Microsoft Azure AI Fundamentals

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In this article, we’ll talk about Microsoft AI, the pathway to learn for beginners who are curious to explore the Microsoft AI Platforms, various functionalities and features supported by Machine Learning Studio in Azure, and the processes to train and better the Machine Learning Models with Azure. We also learn about different algorithms and thus gain the overall knowledge to get started and work with Microsoft Azure AI.

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Microsoft AI is a powerful framework that enables organizations, researchers, and non-profits to use AI technologies with its powerful framework which offers services and features across domains of Machine Learning, Robotics, Data Science, IoT, and many more. Some of the key features of AI and its functionalities are as follows,

One of the advantages of Azure can be realized with this example of how Machine Learning becomes more scalable in the Cloud even while working on Notebooks. There would essentially be no limit on the system of cloud which would arise in on-premises services or our local system while working on research. Azure makes all these issues obsolete and gives us a platform without limitation.

During the first learning process, it is crucial to get a firsthand experience with the process. Being able to play with data and learning from it will help us get insight on working with Microsoft AI and Data too. For this, there are various sample data in Azure itself like Titanic Data, and Data for Credit Fraud Detection. Moreover, we can always use our own data or get it from external sources like Kaggle.

Working with this dataset involves predictive tasks of detection as well as the time scale of possible financial needs pre-hand by the clients. Building Machine Learning Model and using this data will be a full-fledged learning experience for production-level works.

While learning Microsoft AI it is always better to start with real-world use case examples to grow in the learning curve. The development of Recommendation System or Classification Tests eg. Dog or Cat and Visual Operation from Video Analysis to Audio Analysis would be quite a firsthand experience with Artificial Intelligence.

Linear Regression is a linear approach that tries to model the relationship of two different variables such that a linear equation is attempted to be fitted into the observed data.

Simple Linear Regression establishes a relationship using a straight line between two variables.

In order to develop the relationship between a dependent variable and two or more explanatory variables, multiple linear regression is used.

Logistic Regression estimates the parameters of logistic model which finds out the probability of binary events calculating its dependent variables such as in cases of victory/ loss, healthy/ sick. In numerous classes of events, it can be used to model images such that it can help choose between different typesof animals in an image. The values of categorical variables are predicted using the logistic regression.

Decision Trees are one of the algorithms for supervised learning where according to specific parameters, the data is split continuously. Just like a tree, it contains branches where each node is in fact a possible outcome.

Support Vector Machine is an algorithm for supervised learning which are extensively used for anomaly detection, regression, and classification.

Naïve Bayes is basically classifiers which are the collection of classification algorithms that in based upon Baye’s Theorem. Bayes’ Theorem explains a method to find out conditional probability. This theorem is named after the 18th-century British Mathematician Thomas Bayes, who discovered this theorem.

To Learn More about Probability, Check out the previous article: Importance Of Probability In Machine Learning And Data Science

Classification and Regression problems can be solved using kNN which a basic supervising learning method.

K — Means is one of the most extensively used clustering techniques for unlabeled data.

Random Forest is an extensively used supervised learning algorithm that merges all the multiple decision trees it builds in order to produce a highly accurate and stable prediction. The figure below shows the process of a random forest with two different trees,

Dimension Reduction like Principal Component Analysis (PCA Algorithms) offers the technique of reducing the dimension of input variables so that the Machine Learning Algorithms can be run in a better-optimized process.

Machine Learning as the name suggests is a method to make the machine learn using data and identification of patterns with the least amount of human intervention.

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