Update noteboook title. Add link to MLCC text classification guide.

samples/core/tutorials/keras/basic_text_classification.ipynb

@@ -103,7 +103,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "# Classify movie reviews: binary classification"
+        "# Text classification with movie reviews"
       ]
     },
     {
@@ -134,11 +134,11 @@
       "cell_type": "markdown",
       "source": [
         "\n",
-        "This notebook classifies movie reviews as *positive* or *negative* using the text of the review. This is an example of binary—or two-class—classification, an important and widely applicable kind of machine learning problem. \n",
+        "This notebook classifies movie reviews as *positive* or *negative* using the text of the review. This is an example of *binary*—or two-class—classification, an important and widely applicable kind of machine learning problem. \n",
         "\n",
         "We'll use the [IMDB dataset](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb) that contains the text of 50,000 movie reviews from the [Internet Movie Database](https://www.imdb.com/). These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are *balanced*, meaning they contain an equal number of positive and negative reviews. \n",
         "\n",
-        "This notebook uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow."
+        "This notebook uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow. For a more advanced text classification tutorial using `tf.keras`, see the [MLCC Text Classification Guide](https://developers.google.com/machine-learning/guides/text-classification/)."
       ]
     },
     {
@@ -813,4 +813,4 @@
       ]
     }
   ]
-}
+}
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