updatereadme.md files w/ grammer corrections

AI-and-Analytics/End-to-end-Workloads/Census/README.md

@@ -1,31 +1,31 @@
 # End-to-end machine learning workload: Census
-This sample code illustrates how to use Modin for ETL operations and ridge regression algorithm from the DAAL accelerated scikit-learn library to build and run an end to end machine learning workload. It demonstrates how to use software products that can be found in the [Intel AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
+This sample code illustrates how to use Modin for ETL operations, and ridge regression algorithm from the DAAL accelerated scikit-learn library to build and run an end to end machine learning workload. It demonstrates how to use software products that can be found in the [Intel AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
 
 | Optimized for                     | Description
 | :---                              | :---
 | OS                                | 64-bit Linux: Ubuntu 18.04 or higher
 | Hardware                          | Intel Atom® Processors; Intel® Core™ Processor Family; Intel® Xeon® Processor Family; Intel® Xeon® Scalable Performance Processor Family
 | Software                          | Python version 3.7, Modin, Ray, daal4py, Scikit-Learn, NumPy, Intel® AI Analytics Toolkit
-| What you will learn               | How to use Modin and DAAL optimized scikit-learn (developed and owned by Intel) to build end to end ML workloads and gain performance.
+| What you will learn               | How to use Modin and DAAL optimized scikit-learn (developed and owned by Intel) to build an end to end ML workloads and gain performance.
 | Time to complete                  | 15-18 minutes
 
 ## Purpose
-Modin uses Ray to provide an effortless way to speed up your Pandas notebooks, scripts and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing Pandas code. Daal4py is a simplified API to Intel DAAL that allows for fast usage of the framework suited for Data Scientists and Machine Learning users. It is built to help provide an abstraction to Intel® DAAL for either direct usage or integration into one's own framework.
+Modin uses Ray to provide an effortless way to speed up your Pandas notebooks, scripts and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing Pandas code. Daal4py is a simplified API to Intel DAAL that allows for fast usage of the framework suited for Data Scientists and Machine Learning users. It is built to help provide an abstraction to Intel® DAAL for direct usage or integration into one's own framework.
 
 #### Model and dataset
-In this sample, you will use Modin to ingest and process U.S. census data from 1970 to 2010 in order to build a ridge regression based model to find the relation between education and the total income earned in the US.
-Data transformation stage normalizes the income to the yearly inflation, balances the data such that each year has a similar number of data points, and extracts the features from the transformed dataset. The feature vectors are fed into the ridge regression model to predict the income of each sample.
+In this sample, you will use Modin to ingest and process U.S. census data from 1970 to 2010 to build a ridge regression based model to find the relation between education and the total income earned in the US.
+The data transformation stage normalizes the income to the yearly inflation, balances the data such that each year has a similar number of data points, and extracts the features from the transformed dataset. The feature vectors are fed into the ridge regression model to predict the income of each sample.
 
-Dataset is from IPUMS USA, University of Minnesota , [www.ipums.org](https://ipums.org/) (Steven Ruggles, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas and Matthew Sobek. IPUMS USA: Version 10.0 [dataset]. Minneapolis, MN: IPUMS, 2020. https://doi.org/10.18128/D010.V10.0)
+Dataset is from IPUMS USA, University of Minnesota, [www.ipums.org](https://ipums.org/) (Steven Ruggles, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas and Matthew Sobek. IPUMS USA: Version 10.0 [dataset]. Minneapolis, MN: IPUMS, 2020. https://doi.org/10.18128/D010.V10.0)
 
 ## Key Implementation Details
 This end-to-end workload sample code is implemented for CPU using the Python language. The example requires you to have Modin, Ray, daal4py, Scikit-Learn, NumPy installed inside a conda environment, similar to what is directed by the [oneAPI AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/articles/installing-ai-kit-with-conda.html) as well as the steps that follow in this README. 
 
 ## License
 
-This code sample is licensed under MIT license
+This code sample is licensed under the MIT license
 
-## Building Modin and daal4py for CPU to build and run end-to-end workload
+## Building Modin and daal4py for CPU to build and run an end-to-end workload
 
 Modin and oneAPI Data Analytics Library (DAAL) is ready for use once you finish the Intel AI Analytics Toolkit installation with the Conda Package Manager.
 
@@ -34,7 +34,7 @@ You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi)
 
 ### Activate conda environment With Root Access
 
-Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script and Intel Distribution of Modin environment installation (https://software.intel.com/content/www/us/en/develop/articles/installing-ai-kit-with-conda.html). Then navigate in Linux shell to your oneapi installation path, typically `/opt/intel/oneapi/` when installed as root or sudo, and `~/intel/oneapi/` when not installed as a super user. If you customized the installation folder, the `setvars.sh` file is in your custom folder.
+Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script and Intel Distribution of Modin environment installation (https://software.intel.com/content/www/us/en/develop/articles/installing-ai-kit-with-conda.html). Then navigate in Linux shell to your oneapi installation path, typically `/opt/intel/oneapi/` when installed as root or sudo, and `~/intel/oneapi/` when not installed as a superuser. If you customized the installation folder, the `setvars.sh` file is in your custom folder.
 
 Activate the conda environment with the following command:
 
@@ -73,7 +73,7 @@ pip install jupyter
 
 ### Install wget package
 
-Install wget package in order to retrieve the Census dataset using HTTPS
+Install wget package to retrieve the Census dataset using HTTPS
 
 ```
 pip install wget
@@ -98,7 +98,7 @@ Open .ipynb file and run cells in Jupyter Notebook using the "Run" button. Alter
 
 ### Run as Python File
 
-Open notebook in Jupyter and download as python file (see image using "census modin" sample)
+Open notebook in Jupyter and download as python file (see the image using "census modin" sample)
 
 ![Download as python file in the Jupyter Notebook](Running_Jupyter_notebook_as_Python.jpg "Download as python file in the Jupyter Notebook")
 

AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedKMeans/README.md

@@ -1,5 +1,5 @@
 # Intel Python daal4py Distributed K-Means 
-This sample code shows how to train and predict with a distributed k-means model using the python API package daal4py for oneAPI Data Analytics Library. It assumes you have a working version of MPI library installed and it demonstrates how to use software products that can be found in the [Intel oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or [Intel AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
+This sample code shows how to train and predict with a distributed k-means model using the python API package daal4py for oneAPI Data Analytics Library. It assumes you have a working version of the MPI library installed, and it demonstrates how to use software products that can be found in the [Intel oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or [Intel AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
 
 | Optimized for                     | Description
 | :---                              | :---
@@ -11,9 +11,9 @@ This sample code shows how to train and predict with a distributed k-means model
 
 ## Purpose
 
-daal4py is a simplified API to Intel® DAAL that allows for fast usage of the framework suited for Data Scientists or Machine Learning users. Built to help provide an abstraction to Intel® DAAL for either direct usage or integration into one's own framework.
+daal4py is a simplified API to Intel® DAAL that allows for fast usage of the framework suited for Data Scientists or Machine Learning users. Built to help provide an abstraction to Intel® DAAL for direct usage or integration into one's own framework.
 
-In this sample you will run a distributed K-Means model with oneDAL daal4py library memory objects. You will also learn how to train a model and save the information to a file.
+In this sample, you will run a distributed K-Means model with oneDAL daal4py library memory objects. You will also learn how to train a model and save the information to a file.
 
 ## Key Implementation Details 
 This distributed K-means sample code is implemented for CPU using the Python language. The example assumes you have daal4py and scikit-learn installed inside a conda environment, similar to what is delivered with the installation of the Intel(R) Distribution for Python as part of the [oneAPI AI Analytics Toolkit powered by oneAPI](https://software.intel.com/en-us/oneapi/ai-kit). 
@@ -22,17 +22,17 @@ This distributed K-means sample code is implemented for CPU using the Python lan
 You will need a working MPI library. We recommend to use Intel(R) MPI, which is included in the [oneAPI HPC Toolkit](https://software.intel.com/en-us/oneapi/hpc-kit).
 
 ## License  
-This code sample is licensed under MIT license
+This code sample is licensed under the MIT license
 
 ## Building daal4py for CPU
 
-oneAPI Data Analytics Library is ready for use once you finish the Intel AI Analytics Toolkit installation, and have run the post installation script.
+oneAPI Data Analytics Library is ready for use once you finish the Intel AI Analytics Toolkit installation and have run the post installation script.
 
-You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi) for toolkit installation, and the Toolkit [Getting Started Guide for Linux](https://software.intel.com/en-us/get-started-with-intel-oneapi-linux-get-started-with-the-intel-ai-analytics-toolkit) for post-installation steps and scripts.
+You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi) for toolkit installation and the Toolkit [Getting Started Guide for Linux](https://software.intel.com/en-us/get-started-with-intel-oneapi-linux-get-started-with-the-intel-ai-analytics-toolkit) for post-installation steps and scripts.
 
 ### Activate conda environment With Root Access
 
-Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script. Then navigate in Linux shell to your oneapi installation path, typically `/opt/intel/oneapi/` when installed as root or sudo, and `~/intel/oneapi/` when not installed as a super user. If you customized the installation folder, the `setvars.sh` file is in your custom folder. 
+Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script. Then navigate in Linux shell to your oneapi installation path, typically `/opt/intel/oneapi/` when installed as root or sudo, and `~/intel/oneapi/` when not installed as a superuser. If you customized the installation folder, the `setvars.sh` file is in your custom folder. 
 
 Intel Python environment will be active by default. However, if you activated another environment, you can return with the following command:
 
@@ -80,9 +80,9 @@ Run the Program
 
 `mpirun -n 4 python ./IntelPython_daal4py_Distributed_Kmeans.py`
 
-The output of the script will be saved in the included models and results directories. 
+The output of the script will be saved in the included models and result directories. 
 
-_Note: This code samples focuses on how to use daal4py to do distributed ML computations on chunks of data. The `mpirun` command above will only run on single local node. In order to launch on a cluster, you will need to create a host file on the master node among other steps. The **TensorFlow_Multinode_Training_with_Horovod** code sample explains this process well._
+_Note: This code samples focus on using daal4py to do distributed ML computations on chunks of data. The `mpirun` command above will only run on a single local node. To launch on a cluster, you will need to create a host file on the master node, among other steps. The **TensorFlow_Multinode_Training_with_Horovod** code sample explains this process well._
 
 ##### Expected Printed Output (with similar numbers, printed 4 times):
 ```

AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedLinearRegression/README.md

@@ -1,5 +1,5 @@
 # Intel Python daal4py Distributed Linear Regression
-This sample code shows how to train and predict with a distributed linear regression model using the python API package daal4py for oneAPI Data Analytics Library. It assumes you have a working version of MPI library installed and it demonstrates how to use software products that can be found in the [Intel oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or [Intel AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
+This sample code shows how to train and predict with a distributed linear regression model using the python API package daal4py for oneAPI Data Analytics Library. It assumes you have a working version of the MPI library installed, and it demonstrates how to use software products that can be found in the [Intel oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or [Intel AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
 
 | Optimized for                     | Description
 | :---                              | :---
@@ -11,30 +11,30 @@ This sample code shows how to train and predict with a distributed linear regres
 
 ## Purpose
 
-daal4py is a simplified API to Intel® DAAL that allows for fast usage of the framework suited for Data Scientists or Machine Learning users. Built to help provide an abstraction to Intel® DAAL for either direct usage or integration into one's own framework.
+daal4py is a simplified API to Intel® DAAL that allows for fast usage of the framework suited for Data Scientists or Machine Learning users. Built to help provide an abstraction to Intel® DAAL for direct usage or integration into one's own framework.
 
-In this sample you will run a distributed Linear Regression model with oneDAL daal4py library memory objects. You will also learn how to train a model and save the information to a file.
+In this sample, you will run a distributed Linear Regression model with oneDAL daal4py library memory objects. You will also learn how to train a model and save the information to a file.
 
 ## Key Implementation Details 
-This distributed linear regression sample code is implemented for CPU using the Python language. The example assumes you have daal4py and scikit-learn installed inside a conda environment, similar to what is delivered with the installation of the Intel(R) Distribution for Python as part of the [oneAPI AI Analytics Toolkit](https://software.intel.com/en-us/oneapi/ai-kit). 
+This distributed linear regression sample code is implemented for the CPU using the Python language. The example assumes you have daal4py and scikit-learn installed inside a conda environment, similar to what is delivered with the installation of the Intel(R) Distribution for Python as part of the [oneAPI AI Analytics Toolkit](https://software.intel.com/en-us/oneapi/ai-kit). 
 
 
 ## Additional Requirements
 You will need a working MPI library. We recommend to use Intel(R) MPI, which is included in the [oneAPI HPC Toolkit](https://software.intel.com/en-us/oneapi/hpc-kit).
 
 ## License  
-This code sample is licensed under MIT license
+This code sample is licensed under the MIT license
 
 ## Building daal4py for CPU
 
-oneAPI Data Analytics Library is ready for use once you finish the Intel AI Analytics Toolkit installation, and have run the post installation script.
+oneAPI Data Analytics Library is ready for use once you finish the Intel AI Analytics Toolkit installation and have run the post installation script.
 
-You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi) for toolkit installation, and the Toolkit [Getting Started Guide for Linux](https://software.intel.com/en-us/get-started-with-intel-oneapi-linux-get-started-with-the-intel-ai-analytics-toolkit) for post-installation steps and scripts.
+You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi) for toolkit installation and the Toolkit [Getting Started Guide for Linux](https://software.intel.com/en-us/get-started-with-intel-oneapi-linux-get-started-with-the-intel-ai-analytics-toolkit) for post-installation steps and scripts.
 
 
 ### Activate conda environment With Root Access
 
-Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script. Then navigate in Linux shell to your oneapi installation path, typically `/opt/intel/oneapi/` when installed as root or sudo, and `~/intel/oneapi/` when not installed as a super user. If you customized the installation folder, the `setvars.sh` file is in your custom folder. 
+Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script. Then navigate in Linux shell to your oneapi installation path, typically `/opt/intel/oneapi/` when installed as root or sudo, and `~/intel/oneapi/` when not installed as a superuser. If you customized the installation folder, the `setvars.sh` file is in your custom folder. 
 
 Intel Python environment will be active by default. However, if you activated another environment, you can return with the following command:
 
@@ -83,9 +83,9 @@ Run the Program
 
 `mpirun -n 4 python ./IntelPython_daal4py_Distributed_LinearRegression.py`
 
-The output of the script will be saved in the included models and results directories. 
+The output of the script will be saved in the included models and result directories. 
 
-_Note: This code samples focuses on how to use daal4py to do distributed ML computations on chunks of data. The `mpirun` command above will only run on single local node. In order to launch on a cluster, you will need to create a host file on the master node among other steps. The **TensorFlow_Multinode_Training_with_Horovod** code sample explains this process well._
+_Note: This code samples focus on using daal4py to do distributed ML computations on chunks of data. The `mpirun` command above will only run on a single local node. To launch on a cluster, you will need to create a host file on the master node, among other steps. The **TensorFlow_Multinode_Training_with_Horovod** code sample explains this process well._
 
 ##### Expected Printed Output (with similar numbers, printed 4 times):
 ```