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A software developer, specialized in Microsoft technologies 

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Naive Bayes Classification Algorithm

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You are a data scientist,  an Intelligence Artificielle engineer, or a Statisticien, this post can help you to resolve your problems You are working on a classification problem and you have generated your set of hypothesis, created features and discussed the importance of variables. Within an hour, stakeholders want to see the first cut of the model. What will you do? You have hunderds of thousands of data points and quite a few variables in your training data set. In such situation, if I were at your place, I would have used ‘Naive Bayes‘, which can be extremely fast relative to other classification algorithms. It works on Bayes theorem of probability to predict the class of unknown data set. You wold like to know haw it work ? Step 1: Convert the data set into a frequency table Step 2: Create Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64. Step 3: Naive Bayesian equation to calculate...
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ChatBot

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How we   choose the bot framework ? If i talk about Microsoft. Essentially we have a single bot, is built and later rolled out to multiple canvasses without any further customization. Currently, the platform supports many channels (such as Facebook Messenger, Slack, SMS, email, Web Chat) including Microsoft’s own premiers (like Skype, Cortana, Microsoft Teams, Bing, GroupMe). All settings are pre-configured on the Developer Portal with an easy-to-follow walkthrough about how to integrate the bot with the hosting applications. How we   consume the bot framework ? Developers can connect directly from their client application, web chat controls or mobile apps throw Direct Line REST API tool of the bot Connector. This API makes it possible to empower existing applications and services with a conversational user interface. Microsoft has its own massive range of cloud-based Cognitive Services   APIs with the power of machine learning and AI algorithms. ...
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Cross-Origin Resource Sharing and Azure Machine Learning web services

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Cross-Origin Resource Sharing (CORS) is a mechanism that allows web objects (e.g., JavaScript) from one domain to request objects (web service requests) from another domain. Cross-origin requests initiated from scripts have been subject to restrictions primarily for security reasons. CORS provides a way for web servers to support cross-site access controls, which enable secure cross-site data transfers. At the time of this writing, it should be noted that Azure Machine Learning web services do not currently support CORS requests. See the following link for more information: https://social.msdn.microsoft.com/Forums/en-US/b6ddeb77-30e1-45b2-b7c1-eb4492142c0a/azure-ml-published-web-services-cross-origin-requests?forum=MachineLearning. This CORS restriction really means that if you wish to fully exploit Azure Machine Learning web services for deployment, testing, and production for a wide variety of (web) clients, you will need to host your own server-side applications. You basically hav...
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