Added the "Getting Started with TensorFlow for ML Beginners" chapter to Get

Started home page.

PiperOrigin-RevId: 181548668

2 files changed, 21 insertions, 16 deletions

diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md
index d0cb69d211..b7bd1286e3 100644
--- a/tensorflow/docs_src/get_started/index.md
+++ b/tensorflow/docs_src/get_started/index.md
@@ -1,35 +1,35 @@
 # Getting Started
 
 TensorFlow is a tool for machine learning. While it contains a wide range of
-functionality, it is mainly designed for deep neural network models.
+functionality, TensorFlow is mainly designed for deep neural network models.
 
-The fastest way to build a fully-featured model trained on your data is to use
-TensorFlow's high-level API. In the following examples, we will use the
-high-level API on the classic [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set).
-We will train a model that predicts what species a flower is based on its
-characteristics, and along the way get a quick introduction to the basic tasks
-in TensorFlow using Estimators.
+TensorFlow provides many APIs. This section focuses on the high-level APIs.
+If you are new to TensorFlow, begin by reading one of the following documents:
 
-This tutorial is divided into the following parts:
+  * @{$get_started/get_started_for_beginners}, which is aimed at readers
+    new to machine learning.
+  * @{$get_started/premade_estimators}, which is aimed at readers who have
+    experience in machine learning.
 
-  * @{$get_started/premade_estimators}, which shows you
-    how to quickly setup prebuilt models to train on in-memory data.
-  * @{$get_started/checkpoints}, which shows you how to save training progress,
+Then, read the following documents, which demonstrate the key features
+in the high-level APIs:
+
+  * @{$get_started/checkpoints}, which explains how to save training progress
     and resume where you left off.
   * @{$get_started/feature_columns}, which shows how an
     Estimator can handle a variety of input data types without changes to the
     model.
-  * @{$get_started/datasets_quickstart}, which is a minimal introduction to
-    the TensorFlow's input pipelines.
+  * @{$get_started/datasets_quickstart}, which introduces TensorFlow's
+    input pipelines.
   * @{$get_started/custom_estimators}, which demonstrates how
     to build and train models you design yourself.
 
 For more advanced users:
 
   * The @{$low_level_intro$Low Level Introduction} demonstrates how to use
-    tensorflow outside of the Estimator framework, for debugging and
+    TensorFlow outside of the Estimator framework, for debugging and
     experimentation.
-  * The remainder of the @{$programmers_guide$Programmer's Guide} contains
-    in-depth guides to various major components of TensorFlow.
+  * The @{$programmers_guide$Programmer's Guide} details major
+    TensorFlow components.
   * The @{$tutorials$Tutorials} provide walkthroughs of a variety of
     TensorFlow models.
diff --git a/tensorflow/docs_src/get_started/leftnav_files b/tensorflow/docs_src/get_started/leftnav_files
index 668daae9cb..437791d6a3 100644
--- a/tensorflow/docs_src/get_started/leftnav_files
+++ b/tensorflow/docs_src/get_started/leftnav_files
@@ -1,5 +1,10 @@
 index.md
+
+### Getting Started
+get_started_for_beginners.md
 premade_estimators.md
+
+### Details
 checkpoints.md
 feature_columns.md
 datasets_quickstart.md