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Azure Data Scientist Associate Certification with an Extra Boost Course.

Empower Your Cloud Journey with Azure Excellence.

Dynamic Online Learning with Virejetech Certified Instructors.
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"Advance your career with our holistic App Development Program, meticulously designed to cultivate the skills of a modern app developer. This sought-after course covers key modules, including App Development Foundations and Techniques, providing extensive exposure to diverse domains. Empower yourself with cutting-edge tools for Visualization and Insights, ensuring a well-rounded skill set in the dynamic field of app development."

25 hrs

Learning content

Azure Data Scientist Associate

Module 1:Introduction to Azure Machine Learning (AML)

2 Quizzes

1 Project

1.1 Overview of Azure Machine Learning
Introduce Azure DevOps, a comprehensive set of development tools and services by Microsoft, encompassing features for source code management, continuous integration
1.2 Azure Machine Learning Workspaces
Azure DevOps is a comprehensive set of services and components, including Azure Repos, Pipelines, Boards, Test Plans, Artifacts, and Extensions, designed to support end-to-end software development and delivery processes.
1.3 Azure Machine Learning Services and CLI
To configure security for data at rest, implement encryption mechanisms such as Transparent Data Encryption (TDE) in Azure SQL Database, ensuring that sensitive data stored on disk is encrypted

Module 2:Data Preprocessing and Data Wrangling

2.1 Data Exploration and Visualization
Understanding DevOps culture involves fostering a collaborative and communicative environment that encourages seamless collaboration between development and operations teams
2.2 Data Preprocessing Techniques
Planning for DevOps transformation involves defining clear goals, assessing current processes, identifying bottlenecks, and creating a roadmap for adopting DevOps practices
2.3 Data Transformation and Feature Engineering
Assessing your DevOps maturity involves evaluating your organization's current state in terms of collaboration, automation, and continuous delivery practices.

Module 3:Machine Learning with Azure ML

3.1 Supervised vs. Unsupervised Learning
Introduction to source control involves understanding the importance of managing and versioning source code using systems like Git or centralized version control systems.
3.2 Model Training and Evaluation
Azure Repos is a version control service in Azure DevOps that provides Git repositories for source code management. It allows teams to collaborate on software development by hosting and versioning code

Module 4:Building and Training Machine Learning Models

4.1 Choosing the Right Algorithm
Git workflows, such as Centralized, Feature Branch, Gitflow, GitHub Flow, and Forking, guide collaborative development and version control strategies, catering to different team sizes and project complexities.
4.2 Model Training with Azure ML
Implementing and managing pull requests involves creating a mechanism for code review, collaboration, and integration of changes in a Git repository
4.3 Model Evaluation and Selection
Exercise: Scaling Git for Enterprise DevOps involves hands-on activities to address challenges associated with large-scale Git repositories, multiple teams, and complex development workflows

Module 5:Automated Machine Learning (AutoML)

5.1 Introduction to AutoML
Azure Pipelines is a continuous integration and continuous delivery (CI/CD) service provided by Azure DevOps
5.2 Automated Feature Engineering
Implementing and managing YAML pipelines in Azure DevOps involves defining build and release pipelines using YAML syntax.
5.3 AutoML Experimentation and Optimization
Implementing and managing GitHub Actions involves defining workflows using YAML files directly within a GitHub repository. GitHub Actions automate various tasks, including building
6.1 Deploying Machine Learning Models in Azure
Azure Pipelines is a continuous integration and continuous delivery (CI/CD) service provided by Azure DevOps
6.2 Monitoring and Managing Deployed Models
Exercise: Scaling Git for Enterprise DevOps involves hands-on activities to address challenges associated with large-scale Git repositories, multiple teams, and complex development workflows
6.3 Scaling and Updating Models
To configure security for data at rest, implement encryption mechanisms such as Transparent Data Encryption (TDE) in Azure SQL Database, ensuring that sensitive data stored on disk is encrypted

Module 7:Responsible AI

7.1 Fairness and Bias in Machine Learning
Introduction to CI/CD (Continuous Integration/Continuous Delivery) involves adopting practices that automate and streamline the software development lifecycle
7.2 Interpreting Machine Learning Models
Implementing CI/CD pipelines involves creating automated workflows that integrate and deliver code changes seamlessly from development to production.
7.3 Explainable AI (XAI) in Azure
Managing application configuration and secrets in CI/CD involves securely handling sensitive information, such as API keys or connection strings, during the deployment process
8.1 Introduction to MLOps
Exercise: Implementing CI/CD involves hands-on tasks to set up and configure a CI/CD pipeline for a specific application or project.
8.2 Building CI/CD Pipelines for ML
Introduction to CI/CD (Continuous Integration/Continuous Delivery) involves adopting practices that automate and streamline the software development lifecycle

Module 9:Working with Datasets and Datastores

9.1 Datasets and Datastores in Azure ML
Infrastructure as Code (IaC) principles involve treating infrastructure configurations as code, emphasizing declarative syntax, idempotency, version control, reusability, automatione
9.2 Data Ingestion and Management
Implementing ARM (Azure Resource Manager) templates involves using JSON-based scripts to define and deploy Azure infrastructure resources. ARM templates provide a declarative way to specify the desired state of resources
9.3 Data Versioning and Data Monitoring
Infrastructure provisioning with Terraform involves using HashiCorp's Terraform tool to define and manage infrastructure as code

Module 10:Azure Cognitive Services Integration

10.1 Integrating Azure Cognitive Services with ML
Configuration management automates and maintains consistent infrastructure and software configurations, ensuring reliability, version control, and idempotent application of desired states.
10.2 Using Cognitive Services for NLP and Computer Vision
Azure Key Vault is a cloud service that enables secure storage and management of sensitive information such as secrets, encryption keys, and certificates.
10.3 Building AI-powered Applications
Exercise: Managing application configuration and secrets involves practical tasks to secure and handle sensitive information within an application using tools like Azure Key Vault