Typos in README

README.md

@@ -89,7 +89,7 @@ To set up on your local machine:
 
 **NOTE** - The [Alternating Least Squares (ALS)](examples/00_quick_start/als_movielens.ipynb) notebooks require a PySpark environment to run. Please follow the steps in the [setup guide](SETUP.md#dependencies-setup) to run these notebooks in a PySpark environment. For the deep learning algorithms, it is recommended to use a GPU machine and to follow the steps in the [setup guide](SETUP.md#dependencies-setup) to set up Nvidia libraries.
 
-**NOTE for DSVM Users** - Please follow the steps in the [Dependencies setup - Set PySpark environment variables on Linux or MacOS](SETUP.md#dependencies-setup) and [Troubleshooting for the DSVM](SETUP.md#troubleshooting-for-the-dsvm) sections if you encounter any issue.
+**NOTE for DSVM Users** - Please follow the steps in the [Dependencies setup - Set PySpark environment variables on Linux or macOS](SETUP.md#dependencies-setup) and [Troubleshooting for the DSVM](SETUP.md#troubleshooting-for-the-dsvm) sections if you encounter any issue.
 
 **DOCKER** - Another easy way to try the recommenders repository and get started quickly is to build [docker images](tools/docker/README.md) suitable for different environments. 
 
@@ -182,7 +182,7 @@ Smoke and integration tests are run daily on AzureML.
 
 ## Related projects
 
-- [Microsoft AI Github](https://github.com/microsoft/ai): Find other Best Practice projects, and Azure AI design patterns in our central repository.
+- [Microsoft AI GitHub](https://github.com/microsoft/ai): Find other Best Practice projects, and Azure AI design patterns in our central repository.
 - [NLP best practices](https://github.com/microsoft/nlp-recipes): Best practices and examples on NLP.
 - [Computer vision best practices](https://github.com/microsoft/computervision-recipes): Best practices and examples on computer vision.
 - [Forecasting best practices](https://github.com/microsoft/forecasting): Best practices and examples on time series forecasting.

tests/README.md

@@ -105,7 +105,7 @@ pytest tests/unit/test_notebooks_python.py::test_sar_single_node_runs
 
 A more advanced option is used in the smoke and integration tests, where we not only execute the notebook, but inject parameters and recover the computed metrics.
 
-The first step is to tag the parameters that we are going to inject. For it we need to modify the notebook. We will add a tag with the name `parameters`. To add a tag, go the the notebook menu, View, Cell Toolbar and Tags. A tag field will appear on every cell. The variables in the cell tagged with `parameters` can be injected. The typical variables that we inject are `MOVIELENS_DATA_SIZE`, `EPOCHS` and other configuration variables for our algorithms.
+The first step is to tag the parameters that we are going to inject. For it we need to modify the notebook. We will add a tag with the name `parameters`. To add a tag, go the notebook menu, View, Cell Toolbar and Tags. A tag field will appear on every cell. The variables in the cell tagged with `parameters` can be injected. The typical variables that we inject are `MOVIELENS_DATA_SIZE`, `EPOCHS` and other configuration variables for our algorithms.
 
 The way papermill works to inject parameters is very simple, it generates a copy of the notebook (in our code we call it `OUTPUT_NOTEBOOK`), and creates a new cell with the injected variables.