Update Readme.md

Author: ksalama

README.md

@@ -14,20 +14,20 @@
   * Image Analysis (CNN for Image Classification)
   *  How to use **canned estimators**  to train ML models.
 
-*  How to implement **custom estimators** (model_fn & EstimatorSpec).
+* How to implement **custom estimators** (model_fn & EstimatorSpec).
 
-*  A standard **metadata-driven** approach to build the model **feature_column**(s) (including numeric features as well as categorical features with 1) vocabulary, 2) hash bucket, and 3) identity.
+* A standard **metadata-driven** approach to build the model **feature_column**(s) (including numeric features as well as categorical features with 1) vocabulary, 2) hash bucket, and 3) identity.
 
-*  Data **input pipelines** (input_fn) using: 
- * tf.estimator.inputs.**pandas_input_fn**, 
- * tf.train.**string_input_producer**, and 
- * tf.data.**Dataset** APIs to read both **.csv** and **.tfrecords** (tf.example) data files
- * tf.contrib.timeseries.**RandomWindowInputFn** and **WholeDatasetInputFn** for time-series data
- * Feature **preprocessing** and **creation** as part of reading data (input_fn), for example, sin, sqrt, square, log, boolean comparisons, euclidean distance, etc.
+* Data **input pipelines** (input_fn) using:
+  * tf.estimator.inputs.**pandas_input_fn**, 
+  * tf.train.**string_input_producer**, and 
+  * tf.data.**Dataset** APIs to read both **.csv** and **.tfrecords** (tf.example) data files
+  * tf.contrib.timeseries.**RandomWindowInputFn** and **WholeDatasetInputFn** for time-series data
+  * Feature **preprocessing** and **creation** as part of reading data (input_fn), for example, sin, sqrt, square, log, boolean comparisons, euclidean distance, etc.
 
-*  A standard approach to prepare **wide** (sparse) and **deep** (dense) feature_column(s) for Wide and Deep **DNN Liner Combined Models**
+* A standard approach to prepare **wide** (sparse) and **deep** (dense) feature_column(s) for Wide and Deep **DNN Liner Combined Models**
 
-*  The use of **normalizer_fn** in numeric_column() to **scale** the numeric features using pre-computed statistics (for Min-Max or Standard scaling)
+* The use of **normalizer_fn** in numeric_column() to **scale** the numeric features using pre-computed statistics (for Min-Max or Standard scaling)
 
 * The use of **weight_column** in the canned estimators