fix api link

Author: EricWrightAtWork

articles/machine-learning/how-to-auto-train-forecast.md

@@ -401,7 +401,7 @@ In this sample, the step size for the rolling forecast is set to one which means
 
 ### Prediction into the future
 
-The [forecast_quantiles()](python/api/azureml-training-tabular/azureml.training.tabular.models.forecasting_pipeline_wrapper_base.forecastingpipelinewrapperbase#azureml-training-tabular-models-forecasting-pipeline-wrapper-base-forecastingpipelinewrapperbase-forecast-quantiles) generates forecasts for given quantiles of the prediction distribution. This method thus provides a way to get a point forecast with a cone of uncertainty around it. Learn more in the [Forecasting away from training data notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb).
+The [forecast_quantiles()](/python/api/azureml-training-tabular/azureml.training.tabular.models.forecasting_pipeline_wrapper_base.forecastingpipelinewrapperbase#azureml-training-tabular-models-forecasting-pipeline-wrapper-base-forecastingpipelinewrapperbase-forecast-quantiles) generates forecasts for given quantiles of the prediction distribution. This method thus provides a way to get a point forecast with a cone of uncertainty around it. Learn more in the [Forecasting away from training data notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb).
 
 In the following example, you first replace all values in `y_pred` with `NaN`. The forecast origin is at the end of training data in this case. However, if you replaced only the second half of `y_pred` with `NaN`, the function would leave the numerical values in the first half unmodified, but forecast the `NaN` values in the second half. The function returns both the forecasted values and the aligned features.