Definitive Guide to Azure Machine Learning Studio

In this module, first, some introduction to AI and machine learning will be provided, also the Microsoft AI technologies will be explained. Then in the next Lesson, some introduction to Azure ML Studio environment will be provided, next how to import data into it will be presented, how to create a new experiment for aim of machine learning will be presented, an overview of what modules we have will be shown, next how to visualize and how to work with portal will be explained, also how to visualize the data will be shown. In the lesson3, first some explanation on the machine learning process will be provided, some of the steps will be discussed briefly such as business understanding, predictive and descriptive models, what is regression and what is classification, then a simple scenario for prediction of cancer diagnosis will be presented and Audience will learn how to create a simple machine learning model with AzureML Studio.

In this module, an overview of how to import data and general approaches to clean data, choose model, and enhance the prediction will be discussed and shown. In Lesson 4, first a discussion on how to access to the data presented, next, how to import data into Azure ML Studio presented, Audience get familiar with Import data module and how to get data from web or Blob storage has been shown. Next, they have seen how to join two datasets using Join module. The audience learns how they able to use SQL Transformation module to join more data and in general how to write code in SQL editor in Azure ML Studio. Finally, how to enter data manually into Azure ML Studio has been shown. In lesson 5, first a discussion on how much data we need to avoid Overfitting and Underfitting and their concepts have been discussed. Next, some discussion on Variance and Baise presented. In the last part, some other features related to Azure ML Studio have been shown. Next in Lesson 6, some basic techniques of data cleaning from Missing value, detect outliers and remove them by clip values has been shown. Also how to normalize data and why we need to do that has been discussed and demonstrated. In Lesson 7, how we able to choose the model for machine learning will be discussed, and the related Azure ML cheat sheet has been shown. Moreover, the best practice of applying multiple machine learning model on a data set has been discussed. The process of feature selection has been explained and the different feature selection approaches in Azure ML Studio has been shown. Finally, how to evaluate the model presented. IN last lesson of this module, Lesson 8, a brief discussion on how to evaluate models in Azure ML Studio will be provided, even comparing more than two models. Also how we able to interpret the evaluation result in classification and regression has been discussed.

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.

In this module, there are different ways of using Azure ML Studio webservices in Other applications has been explained. First in lesson 15, how we able to use it in Power BI desktop using some R codes to run it ha been explained. Next in the lesson 16 how we able to handle it in Power BI Service for Premium capacity has been explained.