diff --git a/        end_to_end_ml_lifecycle/README.md b/        end_to_end_ml_lifecycle/README.md
index 1ebb0cb40e..fc65b0d746 100644
--- a/        end_to_end_ml_lifecycle/README.md	
+++ b/        end_to_end_ml_lifecycle/README.md	
@@ -6,4 +6,4 @@ These examples are a diverse collection of end-to-end notebooks that demonstrate
 
 - [Customer Churn Prediction with Amazon SageMaker Autopilot](sm-autopilot_customer_churn.ipynb)
 - [Housing Price Prediction with Amazon SageMaker Autopilot](sm-autopilot_linear_regression_california_housing.ipynb)
-- [Time-Series Forecasting with Amazon SageMaker Autopilot](sm-sm-autopilot_time_series_forecasting.ipynb)
+- [Time-Series Forecasting with Amazon SageMaker Autopilot](sm-autopilot_time_series_forecasting.ipynb)
diff --git a/       prepare_data/README.md b/       prepare_data/README.md
index f31c105abb..a0b5d97148 100644
--- a/       prepare_data/README.md	
+++ b/       prepare_data/README.md	
@@ -2,7 +2,7 @@
 
 ### Prepare Data
 
-The example notebooks within this folder showcase Sagemaker's data preparation capabilities. Data preparation in machine learning refers to the process of collecting, preprocessing, and organizing raw data to make it suitable for analysis and modeling.
+The example notebooks within this folder showcase SageMaker's data preparation capabilities. Data preparation in machine learning refers to the process of collecting, preprocessing, and organizing raw data to make it suitable for analysis and modeling.
 
 - [Data Wrangler Data Prep Widget - Example Notebook](sm-data_wrangler_data_prep_widget/sm-data_wrangler_data_prep_widget.ipynb)
 - [Amazon SageMaker Feature Store: Feature Processor Introduction](sm-feature_store_feature_processor/sm-feature_store_feature_processor.ipynb)