Update README.md

Author: Amit Banerjee

samples/features/sql2019notebooks/README.md

@@ -24,8 +24,7 @@ The [What's New](https://docs.microsoft.com/en-us/sql/sql-server/what-s-new-in-s
 ### Big Data, Machine Learning & Data Virtualization
 * **[SQL Server Big Data Clusters](https://github.com/microsoft/sqlworkshops/tree/master/sqlserver2019bigdataclusters/SQL2019BDC/notebooks)** - Part of our **[Ground to Cloud](https://aka.ms/sqlworkshops)** workshop. In this lab, you will use notebooks to experiment with SQL Server Big Data Clusters (BDC), and learn how you can use it to implement large-scale data processing and machine learning.
 * **[Data Virtualization using PolyBase](https://github.com/microsoft/sqlworkshops/tree/master/sql2019workshop/sql2019wks/08_DataVirtualization/sqldatahub)** - The notebooks in this SQL Server 2019 workshop covers how to use SQL Server as a hub for data virtualization for sources like Oracle, SAP HANA, Azure CosmosDB, SQL Server and Azure SQL Database.
-* **[train_score_export_ml_models_with_spark.ipynb](https://github.com/microsoft/sql-server-samples/blob/master/samples/features/sql-big-data-cluster/spark/sparkml/train_score_export_ml_models_with_spark.ipynb)** -This notebooks covers how you can use Spark to create and deploy machine learning models.
-* **[accessing_spark_via_livy.ipynb](https://github.com/microsoft/sql-server-samples/blob/master/samples/features/sql-big-data-cluster/spark/restful-api-access/accessing_spark_via_livy.ipynb)** - The notebook demostrates using spark service via the livy end point. 
+
 * **[Spark with Big Data Clusters](https://github.com/microsoft/sql-server-samples/tree/master/samples/features/sql-big-data-cluster/spark)** - The notebooks in this folder cover the following scenarios:
   * Data Loading - Transforming CSV to Parquet
   * Data Transfer - Spark to SQL using Spark JDBC connector
@@ -35,7 +34,7 @@ The [What's New](https://docs.microsoft.com/en-us/sql/sql-server/what-s-new-in-s
   * Restful-Access - Access Spark in BDC via restful Livy APIs
 
 * **Machine Learning**
- * **[Powerplant Output Prediction] (https://github.com/microsoft/sql-server-samples/blob/master/samples/features/sql-big-data-cluster/machine-learning/spark/h2o/h2o-automl-powerplant.ipynb)** - This sample uses the automated machine learning capabilities of the third party H2O package running in Spark in a SQL Server 2019 Big Data Cluster to build a machine learning model that predicts powerplant output.
+ * **[Powerplant Output Prediction](https://github.com/microsoft/sql-server-samples/blob/master/samples/features/sql-big-data-cluster/machine-learning/spark/h2o/h2o-automl-powerplant.ipynb)** - This sample uses the automated machine learning capabilities of the third party H2O package running in Spark in a SQL Server 2019 Big Data Cluster to build a machine learning model that predicts powerplant output.
  * **[TensorFlow on GPUs in SQL Server 2019 big data cluster](https://github.com/microsoft/sql-server-samples/tree/master/samples/features/sql-big-data-cluster/machine-learning/spark/tensorflow)** - The notebooks in this directory illustrate fitting TensorFlow image classification models using GPU acceleration.
 
 ### SQL Server Troubleshooting Notebooks