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CAN YOU PROVIDE SOME RESOURCES OR TUTORIALS FOR BEGINNERS TO LEARN AZURE CAPSTONE PROJECTS?

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Microsoft Azure provides a wide range of cloud services that you can use to build real-world applications. When you are just starting out with Azure, it’s a good idea to take on capstone projects that allow you to gain hands-on experience with the platform. This will help reinforce your learning and prepare you to work on more advanced Azure projects.

Some good capstone project ideas for Azure beginners include:

Create a basic web application – This is a common first project that introduces key Azure services like Azure App Service, Azure SQL Database, Azure Blob Storage, etc. You’ll deploy a simple website that interacts with a database and stores files. Microsoft has tutorials for building websites using ASP.NET, PHP, Node.js, etc.

Build a cloud-hosted REST API – APIs are the foundation of modern applications. You can develop a RESTful web API using Azure Functions, ASP.NET Web API, or another framework and deploy it to App Service. Include features like authentication, data access with Cosmos DB, etc.

Develop a serverless mobile backend – Use Azure Functions and other serverless compute services like Logic Apps as a backend for a simple mobile app. Consume APIs, store data in storage accounts or Cosmos DB, send push notifications with Notification Hubs, etc.

Implement cloud infrastructure automation – Learn to deploy and manage your Azure resources using infrastructure as code tools like Azure Resource Manager templates, Terraform, or Bicep. Automatically deploy virtual machines, web apps, databases and other services.

Build an image or file processing pipeline – Use Azure services like Blob Storage, Data Factory, Functions and Cognitive Services to implement a file upload workflow that processes images/files, extracts metadata, applies AI/ML models, and more.

Create an IoT solution – Build a basic IoT prototype that collects sensor data from simulated or real devices into IoT Hub, analyzes it using Stream Analytics, and visualizes metrics with Power BI.

Configure a highly available web application – Implement load balancing, auto-scaling, failover, and other high availability features for a web app using Azure App Service, Traffic Manager, and monitor it with Azure Monitor.

Here are some detailed tutorials and courses to help you successfully complete Azure capstone projects:

Microsoft Learn Modules – Microsoft’s official self-paced learning platform has excellent beginner modules on topics like “Build your first Azure app”, “Work with Azure Storage”, “Implement web apps on Azure”, etc.

Azure Documentation – The documentation includes dozens of step-by-step tutorials on Azure services, with detailed guidance on everything from account setup to building full solutions.

Cloud Skills Challenge – A beginner-friendly hands-on labs from Microsoft that teach core Azure skills through guided scenarios and projects. The “Azure Developer Fundamentals” pathway is very useful.

A Cloud Guru (a.k.a Linux Academy) – Paid courses from this top cloud training provider that teach Azure fundamentals and then guide learners through implementing solutions using common services.

CognoSphere Azure Tutorials – Free video tutorials that walk through building end-to-end cloud apps using Blazor, React, Python, Java and more on Azure. Great for visual learners.

Udemy – Many affordable and highly-rated project-based courses to learn Azure development, DevOps, AI/ML, serverless computing and more through guided tutorials.

edX Microsoft Azure Courses – Free courses from Microsoft on edX platform that range from introductory to advanced levels, from single services to fully-featured applications. Requires verification for certificates.

YouTube Azure Channels – YouTubers like Cloud Concepts, Kevin Williamson, Scott Hanselman, etc. provide project tutorials, code reviews and other guidance for Azure.

When taking on an Azure capstone project, be sure to thoroughly research documentation, ask questions on forums, and carefully plan each step. Break projects into small, well-defined tasks and celebrate incremental wins. Completing even simple capstone projects will accelerate your learning and give you confidence to tackle larger projects. With practice, it gets much easier to design and deploy solutions on the Azure cloud platform.

CAN YOU PROVIDE SOME EXAMPLES OF KAGGLE COMPETITIONS THAT WOULD BE SUITABLE FOR BEGINNERS

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Titanic: Machine Learning from Disaster (Beginner-friendly): This is widely considered the best competition for newcomers to Kaggle as it is straightforward and a classic “getting started” type of problem. The goal is to predict which passengers survived the sinking of the RMS Titanic using variables like age, sex and passenger class. This was one of the earliest competitions on Kaggle and has a very clear objective. Cleaning and exploring the data is quite simple, and many common machine learning algorithms like logistic regression, decision trees, and random forests can be applied. This competition introduces the basic pattern of exploring data, building models, and submitting your predictions for evaluation.

Digit Recognizer: This competition asks Kagglers to predict the digit that appears in images of handwritten digits from 0-9. The data contains thousands of 28×28 pixel greyscale images of handwritten single digits. This competition has simple, pre-processed data and a clear classification task, making it good for beginners. Common techniques like convolutional neural networks (CNNs) have proven very effective. While computer vision problems can require more advanced techniques, the data preparation and model building is quite straightforward here.

House Prices – Advanced Regression Techniques: The goal here is to predict housing prices using a provided historical dataset from Ames, Iowa. The features include basic housing information like sqft living, the number of bedrooms, year built etc. This dataset lends itself well to introductory regression techniques like linear regression, gradient boosting and random forest regression. The objective and features are clearly defined. Cleaning and exploring the data involves standard approaches to numeric and categorical variables. This competition allows newcomers to learn common regression techniques before tackling more complex data types.

Bike Sharing Demand: This competition uses historical hourly and seasonal data from the Capital Bikeshare bike rental program in Washington D.C. to predict future bike rental demand. Predictors include weather, dates and times. Forecasting problems are very common in machine learning and this represents an straightforward introduction to the genre with its clear objective and numeric features. Again, common regression algorithms like gradient boosting and XGBoost can be effectively applied. Feature engineering ideas like handling datetimes and including previous rentals as predictors can be explored. The core techniques are entry-level but introduce a relevant business problem.

SIIM-ACR Pneumothorax Segmentation: This medical imaging competition introduces computer vision concepts while still being relatively appropriate for beginners. The task involves segmenting regions of potential pneumothorax (collapsed lung) within X-ray images. While computer vision modeling, especially with deep learning, can get quite advanced, basic convolutional or encoder-decoder type models have achieved good results on this dataset. Similarly to the Digit Recognizer challenge, the data is pre-processed and the classification objective is clear. Common frameworks like Keras and PyTorch allow fast model building and experimentation to learn foundational CV methods. The real-world medical application also provides strong motivation for newcomers.

These Kaggle competitions provide clear, self-contained problems well-suited to explore foundational machine learning techniques. They introduce standard algorithm types, common data wrangling tasks, and validation strategies in realistic and relevant prediction scenarios. The digit, housing, rental demand, medical imaging examples can each be effectively tackled by applying logistic regression, linear regression, random forest, boosting, or CNN models – algorithms appropriate for new learners. The clean Titanic and housing datasets make data exploration straightforward. These competitions allow beginners to start developing machine learning skills through exposure to varied techniques and domains, while keeping modeling itself approachable. They set the stage for exploring increasingly complex problems as skills progress.