Merge pull request #272493 from fbsolo-ms1/update-data-science-virtual-machine-files

Freshness update for dsvm-tools-data-science.md . . .

Author: Jill Grant

articles/machine-learning/data-science-virtual-machine/dsvm-tools-data-science.md

@@ -10,54 +10,54 @@ ms.custom: devx-track-python
 author: timoklimmer
 ms.author: tklimmer
 ms.topic: conceptual
-ms.date: 05/12/2021
+ms.date: 04/17/2024
 ---
 
 # Machine learning and data science tools on Azure Data Science Virtual Machines
-Azure Data Science Virtual Machines (DSVMs) have a rich set of tools and libraries for machine learning available in popular languages, such as Python, R, and Julia.
+Azure Data Science Virtual Machines (DSVMs) have a rich set of tools and libraries for machine learning. These resources are available in popular languages, such as Python, R, and Julia.
 
-Here are some of the machine-learning tools and libraries on DSVMs.
+The DSVM supports these machine-learning tools and libraries:
 
 ## Azure Machine Learning SDK for Python
 
-See the full reference for the [Azure Machine Learning SDK for Python](../overview-what-is-azure-machine-learning.md).
+For a full reference, visit [Azure Machine Learning SDK for Python](../overview-what-is-azure-machine-learning.md).
 
 | Category | Value |
 | ------------- | ------------- |
-| What is it?   |   Azure Machine Learning is a cloud service that you can use to develop and deploy machine-learning models. You can track your models as you build, train, scale, and manage them by using the Python SDK. Deploy models as containers and run them in the cloud, on-premises, or on Azure IoT Edge.   |
+| What is it?   |   You can use the Azure Machine Learning cloud service to develop and deploy machine-learning models. You can use the Python SDK to track your models as you build, train, scale, and manage them. Deploy models as containers, and run them in the cloud, on-premises, or on Azure IoT Edge.   |
 | Supported editions     | Windows (conda environment: AzureML), Linux (conda environment: py36)    |
 | Typical uses      | General machine-learning platform      |
 | How is it configured or installed?      |  Installed with GPU support   |
-| How to use or run it      | As a Python SDK and in the Azure CLI. Activate to the conda environment `AzureML` on Windows edition *or* to `py36` on Linux edition.      |
-| Link to samples      | Sample Jupyter notebooks are included in the `AzureML` directory under notebooks.  |
+| How to use or run it      | As a Python SDK and in the Azure CLI. Activate to the conda environment `AzureML` on the Windows edition *or* activate to `py36` on the Linux edition.      |
+| Link to samples      | Find sample Jupyter notebooks in the `AzureML` directory, under notebooks.  |
 
 ## H2O
 
 | Category | Value |
 | ------------- | ------------- |
-| What is it?   | An open-source AI platform that supports in-memory, distributed, fast, and scalable machine learning.  |
+| What is it?   | An open-source AI platform that supports distributed, fast, in-memory, scalable machine learning.  |
 | Supported versions      | Linux   |
 | Typical uses      | General-purpose distributed, scalable machine learning   |
 | How is it configured or installed?      | H2O is installed in `/dsvm/tools/h2o`.      |
-| How to use or run it      | Connect to the VM by using X2Go. Start a new terminal, and run `java -jar /dsvm/tools/h2o/current/h2o.jar`. Then start a web browser and connect to `http://localhost:54321`.      |
-| Link to samples      | Samples are available on the VM in Jupyter under the `h2o` directory.      |
+| How to use or run it      | Connect to the VM with X2Go. Start a new terminal, and run `java -jar /dsvm/tools/h2o/current/h2o.jar`. Then, start a web browser and connect to `http://localhost:54321`.      |
+| Link to samples      | Find samples on the VM in Jupyter, under the `h2o` directory.      |
 
-There are several other machine-learning libraries on DSVMs, such as the popular `scikit-learn` package that's part of the Anaconda Python distribution for DSVMs. To check out the list of packages available in Python, R, and Julia, run the respective package managers.
+There are several other machine-learning libraries on DSVMs - for example, the popular `scikit-learn` package that's part of the Anaconda Python distribution for DSVMs. For a list of packages available in Python, R, and Julia, run the respective package managers.
 
 ## LightGBM
 
 | Category | Value |
 | ------------- | ------------- |
-| What is it?   | A fast, distributed, high-performance gradient-boosting (GBDT, GBRT, GBM, or MART) framework based on decision tree algorithms. It's used for ranking, classification, and many other machine-learning tasks.    |
+| What is it?   | A fast, distributed, high-performance gradient-boosting (GBDT, GBRT, GBM, or MART) framework based on decision tree algorithms. Machine-learning tasks - ranking, classification, etc. - use it.    |
 | Supported versions      | Windows, Linux    |
 | Typical uses      | General-purpose gradient-boosting framework      |
-| How is it configured or installed?      | On Windows, LightGBM is installed as a Python package. On Linux, the command-line executable is in `/opt/LightGBM/lightgbm`, the R package is installed, and Python packages are installed.     |
+| How is it configured or installed?      | LightGBM is installed as a Python package on Windows. On Linux, the command-line executable is located in `/opt/LightGBM/lightgbm`. The R package is installed, and Python packages are installed.     |
 | Link to samples      | [LightGBM guide](https://github.com/Microsoft/LightGBM/tree/master/examples/python-guide)   |
 
 ## Rattle
 | Category | Value |
 | ------------- | ------------- |
-| What is it?   |   A graphical user interface for data mining by using R.   |
+| What is it?   |   A graphical user interface for data mining that uses R.   |
 | Supported editions     | Windows, Linux     |
 | Typical uses      | General UI data-mining tool for R    |
 | How to use or run it      | As a UI tool. On Windows, start a command prompt, run R, and then inside R, run `rattle()`. On Linux, connect with X2Go, start a terminal, run R, and then inside R, run `rattle()`. |
@@ -76,11 +76,11 @@ There are several other machine-learning libraries on DSVMs, such as the popular
 ## Weka
 | Category | Value |
 | ------------- | ------------- |
-| What is it?   |  A collection of machine-learning algorithms for data-mining tasks. The algorithms can be either applied directly to a data set or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. |
+| What is it?   |  A collection of machine-learning algorithms for data-mining tasks. You can either apply the algorithms directly, or call them from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. |
 | Supported editions     | Windows, Linux     |
 | Typical uses      | General machine-learning tool     |
 | How to use or run it      | On Windows, search for Weka on the **Start** menu. On Linux, sign in with X2Go, and then go to **Applications** > **Development** > **Weka**. |
-| Link to samples      | [Weka samples](https://www.cs.waikato.ac.nz/ml/weka/documentation.html) |
+| Link to samples      | [Weka samples](https://docs.weka.io/) |
 
 ## XGBoost 
 | Category | Value |
@@ -90,4 +90,4 @@ There are several other machine-learning libraries on DSVMs, such as the popular
 | Typical uses      | General machine-learning library      |
 | How is it configured or installed?      |  Installed with GPU support   |
 | How to use or run it      | As a Python library (2.7 and 3.6+), R package, and on-path command-line tool (`C:\dsvm\tools\xgboost\bin\xgboost.exe` for Windows and `/dsvm/tools/xgboost/xgboost` for Linux)    |
-| Links to samples      | Samples are included on the VM, in `/dsvm/tools/xgboost/demo` on Linux, and `C:\dsvm\tools\xgboost\demo` on Windows.   |
+| Links to samples      | Samples are included on the VM, in `/dsvm/tools/xgboost/demo` on Linux, and `C:\dsvm\tools\xgboost\demo` on Windows.   |