Image Processing- Invoice recording using Power App, Microsoft Flow and Cognitive Service- Part 2

In this post, I am going to show how to connect the Power APP, that you created in Part One for the aim of taking a photo from your receipt, send it to the Microsoft Flow and cognitive service to convert the image to text. First, you need to follow the link for the creating ...

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Image Processing- Invoice recording using Power App, Microsoft Flow and Cognitive Service- Part 1

For a month I am looking to create a Microsoft App that user able to take a picture from an invoice then able to see the items in Power BI or store it in storage such as SQL server, excel and so forth. In this posts, first I will show how to create a Power ...

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Run R codes in Azure ML

  There is a possibility to run R codes and Python in Azure ML.  In this short post, I am going to show you how we can run an R codes to show some charts in Azure ML with ggplot2 package. I have a dataset about the dairy product in the USA. this dataset holds ...

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Interactive Charts using R and Power BI: Create Custom Visual Part 1

I am so excited about using Plotly packages in Power BI. So What is Plotly: is an R package for creating interactive web-based graphs via the open source JavaScript graphing library :https://plot.ly/r/getting-started/ this feature has been added recently and had been announce by Christian Christian Berg in Data insight summit 2017. I started to search about ...

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Speaking Files in Microsoft Data Insight Summit 2017

Microsoft Data insight Summit 2017 has been held in Seattle from 12 to 13 Jun. It is the main conference for Microsoft Data Analysis with Power BI. All interesting and new topics in Power BI has been presented. Most of the speakers are from Microsoft product team or  experienced speakers in this fields. there are ...

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Azure ML Part 9- Cross Validation: Machine Learning Prediction (6)

In the previous posts (from Part 1 to Part7), I have explained the whole process of doing machine learning inside the Azure ML, from import data, data cleaning, feature selection, training models, testing models, and evaluating. In the last post, I have explained one of the main ways of improving the algorithms performance name as “Tune ...

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Azure ML Part 8- Tune Parameters: Machine Learning Prediction (5)

In the previous posts from Part 1 to 7, I have explained how to do machine learning with Azure ML. I have explained some of the main components in Azure ML that helps us to do data wrangling, train the model, feature selection and evaluating the result. The data cleaning such as SQL transformation, select ...

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Azure ML Part 7: A Machine Learning Prediction scenario (4)

In the previous posts from Part 1 to 6, I have explained how to do machine learning process. The data cleaning such as SQL transformation, select specific columns, remove missing values,  Edit meta data, and normalize data.  Also, I have explained how to find relevant attributes  using Feature Selection Feature to identify which feature are more ...

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