fix on doc

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md

@@ -8,14 +8,13 @@ build and run pre-trained Resnet 18 model.
 1. [Prerequisites](#prerequisites)
 2. [Download artifacts](#download-artifacts)
 3. [Setup datapath and parameters](#setup-datapath-and-parameters)
-4. [Run the image inference example](#run-the-image-inference-example)
+4. [Run the image classifier example](#run-the-image-inference-example)
 
 ## Prerequisites
 
-1. MXNet
-2. MXNet Scala Package
-3. [IntelliJ IDE (or alternative IDE) project setup](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/java/mxnet_java_on_intellij.md) with the MXNet Java Package
-4. wget
+1. Build from source with [MXNet](https://mxnet.incubator.apache.org/install/index.html)
+2. [IntelliJ IDE (or alternative IDE) project setup](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/java/mxnet_java_on_intellij.md) with the MXNet Java Package
+3. wget
 
 ## Download Artifacts
 
@@ -38,9 +37,9 @@ The available arguments are as follows:
 | `model-dir`                   | Folder path with prefix to the model (including json, params, and any synset file). |
 | `input-image`                 | The image to run inference on. |
 
-## Run the image inference example
+## Run the image classifier example
 
-After the previous steps, you should be able to run the code using the following script that will pass all of the required parameters to the Infer API.
+After the previous steps, you should be able to run the code using the following script that will pass all of the required parameters to the Predictor API.
 
 From the `scala-package/examples/scripts/infer/predictor/` folder run:
 
@@ -52,7 +51,7 @@ bash run_predictor_java_example.sh ../models/resnet-18/resnet-18 ../images/kitte
 * These are relative paths to this script.
 * You may need to run `chmod +x run_predictor_java_example.sh` before running this script.
 
-The example should give expected output as shown below:
+The example should give an output similar to the one shown below:
 ```
 Predict with Float input
 Probability : 0.30337515 Class : n02123159 tiger cat