In lesson 9, some more feature selection like PCA has been introduced. Also, how we can do PCA using R codes has been shown. Moreover, how we can run R code inside Azure ML Studio for doing PCA has been shown. Moreover, what is Azure Notebook and how we able to write the code there has been illustrated. In lesson 10, first some features in Azure ML Studio will shown such as how to export and re use a dataset in Azure ML Environment for other scenarios, then an example of how to do clustering in Azure ML Studio will be presented. In lesson 11, some explanation on what project is and how to allocate items to it. How to access some examples in Azure ML Studio. Also, about Azure mL Notebook, how to create one and how to access and to import the notebook into Azure Notebook. Next in lesson 12, how to create webservice of Azure ML model has been explained. How to create a proper input and output for webservice and how to see the webservice in Excel has been demonstrated. Also, in lesson 13 , how we able to tune a model using Hyper tuning approach is explained, what is hyper parameters and how we can so it in Azure ML Studio has been explained. Finally in lesson 14, The main concepts of cross validation and why we need it has been explained, next how we able to do cross validation in Azure mL studio using Partition and Sample plus Cross validate model has been explained.