Broken link fixed

Author: pritamso

articles/machine-learning/concept-automated-ml.md

@@ -68,15 +68,15 @@ Classification is a type of supervised learning in which models learn using trai
 
 The main goal of classification models is to predict which categories new data will fall into based on learnings from its training data. Common classification examples include fraud detection, handwriting recognition, and object detection. 
 
-See an example of classification and automated machine learning in this Python notebook: [Bank Marketing](https://github.com/Azure/azureml-examples/blob/main/sdk/jobs/automl-standalone-jobs/automl-classification-task-bankmarketing/automl-classification-task-bankmarketing.ipynb).
+See an example of classification and automated machine learning in this Python notebook: [Bank Marketing](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-classification-task-bankmarketing/automl-classification-task-bankmarketing.ipynb).
 
 ### Regression
 
 Similar to classification, regression tasks are also a common supervised learning task. AzureML offers featurization specific to regression problems. Learn more about [featurization options](how-to-configure-auto-train.md#data-featurization). You can also find the list of algorithms supported by AutoML [here](how-to-configure-auto-train.md#supported-algorithms). 
 
 Different from classification where predicted output values are categorical, regression models predict numerical output values based on independent predictors. In regression, the objective is to help establish the relationship among those independent predictor variables by estimating how one variable impacts the others. For example, automobile price based on features like, gas mileage, safety rating, etc. 
 
-See an example of regression and automated machine learning for predictions in these Python notebooks: [Hardware Performance](https://github.com/Azure/azureml-examples/blob/main/sdk/jobs/automl-standalone-jobs/automl-regression-task-hardware-performance/automl-regression-task-hardware-performance.ipynb).
+See an example of regression and automated machine learning for predictions in these Python notebooks: [Hardware Performance](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-regression-task-hardware-performance/automl-regression-task-hardware-performance.ipynb).
 
 
 ### Time-series forecasting
@@ -93,7 +93,7 @@ Advanced forecasting configuration includes:
 * configurable lags
 * rolling window aggregate features
 
-See an example of forecasting and automated machine learning in this Python notebook: [Energy Demand](https://github.com/Azure/azureml-examples/blob/main/sdk/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced.ipynb).
+See an example of forecasting and automated machine learning in this Python notebook: [Energy Demand](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced.ipynb).
 
 ### Computer vision
 
@@ -218,7 +218,7 @@ How-to articles provide additional detail into what functionality automated ML o
 
 ### Jupyter notebook samples 
 
-Review detailed code examples and use cases in the [GitHub notebook repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/main/python-sdk/tutorials/automl-with-azureml](https://github.com/Azure/azureml-examples/tree/main/sdk/jobs/automl-standalone-jobs).
+Review detailed code examples and use cases in the [GitHub notebook repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs.
 
 
 ### Python SDK reference