loading data
+
+To load data to be used with FlinkML we can use the ETL capabilities of
Flink, or specialized
+functions for formatted data, such as the LibSVM format. For supervised
learning problems it is
+common to use the `LabeledVector` class to represent the `(features,
label)` examples. A `LabeledVector`
+object will have a FlinkML `Vector` member representing the features of
the example and a `Double`
+member which represents the label, which could be the class in a
classification problem, or the dependent
+variable for a regression problem.
+
+As an example, we can use Haberman's Survival Data Set , which you can
+[download from the UCI ML
repository](http://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data.
+This dataset *"contains cases from study conducted on the survival of
patients who had undergone
+surgery for breast cancer"*. The data comes in a comma-separated file,
where the first 3 columns
+are the features and last column is the class, and the 4th column
indicates whether the patient
+survived 5 years or longer (label 1), or died within 5 years (label 2).
You can check the [UCI
+page](https://archive.ics.uci.edu/ml/datasets/Haberman%27s+Survival) for
more information on the data.
+
+We can load the data as a `DataSet[String]` first:
+
+{% highlight scala %}
+
+import org.apache.flink.api.scala.ExecutionEnvironment
+
+val env = ExecutionEnvironment.createLocalEnvironment(2)
+
+val survival = env.readCsvFile[(String, String, String,
String)]("/path/to/haberman.data")
+
+{% endhighlight %}
+
+We can now transform the data into a `DataSet[LabeledVector]`. This will
allow us to use the
+dataset with the FlinkML classification algorithms. We know that the 4th
element of the dataset
+is the class label, and the rest are features, so we can build
`LabeledVector` elements like this:
+
+{% highlight scala %}
+
+import org.apache.flink.ml.common.LabeledVector
+import org.apache.flink.ml.math.DenseVector
+
+val survivalLV = survival
+ .map{tuple =>
+ val list = tuple.productIterator.toList
+ val numList = list.map(_.asInstanceOf[String].toDouble)
+ LabeledVector(numList(3), DenseVector(numList.take(3).toArray))
+ }
+
+{% endhighlight %}
+
+We can then use this data to train a learner. We will however use another
dataset to exemplify
+building a learner; that will allow us to show how we can import other
dataset formats.
+
+**LibSVM files**
+
+A common format for ML datasets is the LibSVM format and a number of
datasets using that format can be
+found [in the LibSVM datasets
website](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/). FlinkML
provides utilities for loading
+datasets using the LibSVM format through the `readLibSVM` function
available through the MLUtils object.
+You can also save datasets in the LibSVM format using the `writeLibSVM`
function.
+Let's import the svmguide1 dataset. You can download the
+[training set
here](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/svmguide1)
+and the [test set
here](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/svmguide1.t).
+This is an astroparticle binary classification dataset, used by Hsu et al.
[3] in their practical
--- End diff --
That can be done with anchor links, which I tried for the other docs and
they didn't seem to work properly. I can try this again.
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