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.

## What is Microsoft AI?

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,

*Cognitive Services*The tools are based on the human sense, for instance, computer vision tries to mimic and copy the functionality of the human eye. Similarly, different listening capabilities of humans are made a reality through Natural Language Processing. Smart devices today able to listen and translate sound such as – the Intelligent bot services.*Personalized Models*The developers are enabled to make their own models without using the rest APIs. Azure Machine Learning Services provides automated machine learning model services.*Drag and Drop*The Drap and Drop functionality is accessed with the Designer in Azure. You can easily build an intelligent system with just drag and drop features. Inside this, we can use a notebook or import your code in your favorite IDE to execute our commands.

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.

## How do we get started with Microsoft AI?

Microsoft AI’s products and services go beyond just AI Platforms, Intelligent Applications, Autonomous Systems, and AI partners. All of these products and services make it really easy to work with AI, build models and run them. Howsoever, the surficial knowledge would be of no value without a proper foundation. Thus, it is necessary not just about algorithms. One needs to become strongly foundationally on Mathematics and then learn about how the prediction works. Learn Calculus and Linear Algebra and the other mathematics for AI. To learn about these, there is a series of articles just on Mathematics for you. Check the series of Articles on Mathematics for Artificial Intelligence and Data Science.

## Dataset on Azure

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.

**Credit Fraud Detection Dataset**
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.

## Use Cases of AI

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.

## Basic Algorithms

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

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

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

**Naive Bayes**
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.

**K- Nearest Neighbours(kNN)**
Classification and Regression problems can be solved using kNN which a basic supervising learning method.

**K-Means**
K – Means is one of the most extensively used clustering techniques for unlabeled data.

**Random Forest**
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,

**Dimensionality Reduction Algorithms**
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.

**Microsoft AI Trial/ Demo program**

Microsoft Azure provides over 25+ free services which include various conditional free features for Machine Learning and AI on Computer Vision e.g., 5000 Transaction in S1 tier, Translator – e.g., 2 million characters, Anomaly Detector for 20,000 transactions, and numerous more.

**Machine Learning**
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.
Watch this video of AMA on Microsoft AI by Microsoft MVP Eve Pardi to learn in detail.

**Deep learning**
Deep Learning is a subset of Machine Learning which contains networks that have the ability of unsupervised learning from unlabeled and unstructured data. Itslayman's term tries to function or mimic the process of the human's brain working from neurons to taking in data from the input layer, hidden layers in the middle, and the output layer such that each layer is a level deeper than the other thus, the name.

**Machine Learning Models in Azure**
Azure has a Machine Learning Studio which can perform all necessary tasks for production-grade Machine Learning from Data selection to Model Building and analysis for higher accuracy of the system.

The Model is trained, scored, and evaluated and with the experimental results, different algorithms are used to provide better results with re-iterated operations.

With the results obtained, the confusion matrix helps us understand the metrics of the result.

**Confusion Matrix**
In most supervised learnings, the visualization of the performance of the algorithms is done using a confusion matrix which displays the error matrix based on statistical classification. It gives value to the level of confusion in the data analysis.

## Conclusion

Thus, in this article, we learned about the fundamentals of Microsoft Azure AI and explored the process to model and train our datasets in Azure. We learned about numerous Algorithms like Regression, SVM, Decision Trees. Dimension Reduction and more. We took a brief overview of Machine Learning, Deep Learning, and its support on Microsoft Azure.

**Source**: Paper.li

The Tech Platform

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