| [sqlRUtils](https://docs.microsoft.com/machine-learning-server/r-reference/sqlrutils/sqlrutils) | R | Helper functions for encapsulating R and T-SQL. |

description: Install, configure, and validate the Java language extension on SQL Server 2019 for both Linux and Windows systems.

description: Run Java sample code on SQL Server 2019 to learn steps for using the Java language extension with SQL Server data.

+ Packages must be Python 3.5-compliant and run on Windows.

**microsoftml** is a Python35-compatible module from Microsoft providing high-performance machine learning algorithms. It includes functions for training and transformations, scoring, text and image analysis, and feature extraction for deriving values from existing data.

Functions in **microsoftml** are callable in Python code encapsulated in stored procedures. Most developers build **microsoftml** solutions locally, and then migrate finished Python code to stored procedures as a deployment exercise.

The machine learning APIs were developed by Microsoft for internal machine learning applications and have been refined over the years to support high performance on big data, using multicore processing and fast data streaming. This package originated as a Python equivalent of an R version, [MicrosoftML](../r/ref-r-microsoftml.md), that has similar functions.

The **microsoftml** library is distributed in multiple Microsoft products, but usage is the same whether you get the library in SQL Server or another product. Because the functions are the same, [documentation for indvidual microsoftml functions](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/microsoftml-package) is published to just one location under the [Python reference](https://docs.microsoft.com/machine-learning-server/python-reference/introducing-python-package-reference) for Microsoft Machine Learning Server. Should any product-specific behaviors exist, discrepancies will be noted in the function help page.

The **microsoftml** module is based on Python 3.5 and available only when you install one of the following Microsoft products or downloads:

+ [SQL Server 2017 Machine Learning Services](../install/sql-machine-learning-services-windows-install.md)

+ [Microsoft Machine Learning Server 9.2.0 or later](https://docs.microsoft.com/machine-learning-server/)

+ [Python client libraries for a data science client](setup-python-client-tools-sql.md)

Algorithms in **microsoftml** depend on [revoscalepy](ref-py-revoscalepy.md) for:

+ Data source objects. Data consumed by **microsoftml** functions are created using **revoscalepy** functions.

+ Remote computing (shifting function execution to a remote SQL Server instance). The **revoscalepy** library provides functions for creating and activating a remote compute context for SQL server.

In most cases, you will load the packages together whenever you are using **microsoftml**.

This section lists the functions by category to give you an idea of how each one is used. You can also use the [table of contents](https://docs.microsoft.com/machine-learning-server/python-reference/introducing-python-package-reference) to find functions in alphabetical order.

| Function | Description |

|----------|-------------|

|[microsoftml.rx_ensemble](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/rx-ensemble.md) | Train an ensemble of models |

|[microsoftml.rx_fast_forest](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/rx-fast-forest.md) | Random Forest |

|[microsoftml.rx_fast_linear](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/rx-fast-linear.md) | Linear Model with Stochastic Dual Coordinate Ascent |

|[microsoftml.rx_fast_trees](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/rx-fast-trees.md) | Boosted Trees |

|[microsoftml.rx_logistic_regression](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/rx-logistic-regression.md) | Logistic Regression |

|[microsoftml.rx_neural_network](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/rx-neural-network.md) | Neural Network |

|[microsoftml.rx_oneclass_svm](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/rx-oneclass-svm.md) | Anomaly Detection |

<a name="ml-transforms"></a>

| Function | Description |

|----------|-------------|

|[microsoftml.categorical](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/categorical.md) | Converts a text column into categories |

|[microsoftml.categorical_hash](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/categorical-hash.md) | Hashes and converts a text column into categories |

| Function | Description |

|----------|-------------|

|[microsoftml.concat](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/concat.md) | Concatenates multiple columns into a single vector |

|[microsoftml.drop_columns](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/drop-columns.md) | Drops columns from a dataset |

|[microsoftml.select_columns](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/select-columns.md) | Retains columns of a dataset |

| Function | Description |

|----------|-------------|

|[microsoftml.count_select](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/count-select.md) |Feature selection based on counts |

|[microsoftml.mutualinformation_select](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/mutualinformation-select.md) | Feature selection based on mutual information |

| Function | Description |

|----------|-------------|

|[microsoftml.featurize_text](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/featurize-text.md) | Converts text columns into numerical features |

|[microsoftml.get_sentiment](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/get-sentiment.md) | Sentiment analysis |

| Function | Description |

|----------|-------------|

|[microsoftml.load_image](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/load-image.md) | Loads an image |

|[microsoftml.resize_image](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/resize-image.md) | Resizes an Image |

|[microsoftml.extract_pixels](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/extract-pixels.md) | Extracts pixels form an image |

|[microsoftml.featurize_image](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/featurize-image.md) | Converts an image into features |

| Function | Description |

|----------|-------------|

|[microsoftml.rx_featurize](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/rx-featurize.md) | Data transformation for data sources |

<a name="ml-scoring"></a>

| Function | Description |

|----------|-------------|

|[microsoftml.rx_predict](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/rx-predict.md) | Scores using a Microsoft machine learning model |

+ [Python tutorials](../tutorials/sql-server-python-tutorials.md)

+ [Tutorial: Embed Python code in T-SQL](../tutorials/run-python-using-t-sql.md)

+ [Python Reference (Microsoft Machine Learning Server)](https://docs.microsoft.com/machine-learning-server/python-reference/introducing-python-package-reference)