This is some tutorial to get you started with Azure ML Service

This is detailed documentation for the Azure ML tutorial/workshop on flight prediction.

Tutorial

[WIP] pill id tutorial on Azure ML

Summary In this article, we are going to present the solution for the Women’s Health Risk Assessment data science competition on Microsoft’s Cortana Intelligence platform which was ranked among the top 5%. In this page, you can find the published Azure ML Studio experiment., a description of the data science process used, and finally a link to the R code (in GitHub). Competition Here is the description from the Microsoft Cortana Competition “To help achieve the goal of improving women's reproductive health outcomes in underdeveloped regions, this competition calls for optimized machine learning solutions so that a patient can be accurately categorized into different health risk segments and subgroups. Based on the categories that a patient falls in, healthcare providers can offer an appropriate education and training program to patients. Such customized programs have a better chance to help reduce the reproductive health risk of patients. This dataset used in this competition was collected via survey in 2015 as part of a Bill & Melinda Gates Foundation funded project exploring the wants, needs, and behaviors of women and girls with regards to their sexual and reproductive health in nine geographies. The objective of this machine learning competition is to build machine learning models to assign a young woman subject (15-30 years old) in one of the 9 underdeveloped regions into a risk segment, and a subgroup within the segment.” https://gallery.cortanaintelligence.com/Competition/Womens-Health-Risk-Assessment-1 Dataset The contains 9000 observations The original training dataset is in CSV format and can be found in the competition’s description. To submit a solution, two options are possible: build it in Azure ML Studio or build your solutions locally in R and then submit it through Azure ML Studio. An Azure ML’s solution, and a R script code where given as example. The two solutions are based on the use of a Generalized Linear Model is automatically downloaded. You can find a detailed description of the dataset, the R sample Code and a tutorial using Azure ML and R in the competition page Solution I started following the R tutorial for this competition. Then I have submitted the exact same R solution. The sample model has a 77% accuracy Pre-processing & Cleaning The first thing I did was changing the initial multinomial model (nnet package) for a random forest model (RandomForest package). All missing values have been replaced by 0 Feature selection Features have been selected using the function varImpPlot from the randomforest package Parameter tuning I have chosen (for educational matter) to use the module Tune Model Hyperparameters in Azure ML Studio. I could have also used the R Package Caret. Evaluation The final model has an accuracy of 86.36% (18 position over almost 500 participants) You can download the R code here

This is a tutorial for setting up a full MLOps pipeline using Azure

Tutorial on how to parallelize experiments using Azure ML

Code for the tutorial on integrating Azure ML with PubNub MQTT Bridge

Tutorial on setup of online and offline feature stores in Azure Machine Learning (Azure ML), using command line interface (Az CLI)

R Tutorial for Azure ML Services R SDK

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For this project, the Bank Marketing Dataset is used with Azure to configure a cloud-based machine learning production model. The hyper-parameters are optimized using the hyperdrive tool and a comparison is made to Azure's AutoML hyperparameter tuning tool

Description of Microsoft Azure ML for Sentiment Analysis Use Case with tutorial

A practical handbook for newcomers to understand and apply AI and ML technologies using cloud services like AWS, Azure, and Google Cloud. Covers fundamental concepts, responsible AI practices, cloud infrastructure, real-world case studies, and hands-on tutorials to get you started with AI development.

Tutorials completed while learning the Azure ML Service

Azure ML tutorial

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Tutorials for Azure ML

Azure ml tutorial documentation

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My personal hands-on lab exercises for the Microsoft Learning Paths exploring Azure Machine Learning

Esempio MLOps

My personal hands-on lab exercises for the Microsoft Learning Paths exploring Azure MLops

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Desafio DIO BootCamp "Microsoft Azure AI Fundamentals", Desafio "Trabalhando com Machine Learning na Prática no Azure ML"

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Tutorial for MSE 544 Azure ML HyperDrive

Quickstart Tutorial on R for Azure ML