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Azure I Machine Learning I Cognitive Services Course I Training

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What will you learn?

Learn about Azure Machine Learning.

Discover which machine learning algorithms Azure Machine Learning supports.

Learn how to design and carry out a machine-learning experiment using datasets from the real world.

Learn how to use machine learning algorithms for classification.

Learn how to use machine learning techniques for regression.

Learn how to create an API or web service for the Azure ML machine learning project.

Learn how to integrate a web application with the Azure ML machine learning experiment API.

What is Azure?

Microsoft Azure, sometimes known as Azure, is a cloud computing platform that Microsoft manages for the purpose of managing programs through data centers under its control.

Applications as a service (SaaS), platform as a service (PaaS), and infrastructure as a service are just a few of Microsoft Azure's features (IaaS). It also supports a wide range of Microsoft and third-party software and systems, as well as programming languages, tools, and frameworks.

What is machine learning?

Machine learning (ML) is a field of study that focuses on comprehending and creating "learning" processes, or processes that employ data to enhance performance on various tasks.

Without being expressly programmed to do so, machine learning algorithms build a model from sample data, also known as training data, to generate predictions or judgments.
Machine learning algorithms are used in various fields, such as medicine, email filtering, voice control, agriculture, and machine vision, where it would be challenging or impossible to design traditional algorithms to complete the necessary tasks.

Machine learning, however, is not always statistical learning. Computational statistics, which concentrates on making predictions with computers, is tightly related to a subset of machine learning, however not all machine learning includes statistical knowledge.

Machine learning is supported by mathematical optimization research in the areas of technique, theory, and application domains. Data mining is a similar field of study that focuses on unsupervised learning for exploratory data analysis.

Some machine learning techniques mimic how a biological brain operates by using data and neural networks.

When applied to business concerns, machine learning is commonly referred to as predictive analytics.

What is Azure Machine Learning?

A cloud service called Azure Machine Learning organizes and expedites the machine learning project lifecycle. Engineers, data scientists, and machine learning experts can use it in their daily workloads: MLOps management, model deployment, and training

You have the option of creating your own models in Azure Machine Learning or using one created using an open-source framework like Pytorch, TensorFlow, or sci-kit-learn. The products from MLOps help you monitor, retrain, and redeploy models.

Microsoft's Azure Machine Learning cloud service is used for creating, maintaining, and deploying machine learning projects. It is sometimes referred to as Azure Machine Learning Service and goes by the acronym AML. It is a tool for working together that addresses the daily and operational challenges that Data Scientists, Machine Learning Engineers, and Data Analysts face. from creating and honing prediction models to putting machine learning solutions into use and keeping an eye on them.

What are Cognitive Services?

A Microsoft machine learning service called Azure Cognitive Services gives developers access to AI. A collection of machine learning algorithms created by Microsoft can be used to address a variety of artificial intelligence challenges.

Microsoft developed Cognitive Services, a suite of machine learning techniques, to address challenges in the artificial intelligence field (AI). The goal of Cognitive Services is to democratize AI by separating it into separate parts that programmers can include in their own projects.

What are the key benefits of Azure Machine Learning?

Providing machine learning as a service

Any service that offers machine learning (ML) solutions as a component of its cloud computing solutions is referred to as offering machine learning as a service (MLaaS). Because of its dependability, affordability, and ability to manage infrastructure concerns including data preparation, model training, and evaluation, SMBs prefer MLaaS.

The Microsoft Azure public cloud platform is connected with the well-known MLaaS provider Azure ML. It differs from other MLaaS providers, nevertheless, because of its unique features.

complete ML support with access to hundreds of ML methods

ML is usable at all skill levels.

Azure Machine Learning APIs

Data pre-processing is made simple with sample modules.

Azure ML has the advantage of being a pay-as-you-go service.

Additionally, Azure ML makes it possible to develop apps without the use of sophisticated hardware or software. Azure will take care of your machine learning applications' deployment and maintenance; you only need to subscribe to and use what you need.

Allow remote access to cloud-based services.
 

Enterprises must implement technologies that provide whenever anywhere access from any device if they want employees to have access to company information in this era of remote and hybrid working. Azure ML services may help businesses promote flexible work schedules, streamline remote working, and give employees access to company information and reports from anywhere. Solutions created with Azure ML may offer stakeholders an interactive view of the crucial business information on any device and location through attractive data visualizations.

How much does a practitioner of Azure Machine Learning make?

Azure Machine Learning's total yearly compensation is anticipated to be $181,438.

Who can take this course?

Data science enthusiasts

Software and IT developers

Statisticians

Professionals in the cloud

Developers of software

Startup CEOs from both technical and non-technical fields.

 

Course Syllabus

Getting Started 11 lectures 17 mins
Introduction Preview 01:42
Welcome guide document 10 Pages
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Welcome guide document 10 Pages
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