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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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Azure Machine Learning Workflow

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The process of creating a Machine Learning on Azure  is composed of a many pattern of workflow steps. This workflow are designed to help users to create a new predictive alanytics in no limited time. The main steps in the process are summarized in this figure : Data :  Is your input, who will be acquired, compiled , analyzed, tested and trained Create the model :  There is a versious algorithms of machine learning. we should create a models who is capable to make predictions, based on interfaces about the datasets. Evaluate the model :  This step is so important in the process because we examine the accuracy of new predictive models based on ability to predict the correct outcome, when we know in advance the input and the output values. Accuracy is measured in terms of confidence factor approching the whole number one Refine and evaluate the model :  After evaluating the model we refine-it by comparing, contrasting and combining alternate...
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