Microsoft Azure Machine Learning Tutorial

Microsoft Azure Machine Learning Tutorial

In this Azure tutorial, we will discuss Microsoft Azure Machine Learning Tutorial. Along with this, we will also discuss the below topics.

  • What is Machine Learning?
  • Benefits of Azure Machine Learning
  • Why Azure Machine Learning?
  • Difference between Azure Machine Learning Studio and Azure Machine Learning Service
  • Working with Azure Machine Learning Studio
  • Hands-on with Azure Machine Learning Studio
  • Azure Machine Learning Designer

Microsoft Azure Machine Learning Tutorial

Well, Before moving to the actual topic i.e Microsoft Azure Machine Learning Tutorial, We should aware of What is Machine Learning?

What is Machine Learning?

Machine Learning is the part of Artificial intelligence (AI) that provides the ability to the systems to learn without being explicitly Programmed. This is an application from Artificial intelligence.

Now the next question that is coming to your mind is, What is Artificial intelligence (AI) then?

Artificial intelligence (AI) is the process of simulating the intelligence, thinking, and behavior of a human in the machine with the help of Programs.

Now let’s move towards the Microsoft Azure Machine Learning Tutorial.

Azure Machine Learning is a different set of services and tools that helps the developers to develop and deploy the different Machine learning models easily. Primarily, it also saves a lot of cost and time needed for the implementation.

The most interesting fact is you can create the machine learning model now with a few clicks. Can you believe this? Yes, this is the truth, with the help of IDE i.e Azure Machine Learning Studio, we can develop and deploy the machine learning model easily.

Benefits of Azure Machine Learning | Why Azure Machine Learning?

  • There are lots of benefits that Azure ML provides that are like saves lots of cost and time so the medium size and small size business can dare to start developing the machine learning models. So it actually provides the opportunity to different middle scale companies and small companies to go with the Machine learning models.
  • The other benefit with the Azure ML is you can directly connect with the Azure SQL and other services so it will make the task for the developer easy.
  • Azure Machine Learning supports the operation with massive data or Big data. No restrictions on the quantity of data.
  • Coming to the Pricing model, It is flexible for pricing with the “pay as you go” model. Which really helps the organization to save a lot of costs.
  • Azure ML is very user friendly and comes with a simple set of tools that are easy to use.
  • Azure Machine Learning Studio helps us with drag and drop feature where no coding knowledge is required.
  • Azure Machine Learning Studio comes with some in-built algorithms and data transformation tools that help a lot.
  • The set of Azure ML tools are built with the latest technologies and features that help to provide more accurate predictions.
  • It helps with real-time predictions with the fastest algorithm.
  • We can Publish our data model as a web service which really helps.
  • The clutter feature in Office 365 is also being used along with the Azure Machine Learning that helps to limit the inaccuracy of the data.

These are few of the benefits with the Azure Machine Learning and this why Azure Machine Learning?

Azure Machine Learning comes with the Azure Machine Learning Studio and Azure Machine Learning Service.

Azure Machine Learning Studio is the IDE (Integrated Development Environment) that helps us with the environment and options to develop the Machine learning models with the drag and drop facilities. The best part is no coding knowledge required here.

Azure Machine Learning Service is a fully managed cloud service that helps us to provide the facility to train, deploy, and manage the machine learning models. Here coding is involved. It supports open-source technologies.

You can use many open source python packages like TensorFlow, Pytorch, etc along with the Azure Machine Learning Service.

Azure Machine Learning service also comes with the support with Visual Studio Code extension that helps you manage your resources and deployments in the Visual Studio Code.

Difference between Azure Machine Learning Studio and Azure Machine Learning Service

Below are few key differences between the Azure Machine Learning Studio and Azure Machine Learning Service.

Azure Machine Learning StudioAzure Machine Learning Service
The IDE (Integrated Development Environment) for the development of the Machine learning model.Azure Machine Learning Service is a fully managed cloud service to manage the Machine Learning model.
No coding needed.It is a coding environment.
Provides lots of inbuilt Machine learning algorithms that you can use to build your Machine learning models. In case of any customization that is not supported by the Out Of Box approach with Azure Machine Learning Studio, you can go with the Azure Machine Learning Service.
Provides the feature with drag and drop facility to built the Azure Machine learning model.You have to build the Azure ML Models with the help of Python coding.

These are few key differences between Azure Machine Learning Studio and Azure Machine Learning Service.

Working with Azure Machine Learning Studio

If we will discuss little more on the Azure Machine Learning Studio, Microsoft Azure Machine Learning Studio is a tool that provides you the drag and drops feature and is used to build, test, and deploy the predictive analytics solution on your data.

As already discussed, We can publish Azure Machine learning as a web service that can be consumed by different external applications as well as Excel, etc.

Developing an Azure Machine learning model is an iterative process where you use data from different sources, transform, and analyze that data through various data manipulation mechanisms, and end up with generating a set of Outputs.

Azure Machine Learning Studio provides you a visual workspace that helps you to easily build, test, and iterate the Predictive data analysis model. You can drag and drop the datasets and modules and then after connecting them to form an experiment. You can use that experiment in your Azure Machine learning studio.

A valid experiment has the below characteristics

  1. The experiment should contain minimum one dataset and one module
  2. Datasets may be connected to only different modules.
  3. The modules can be connected to different datasets or different other modules.
  4. You have to set all the required parameters for the modules that you are using for the experiment.

Datasets

A dataset is a data that you will have to upload to Azure Machine learning Studio that you will use it for the data analytics model. You can upload the dataset as per your requirement. Along with this, you will also get a set of sample datasets from different areas, those data sets you can utilize as per your requirement.

Some of the sample datasets available are

  • MPG Dataset for various automobiles
  • Weather Dataset
  • Flight Delays Data
  • Time Series Dataset
  • Book Reviews from Amazon
  • Movie Tweets
  • Airport Codes Dataset

Modules

Modules are nothing but a different set of algorithms that you are performing on your data. Azure Machine learning Studio has a different set of modules that starts from the data ingress functions to training, scoring, and validation processes, etc.

Below are few examples of the inbuilt modules.

  • Linear Regression
  • Score Model
  • two-class Logistic Regression

There is absolutely no programming knowledge required while using Azure Machine Learning Studio. What you need is, you need datasets and modules to construct your predictive data analytics model.

Now the next question that comes in mind is from where, you will get the Azure Machine Learning studio.

Hands-on with Azure Machine Learning Studio

Before accessing the Azure Machine Learning Studio, As a prerequisite, what we need is, we need Azure account or Subscription. Make sure, you have an Azure Account or Azure Subscription. If you don’t have an Azure account, you can Create An Azure Free Account now.

Now assuming you have a valid Azure account now, to access the Azure Machine Learning Studio, you can search for the Azure Machine Learning studio in google and access the first link and once the link opens, click on the Sign In button and enter your Azure credentials when it prompts you to enter your credentials.

Difference between Azure Machine Learning Studio and Azure Machine Learning Service

Once you Sign In with your Azure credentials, you can able to see the Microsoft Azure Machine Learning Studio as below.

Microsoft Azure Machine Learning Tutorial

Now the next step is, you can select the Experiment from the left navigation and then click on the + New button to create a new experiment

Working with Azure Machine Learning Studio

Now, click on the Blank experiment as highlighted below.

Hands-on with Azure Machine Learning Studio

You can see “Experiment created on the date”. Now you can rename it to the Experiment name that you want to mention. Here I mentioned the experiment name as Prediction for Diabetes.

Create your first data science experiment in Machine Learning Studio

Now the next step is to select the DataSet. To do that, click on the Saved Datasets from the left side menu and Expands the Samples, you can able to see many sample datasets. From those datasets, select the dataset named as Pima Indian Diabetes Binary Classification Dataset.

azure machine learning service

Now drag the Pima Indian Diabetes Binary Classification Dataset to the center of the screen as mentioned with the Arrow mark with the hints Drag items here.

azure machine learning studio tutorial

Now, if you want to see what is exactly there inside the data set which we have selected for our experiment then click on the 1 with circle option as high lighted below and then click on the Visualize option. You can also download that data by clicking on the Download link.

azure machine learning services vs studio

You can see now, it is showing the detailed data on that.

azure machine learning service tutorial

Now the next step is to select the set of columns that you want choose for your Data Model.

microsoft azure machine learning tutorial for beginners

Now on the left side, you can see a search bar, search for Select Columns in Dataset, and drag that search result towards the center of the screen down to the Pima Indian Diabetes Binary Classification Dataset box and Now connect both the box by dragging from the dot from the first box. Once you connected both it will highlight with an arrow as shown below.

Now click on the Launch Column Selector button from the right side.

microsoft azure machine learning studio tutorial

Now you can see it is shown with lists of 9 Columns. All are relevant to our topic so you can select all the columns and click on the > button, you can see now all the columns are now moved to the Selected Columns section, now click on the tick mark button on the bottom of the screen as highlighted below.

azure machine learning training

Now the next step is, we need to split our data into the training and test datasets. To do that, Search for the Split Data from the left side navigation search bar.

Drag the search result Split Data on the Workspace under the Select Columns in DataSet box. Then connect the Select Columns in DataSet box and “Split Data” box by dragging the dot symbol as highlighted below. Once both are connected, you can see an arrow as highlighted below. From the right side, you can change the percentage if you want.

getting started with azure machine learning

Now the next thing that we need is the algorithm to train our data model. So there are many algorithms available but here we will choose the two-class logistic regression algorithm. Now, you might be thinking about why this algorithm?. OK, the answer here is this algorithm is used to predict the probability of an outcome.

We are splitting into two Out comes here, so we are using the two-class logistic regression algorithm.

You need to search for the two-class logistic regression from the left side search box and then you will find the two-class logistic regression algorithm as the search result. Drag that algorithm to the workspace area as highlighted below.

azure machine learning functions

Now that we have selected our algorithm, Now we need to train our Data Model. For that what we will have to do is, we need to search for the Train Model and drag it to the workspace as highlighted below.

Now connect it from two-class Logistic Regression to Train Model and Split Data to the Train Model as highlighted below. Now click on the Launch column selector to select the columns and select the first column, you can select any of the column based on your requirement, I have selected the first column.

azure ml studio tutorial

Now we need to score our trained Model. To do that, Search for the Score Model and drag the search result to the Workspace. Connect it to the Train Model and then connect the Training Dataset from the Split Data to Train Model.

Now the next step is, search for the Evaluate Model from the left navigation search area and then drag the result to the Workspace. Connect the Score Model with it. see the below image for your reference.

Now the next step is to click on the save button to save the experiment and then click on the Run button to run the experiment.

machine learning studio workspace azure

After you click on the run button, it will verify each stage and will show a green tick mark on each of the stages. See the below screenshot for your reference. Once it will finish running, it will show Finished running.

azure machine learning deployment

Now click on the dot on the Evaluate Model and then click on the Visualize option to see the report.

azure machine learning designer

You can see the report below. You can adjust the threshold to minimize the false negative value. Ideally, False Positive and False Negative should be minimum.

azure ml workspace

This is just one example of working with Azure Machine Learning Studio. You can do so many things using this.

Azure Machine Learning Designer

Azure Machine Learning designer helps you to connect the datasets and the modules in an interactive way that helps to create the Machine learning models.

The Azure Machine Learning Designer actually uses the Azure Machine Learning workspace to Organize the resources.

With the help of the Azure Machine Learning Designer, you can able to get a canvas to build, test, and deploy the machine learning data models. You can able to perform the following activities

  • You can able to drag and drop the datasets and modules into the workspace area or canvas area.
  • You can able to connect with different modules and datasets with the modules, etc.
  • You can able to submit a pipeline run using different resources in your Azure Machine learning workspace.
  • It helps us to make predictions on new data in real-time by deploying a real-time inference pipeline to a real-time endpoint.

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Conclusion

Well, in this article, we discussed Microsoft Azure Machine Learning Tutorial, What is Machine Learning?, Benefits of Azure Machine Learning, Why Azure Machine Learning?. Along with this, we also discussed the Difference between Azure Machine Learning Studio and Azure Machine Learning Service, Working with Azure Machine Learning Studio, and finally, we discussed Hands-on with Azure Machine Learning Studio, Azure Machine Learning Designer.

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