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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Create a Virtual Machine for Free on Microsoft Azure

Aryan Garg
Aryan Garg Last Updated : 12 Jul, 2022
7 min read

This article was published as a part of the Data Science Blogathon.

Introduction

This article will show how we can create a free student account on Microsoft Azure without any credit/debit card details and get $100 free for the first 12 months. The prerequisite is that you must have a school or university email address to avail of this benefit.

After creating the student account, I will also show you how you can create a virtual machine on it to deploy your web applications.

First, let’s discuss Microsoft Azure in more detail.

Microsoft Azure Logo
Source – https://safe-connect.com/our-partners/

It is a public cloud computing platform developed by Microsoft. It includes a computing engine, analytics, storage, networking, etc. It also provides a portal for users to manage their cloud services and resources that they have taken from Microsoft.

Below are the various services offered by Microsoft Azure:

Microsoft Azure
Source – https://www.datashift.eu/technology/microsoft-azure

This tutorial will use its Compute Engine service and create a Virtual Machine to host web applications.

Let’s get started, 😉

Creating a Student Account

In this section, we will create our free student account. As I mentioned earlier, you must have a school or university email.

1. Go to the Microsoft Azure Website.

2. Click on Start free button.

create student account

3. Now login to your Microsoft Account, or you can create a new account if you don’t have any.

Note: Do not use your Work/Institution email while signing in on your Microsoft Account. Log in using any of your personnel’s Gmail or Yahoo email addresses. We will later use the Work/Institution email to verify the student account.

Microsoft sign in

4. After successful login, a Student Verification Page opens, in which you have to enter your Work/Institution email address to verify your academic status.

If your Work/Institution email is valid, you will receive an activation link on your Work/Institution email address to activate your account.

After activating the account, you will be automatically redirected to your Azure Portal. Or, you can also access your portal via that link.

It is what your Azure Portal looks like:

Creating a Virtual Machine

In this section, we will create our Ubuntu Virtual machine on Microsoft Azure.

1. Click on the Virtual machines button on your portal.

2. Navigate to Create and then select Azure virtual machine. Refer to the image below.

3. Enter the VM Credentials

Navigate to the Basics tab and create a new Resource Group. Also, enter the Name and Region of the VM.

Note: Sometimes, you will face an issue with the availability of the VMs, that the VMs are not available in the selected region due to the high demand. It mainly occurs in the free tier accounts. If you don’t find any available region, leave that part empty and let Azure select a suitable region automatically.

Now select the following:

  • Select Availability zone as Zones 1 because we want our server only in one zone. But you can also select multiple zones if you wish for higher availability and fewer downtimes.
  • Then select the Operating System of your choice. In this tutorial, we have chosen a Ubuntu Server.
  • Finally, please choose the size of the server. It depends on your budget and requirements.

Then we will set an SSH Public Key to access our VM Remotely. Also, we will select the Inbound port rules to none. Inbound port rules are used to create the firewall settings, and we will make these settings later in the Networking section of the VM.

What is SSH?

SSH (Secure Shell) is a network protocol that enables a secure connection between two machines. It allows transferring files and data in a highly secure manner, using encryption algorithms to encrypt the data during the transfer.

By SSH, we will connect our Local Machine to that VM using a private-public key pair.

Now Navigate to the Disks section, where we will select the type and size of the Disk that we want to use with the VM.

You can select between an HDD or an SSD based on your budget.

Now, you have to create a disk of the particular size that you want to use.

You can select the Size and Type of the Disk according to your requirement.

After creating the Disk, move next to the Networking section.

Select the Advanced networking and click on Create new button to create a new networking group.

Now we will select the PORTs in which we want to listen to the requests. Also, please enter the name of your security group, and then we will add a new Inbound rule.

For the Source, we will select * to open all the ports. But for the Destination, we will select only some specific ports on which we can make our server listen to requests. In this tutorial, we will open default PORT 80 and PORT 5000.

Note: You must run your web server on PORT 80 or on PORT 5000 to access your web page publically.

Now go back to your Networking section.

We will not use a Load Balancer at this time. It is used to distribute the traffic between many servers. If you want to know what is a load balancer and wants to create it. You can refer to my other article. In this, I have demonstrated how can we create a load balancer on the Google Cloud Platform.

Now Navigate directly to the Tags section. We don’t have something to change in the Management and Advanced Section. But if you want to explore it, you can also explore it.

We will create a Tag name in the Tags section to categorize the resources.

After that, please review all the configurations of the VM before the final commitment.

Microsoft Azure

Review all the configurations here

Microsoft Azure

Finally, click on Create button. After that, it will download the Private SSH Key for you to access that VM remotely.

Note: Never share your Private key with anyone. Also, keep it with you because you cannot download it again.

generate new key pair

Your downloaded private key must look like this:

private key

Save your private key. You don’t get it later on if you have deleted it. Now, you will be redirected to your VM instance.

Microsoft Azure

Now, click on Go to resource button to see all the configurations of your VM. You will also see your Public IP address, Server Location, OS, etc.

Microsoft Azure

We will now establish a secure connection between our local machine and the VM instance we have just created.

Navigate to Connect>>SSH.

Microsoft Azure

Put the below-highlighted code in your local machine terminal. The code which is shown below is different for your virtual machine.

Microsoft Azure

Now, open your command prompt and paste that code along with the path of the Private Key that you have downloaded previously.

Microsoft Azure

After authenticating successfully, you will be entered into your VM. You can also monitor the CPU usage, RAM usage and Disk Utilization.

Hurray🎉, now the VM is ready to deploy your web applications.

Conclusion

This tutorial discussed how we could create a free student account on Microsoft Azure. Generally, for the standard account, you need a valid credit card or a debit card to verify your payment details. Still, for students, you only need a valid student email address provided by your university or the institution in which you are studying. Microsoft also offers free 100USD for 12 months to try its services.

Instead of Microsoft Azure, you can also use other famous cloud platforms like Google Cloud or Amazon Web Services. They also provide similar services at a competing price. In the upcoming articles, I will try to cover more services of Microsoft Azure.

The key takeaways of this tutorial are:
1. Firstly, we created the student account on Azure using a university email address.
2. Then, we have selected the configurations of the VM instance like location, size, operating system, etc.
3. Further, we have created an HDD Disk and configured firewall settings to open PORT 80 and PORT 5000.
4. Finally, we have connected our local machine with the VM using the Private SSH Key.

It is all for today. I hope you have enjoyed the article. If you have any doubts or suggestions, feel free to comment below. Or you can also connect with me on LinkedIn. I will be delighted to get associated with you.

Do check my other articles also.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Azureblogathonstudent accountvirtual machine
Aryan Garg
Aryan Garg

I am currently pursuing my Bachelor of Technology (B.Tech.) in Electrical Engineering and Engineering from the Indian Institute of Technology Jodhpur(IITJ). I am very enthusiastic about Machine learning, and Software Development. Feel free to connect with me on Linkedin.

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