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[SPARK-20506][DOCS] Add HTML links to highlight list in MLlib guide for 2.2

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Quick follow up to #17996 - forgot to add the HTML links to the relevant sections of the guide in the highlights list.

## How was this patch tested?

Built docs locally and tested links.

Author: Nick Pentreath <nickp@za.ibm.com>

Closes #18043 from MLnick/SPARK-20506-2.2-migration-guide-2.
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Nick Pentreath 2017-05-22 12:29:29 +02:00
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docs/ml-guide.md
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@ -71,21 +71,24 @@ To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4
The list below highlights some of the new features and enhancements added to MLlib in the `2.2`
release of Spark:
* `ALS` methods for _top-k_ recommendations for all users or items, matching the functionality
in `mllib` ([SPARK-19535](https://issues.apache.org/jira/browse/SPARK-19535)). Performance
was also improved for both `ml` and `mllib`
* [`ALS`](ml-collaborative-filtering.html) methods for _top-k_ recommendations for all
users or items, matching the functionality in `mllib`
([SPARK-19535](https://issues.apache.org/jira/browse/SPARK-19535)).
Performance was also improved for both `ml` and `mllib`
([SPARK-11968](https://issues.apache.org/jira/browse/SPARK-11968) and
[SPARK-20587](https://issues.apache.org/jira/browse/SPARK-20587))
* `Correlation` and `ChiSquareTest` stats functions for `DataFrames`
* [`Correlation`](ml-statistics.html#correlation) and
[`ChiSquareTest`](ml-statistics.html#hypothesis-testing) stats functions for `DataFrames`
([SPARK-19636](https://issues.apache.org/jira/browse/SPARK-19636) and
[SPARK-19635](https://issues.apache.org/jira/browse/SPARK-19635))
* `FPGrowth` algorithm for frequent pattern mining
* [`FPGrowth`](ml-frequent-pattern-mining.html#fp-growth) algorithm for frequent pattern mining
([SPARK-14503](https://issues.apache.org/jira/browse/SPARK-14503))
* `GLM` now supports the full `Tweedie` family
([SPARK-18929](https://issues.apache.org/jira/browse/SPARK-18929))
* `Imputer` feature transformer to impute missing values in a dataset
* [`Imputer`](ml-features.html#imputer) feature transformer to impute missing values in a dataset
([SPARK-13568](https://issues.apache.org/jira/browse/SPARK-13568))
* `LinearSVC` for linear Support Vector Machine classification
* [`LinearSVC`](ml-classification-regression.html#linear-support-vector-machine)
for linear Support Vector Machine classification
([SPARK-14709](https://issues.apache.org/jira/browse/SPARK-14709))
* Logistic regression now supports constraints on the coefficients during training
([SPARK-20047](https://issues.apache.org/jira/browse/SPARK-20047))