Use upper case for keyword predict and some text edits. (sql-machine-…

…learning#303)

* Test Commit

* Update username

* Update SQL Installation and Demo

* Some edits to fix typo and case.

* Move back installation.md to quickstart.md

* minor change in readme.md

README.md

@@ -16,9 +16,9 @@ There are already some work in progress in the industry. We can write simple mac
  - [Google BigQuery](https://cloud.google.com/bigquery/docs/bigqueryml-intro): Google BigQuery enables machine learning in SQL by introducing the `CREATE MODEL` statement.
 
 None of the existing solution solves our pain point, instead we want it to be fully extensible. 
-1. The solution should be compatible to many SQL engines, instead of a specific version or type.
-1. It should also support sophisticated machine learning models, including TensorFlow for deep learning and [xgboost](https://github.com/dmlc/xgboost) for trees.
-1. We also want to add the flexibilies to configure and run cutting-edge algorithms including specifying [feature crosses](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column), at least, no Python or R code embedded in the SQL statements, and integrate hyperparameter estimation.
+1. This solution should be compatible to many SQL engines, instead of a specific version or type.
+1. It should support sophisticated machine learning models, including TensorFlow for deep learning and [xgboost](https://github.com/dmlc/xgboost) for trees.
+1. We also want the flexibility to configure and run cutting-edge ML algorithms including specifying [feature crosses](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column), at least, no Python or R code embedded in the SQL statements, and fully integrated with hyperparameter estimation.
 
 ## How to use SQLFlow
 

doc/demo.md

@@ -15,7 +15,7 @@ sqlflow>
 
 ### Training a DNNClassifier and run prediction
 
-- Step one: Let's see training data from Iris database:
+- Step One: Let's see some training data from Iris database
 ```sql
 sqlflow> select * from iris.train limit 2;
 -----------------------------
@@ -27,7 +27,7 @@ sqlflow> select * from iris.train limit 2;
 +--------------+-------------+--------------+-------------+-------+
 ```
 
-- Step Two: Train a Tensorflow [DNNClassifier](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) from train table:
+- Step Two: Train a Tensorflow [DNNClassifier](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier)
 ```sql
 sqlflow> SELECT *
 FROM iris.train
@@ -42,16 +42,19 @@ Training set accuracy: 0.96721
 Done training
 ```
 
-- Step Three: Run prediction from a trained model:
+- Step Three: Run prediction using the trained model produced from step two. Note my_dnn_model is not a table, therefore it's not visually displayable from Select statement.
 ```sql
 sqlflow> SELECT *
 FROM iris.test
-predict iris.predict.class
+PREDICT iris.predict.class
 USING sqlflow_models.my_dnn_model;
 ```
 
-- Step Four: Checkout the prediction result:
+- Step Four: Checkout the prediction result
 ```sql
 sqlflow> select * from iris.predict limit 10;
 ```
-Now you have successfully run a demo in SQLFlow to train the model using DNNClassifier and make a simple prediction. More demos are on the road, please stay tuned.
+
+
+Congratulations! Now you have successfully completed a demo using SQLFlow syntax to train model using DNNClassifier and make a quick prediction. More demos are on the road, please stay tuned.
+