Machine Learning without Coding: Getting Started with Azure ML

Carrying out Machine Learning using certain programming languages is a thing that is followed by many Data Scientists and Machine Learning engineers. It is the most trending topic that is being discussed and followed around the globe. Companies all over the world are investing huge amounts to include the mechanism of Machine Learning and AI in their system. It is a big achievement for every people out there and this sector would continue to flourish till the time the craze for exploration and research will prevail regarding technologies.

It is good to program any Machine Learning model from scratch and then implement it in the real world but, the task of writing these algorithms is a very tedious and techs centered one that is, one who has the prior knowledge of writing algorithms with the help of programming languages can only write and implement them. So, for people who are from the non-technical background, it is difficult to learn these languages instantly and then start coding.

To help the non-tech people by allowing them to carry out Machine Learning without coding various cloud-based services provide drag and drop facilities and a little bit of tweaking to run Machine Learning problems. One such cloud-based service is Microsoft Azure Machine Learning. This is a complete drag and drop kind of Machine Learning where you can run your model and even deploy it as a web service either in the cloud or in the local system and accessing the same through MS Excel. If you have a Microsoft Account then you can easily access the Microsoft Azure ML features. Let’s take a deep dive and learn how to get started with Microsoft Azure Machine Learning:

How to create a Machine Learning project or model on Microsoft Azure ML

Step 1: Open your browser and just type studio.azuleml.net and this will take you to the main content where the Azure ML is contained. This page will ask you to sign in or create a new account. If you are an existing Microsoft user and have an MS Account then just enter your login credentials and hurray! You have successfully logged in to your ML studio.

main page azure

Step 2: After logging in to your ML account you will see different options like Projects, Experiments, Web Services, Datasets, Trained Models, and Settings. The function of these options are as follows:

Projects: This allows users to create a new project that can contain various experiments and models as well as web services together which will make a complete package and can be shown to the public of interest.

Experiments: This is the main area where you will be creating your very first experiment and deploy the same as a web service so that to make your code accessible to users around the globe.

Web Services: This option helps us to deploy our experiment as a web-based API that can then be called through various programming languages or can be called within your local system through excel. Also, you can save your web service to the cloud to make it a public API that will be accessed by the users and can even sell your work to the world.

Experiments in Azure ML

Datasets: This is where one can find many pre-uploaded datasets from the Microsoft Team and one can use these to run their Machine Learning algorithms to get an idea of how things are done.

Trained Models: Here you can see which models you have trained and want to use the same for testing purposes.

Settings: The settings tab contains different options like editing the workspace, view, and regenerate the authorization tokens, allows users to manipulate your model by letting them work on the same.

Step 3: To start with your very first Azure ML project just click the bottom + sign button that says New. This will take you to a page where you will find the Start New Experiment option. Just click on that and your working pane will get opened. There is also a provision to upload your Python as well as R projects in Azure and tweak the same and deploy them as web service by clicking the Module tab.

create your first Azure ML project

Step 4: After your task pane has opened you can start working on your ML project.

Step 5: To start your very first ML project for example you want to perform a project of Logistic Regression the first and foremost thing is to acquire a dataset either through the cloud or through your system. The workflow mechanism of Azure ML is as follows:

Get the Data: Here we have 3 options either get the data manually or import the data from external data sources or unpack zipped datasets which help us to unpack zip files and use the data for our Machine Learning purpose.

import the data from external data for Machine learning purpose

Prepare the Data: This is mainly the feature engineering part with which we clean our data to work on the same. The various modules that can be used to prepare the data to include Clean Missing Data, Apply SQL Transformation, Convert to Indicator Values, Edit Metadata, and many more. There is also a provision to split our dataset into training and testing to validate our model and use it in real-world cases to make predictions.

Create Model Azure Machine Learning

Feature Selection: This is a very important step before training our model as it allows us to select the number of features that we want the algorithm to work on based on the degree of correlation between the target feature. The various type of feature selection contained here includes Principle Component Analysis, Fisher Linear Discriminant Analysis, Permutation Feature Importance and Filter Based Feature Selection.

Choose and Apply Learning Algorithms: This is the main step towards training our model that is to choose the algorithm that we want to use to train our data. There are varieties of ML algorithms that are present are like Decision Tree, Logistic Regression, Linear Regression, One vs Rest, Naïve Bayes, and many more. We can choose our preferred algorithm and start working using the same.

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Train and Evaluate the Model: This part is the final part of any ML model that is training, testing, and scoring the model. Some modules are present and we need to just drag and drop and do simple tweaking to get good results.

train model

Deploy the Model: After the training and evaluation are done we can now deploy the model as a web-based service with the help of the Deploy option present at the bottom of the task pane and use the same as an API.

One major thing to note in Azure ML is that it works with the mechanism of connecting output node of one variable with the input node of the other variable so users find it very easy to work using Azure ML and many industries are using this cloud-based service in their day to day applications. It is a very powerful tool for both supervised and unsupervised machine learning as well as for deep learning-related activities. For more details regarding its working, you can go through many tutorials on YouTube as well as Udemy. For a good tutorial on Azure ML you can follow this link:

https://www.udemy.com/course/machine-learning-using-azureml/learn/lecture/7432530#overview

Performing Logistic Regression

Here we have performed Logistic Regression with the help of Azure ML and have depicted the same using a pictorial representation:

Logistic Regression with the help of Azure ML

Microsoft Azure Machine Learning Studio (classic)

Conclusion

If you are a very big Machine Learning and Deep Learning enthusiast and want to work on projects related to the same then you should go with Azure ML Studio as it does not require any prior coding knowledge and the modules used are just operated using the drag and drop feature.

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