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  Wassup guys?!! Today we are gonna talk about a very interesting and cool feature of Azure ML studio, that can help you to consume bulk data, to predict complex outcomes, with/without writing any code. Don't believe it? Yeah, this could be done by setting up your Azure ML pipelines, and then implementing proper components to it, that can fiddle with the data, with cleansing, selecting necessary algorthims and training and evaluating the model with outcome scores (that indicates the correctness of your model structure). This article is a step by step process that can guide you to implement the same. But before I begin, let me refresh you with some basic concepts: What is a Decision Tree algorthim in Machine Learning? A decision tree is a supervised learning algorithm that is used for classification and regression modeling. Regression is a method used for predictive modeling, so these trees are used to either classify data or predict what will come next. Below is an example to evalu

  Hi friends, would like to talk today about the immense capabilities of Azure AI models, over the years how has it evolved and how could you leverage the same with C#, without writing much of a code (and of course very little understanding of AI). Before we begin, let us consider some basics: What is meant by REGRESSION in AI? Regression is a machine learning algorithm technique that uses input features to predict continuous numerical outputs. It's a type of supervised learning, meaning it's trained on a labeled dataset with known output values. Let me give you an example: Suppose I have data about your company, as to how has it been performing w.r.t. the profit it has been making over years.  Here is the data:  When plotted it gives a scatter like this: From here we can trace out a line that can cover the most of the points, and based on that we can easily find out the profit value, in another 10 years down the line. Such a plot is typically marked with the equation: y=mx+c ,

  Wassup guys?!!! Here comes another step by step walkthrough on setting up and leveraging Azure Custom Vision to load, tag, train and predict images, using awesome capabilities of Azure Custom Vision. Just in case  you are not aware of Azure Custom Vision, they help you easily customize your own state-of-the-art computer vision models that fit perfectly with your unique use case. All you need is to bring up a few examples of labeled images, train them and let Custom Vision do the hard work of identifying and predicting if any future image is matching its training set.  The trick is to label or tag your data. More you data you can provide and you keep classing them, and feed them to Azure, more precise would be the prediction.  The following article would give you an idea of how all this could be done. Pre-requisities:   An active Azure subscription Visual Studio Community Edition (just in case you want to run the code given along with this example).  Some good collection of similar p