Azure Machine Learning Engineer Associate Boot Camp

DreamsPlus offers a comprehensive Azure Machine Learning Engineer Associate Boot Camp in Chennai and online, tailored to provide hands-on experience and prepare you for the Microsoft certification in machine learning. This program is specifically designed to equip you with the skills needed to excel as an Azure Machine Learning Engineer Associate.

Syllabus 

• Describe Artificial Intelligence workloads and considerations (20–25%)
• Describe fundamental principles of machine learning on Azure (25–30%)
• Describe features of computer vision workloads on Azure (15–20%)
• Describe features of Natural Language Processing (NLP) workloads on Azure (25–30%)

Describe Artificial Intelligence workloads and considerations (20–25%)

Identify features of common AI workloads

•  Define characteristics of workloads for anomaly detection.
• Determine workloads in computer vision.
• Determine the workloads for natural language processing
• Determine the workloads for knowledge mining

Identify guiding principles for responsible AI

•  Explain how an AI solution should take fairness into account.
•  Explain how an AI system should take safety and dependability into account.
•  Describe how an AI solution takes security and privacy into account
•  Explain how an AI solution should take inclusivity into account.
•  Discuss the factors that should be transparent in an AI solution.
•  Explain how an AI solution should take accountability into account.

 Describe fundamental principles of machine learning on Azure (25–30%)

Identify common machine learning types

•  Determine machine learning scenarios for regression.
•  Define machine learning scenarios for classification.
•  Determine which machine learning scenarios to cluster.

Describe core machine learning concepts

• Recognize characteristics and annotations in a dataset for machine learning
• Explain how machine learning uses training and validation datasets.
• Explain the features of Azure Machine Learning Studio’s visual tools.
•  Machine learning that is automated
•  Azure Machine Learning Designer

Describe features of computer vision workloads on Azure (15–20%)

Identify common types of computer vision solution

• Define characteristics of image classification algorithms.
•  List the characteristics of object detecting programs.
• List the characteristics of optical character recognition programs.
•  List the characteristics of facial analysis and detecting software.

Identify Azure tools and services for computer vision tasks

• Determine the Computer Vision service’s capabilities
• List the features that the Custom Vision service offers.
• Determine what features the Face service offers.
• Figure out the form recognition service’s capabilities.

Describe features of Natural Language Processing (NLP) workloads on Azure (25–30%)

Identify features of common NLP workload scenarios

• Define characteristics and applications of key phrase extraction.
• Determine entity recognition features and applications.
• Define sentiment analysis’s characteristics and applications.
•  Determine the features and applications of language modeling; • Determine the features and applications of voice recognition and synthesis;
•  Describe the functions and attributes of translation.

Identify Azure tools and services for NLP workloads

• Determine the language service’s capabilities.
• Establish the speech service’s capabilities.
•  Determine the translator service’s capabilities.

Identify considerations for conversational AI solutions on Azure

• List the functions and attributes of bots.
• List the features of the Azure Bot service and Power Virtual Agents.