Anouar BEN ZAHRA

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

This post will helps you to create your first Azure Machine Learning workspace. For this you should log up with either a free or paid Azure subscription, or using the free trial Azure Machine learning offer. First open the Azure Management Portal https://manage.windowsazure.com ,  To create a new experiment you should click on +New in the bottom left corner of the screen, then you have a set of options to create :  Data Set : this option allows you to upload a new dataset to use with your experiment from a local file on disk Experiment: A Empty experiment or a preexisting experiment to help get you started fast I would like to start with a real exemple such as predicting whether a person’s income exceeds $50,000 per year based on his demographics or census data. You can imagine how incredibly useful the ability to predict a person’s income might be in the world of sales and marketing. This is exactly the kind of predictive analytics that would be most useful for a succes

The first thing to get start with Azure Machine Learning journey is to get access to the Azure environnement, in this way we have 2 options : Option 1 : Trial offer :  http://azure.microsoft.com/en-us/pricing/free-trial  - With this offer allows you to get a $200 to spend on all Azure services only for one month  - This option will require youto apply using a Microsoft acoout and a valid credit card number for account verification purposes.  - During this month the Azure Managment Portal will prominently display your remaining trial credits  Option 2 :  http://azure.microsoft.com/en-us/pricing/free-trial - A free offer that allows you to access to the Azure Machine Learning environnement. - You need a valid Microsoft account  - Sign up for a Microsoft account visit :  http://windows.microsoft.com/en-US/windows-live/sign-up-create-account-how - if you are signed an introductory video is displayed if you like to take advantage of the free Azure Machine L

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 predictive models to find th

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 the post

Pour certains entre vous le terme IA évoque les films science-fiction, Pourtant, cette forme d'intelligence fait partie de notre vie quotidienne, qui peuvent être un objet connecté, chatbot et autres... Le domaine du machine learning se base sur un ensemble des concepts et d'algorithmes pour répondre à différents besoins. Chaque algorithme se repose sur des spécificités mathématiques et algorithmiques. Dans cet article je vais citer et expliquer brièvement 7 algorithmes dont 2 que j'ai du  l'implémenter   1. K-Means  NuGet Package : https://www.nuget.org/packages/K-Means Mon Git : https://github.com/AnouarBenZahra/K-MeansProject C'est un algorithme de classification non supervisé, qui prend un ensemble d’éléments, et k nombre de groupes, une segmentation en k groupes sera faite, toute on minimisant la distance euclidienne entre le centre et un élément donné.  Usage :  Imaginez vous souhaitiez choisir un téléphone portable, vous devez dans un premier

K-Means est un algorithme de classification non supervisé car le nombre de classes est inconnu, la recherche de classes sera faite à l'aveugle. Ce type de classification est facile à implémenter et à comprendre, le but ultime de cet algorithme est de regrouper les points selon un critère bien déterminé,  en effet l'entrée est un ensemble k groupes (clusters). Par la suite on vise à calculer la distance entre chaque individu (point) et les k centre, qui sont déjà choisis au début. la plus petite distance est retenue pour inclure cet individu dans le groupe ayant le centre le plus proche. Une fois tous les individus groupés, on aura k sous-nuages disjoints de la marge total pour chaque groupe, l'algorithme calcule des nouveaux centres de gravité l'algorithme s’arrête lorsque les groupes construits deviennent stables Attention : Le défaut de la méthode c'est qu'on est obligé de fixer le K, aussi le résultat est fortement couplé avec les centres initiaux J