Update README.md

README.md

@@ -7,23 +7,24 @@
 [![Pull Requests](https://img.shields.io/github/issues-pr-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen)
 [![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases)
 
-NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning experiments. 
-The tool dispatches and runs trial jobs that generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments (e.g. local machine, remote servers and cloud).
+NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments. 
+The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.
 
 <p align="center">
 <img src="./docs/img/nni_arch_overview.png" alt="drawing" width="800"/>
 </p>
 
 ## **Who should consider using NNI**
-* You want to try different AutoML algorithms for your training code (model) at local
-* You want to run AutoML trial jobs in different environments to speed up search (e.g. remote servers and cloud)
-* As a researcher and data scientist, you want to implement your own AutoML algorithms and compare with other algorithms
-* As a ML platform owner, you want to support AutoML in your platform
+* Those who want to try different AutoML algorithms in their training code (model) at their local machine.
+* Those who want to run AutoML trial jobs in different environments to speed up search (e.g. remote servers and cloud).
+* Researchers and data scientists who want to implement their own AutoML algorithms and compare it with other algorithms.
+* ML Platform owners who want to support AutoML in their platform.
 
 ## **Install & Verify**
 
 **Install through source code**
-* We only support Linux in current stage, Ubuntu 16.04 or higher are tested and supported. Simply run the following `pip install` in an environment that has `python >= 3.5`, `git` and `wget`.
+* We only support Linux (Ubuntu 16.04 or higher) in our current stage. 
+* Run the following `pip install` in an environment that has `python >= 3.5`, `git` and `wget`.
 ```bash
     git clone -b v0.3 https://github.com/Microsoft/nni.git
     cd nni
@@ -31,18 +32,18 @@ The tool dispatches and runs trial jobs that generated by tuning algorithms to s
 ```
 
 **Verify install**
-* The following example is an experiment built on TensorFlow, make sure you have `TensorFlow installed` before running it.
-* And download the examples via clone the source code
+* The following example is an experiment built on TensorFlow. Make sure you have `TensorFlow installed` before running it.
+* Download the examples via clone the source code.
 ```bash
     cd ~
     git clone -b v0.3 https://github.com/Microsoft/nni.git
 ```
-* Then, run the mnist example
+* Run the mnist example.
 ```bash
     nnictl create --config ~/nni/examples/trials/mnist/config.yml
 ```
 
-* In the command terminal, waiting for the message `Info: Start experiment success!` which indicates your experiment had been successfully started. You are able to explore the experiment using the `Web UI url`.
+* Wait for the message `Info: Start experiment success!` in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the `Web UI url`.
 ```diff
     Info: Checking experiment...
     ...