AI Engineer Associate

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About AI

 When machines are made to think and work like humans, they come under Artificial Intelligence (AI). It is where the future is, it makes everything possible. Interesting but scary to start isn’t it? But with us, it will be easy, because you do not require any technical expertise to take up this course.

Achieve the key components for your AI journey

1. Computational Systems

2. Data Management

3. AI algorithms (code)

Explore use cases, applications, and concepts surrounding AI. Learn to leverage machine learning, deep learning, and neural networks and get hands-on experience with the best industry experts. Demonstrate your learned skills through action ⚔️, with our expert advice, and create your very own mini-project!

What does AI offer to you?

Apart from a bright career opportunity, it gives you a chance to work on cutting-edge technology which impacts every major sector. Large data is easily processed through the use of AI, making it easy to take the appropriate action for any work.

 According to the Bureau of Labour Statistics, over the next decade, AI-related jobs are projected to grow two times fast than other jobs. So it’s definitely the right time to reskill or upskill yourself!


Domain 1: Plan and manage an Azure Cognitive Services solution

Module 1: Select the appropriate Cognitive Services resource

• select the appropriate cognitive service for a vision solution

• select the appropriate cognitive service for a language analysis solution

• select the appropriate cognitive Service for a decision support solution

• select the appropriate cognitive service for a speech solution

Module 2: Plan and configure security for a Cognitive Services solution

• manage Cognitive Services account keys

• manage authentication for a resource

• secure Cognitive Services by using Azure Virtual Network

• plan for a solution that meets responsible AI principles Create a Cognitive Services resource

• create a Cognitive Services resource

• configure diagnostic logging for a Cognitive Services resource

• manage Cognitive Services costs

• monitor a cognitive service

• implement a privacy policy in Cognitive Services

Module 3: Plan and implement Cognitive Services containers

• identify when to deploy to a container

• containerize Cognitive Services (including Computer Vision API, Face API, Languages, Speech, and Form Recognizer)

• deploy Cognitive Services Containers in Microsoft Azure

 

Domain 2: Implement Computer Vision solutions

Module 1: Analyze images by using the Computer Vision API

• retrieve image descriptions and tags by using the Computer Vision API

• identify landmarks and celebrities by using the Computer Vision API

• detect brands in images by using the Computer Vision API

• moderate content in images by using the Computer Vision API

• generate thumbnails by using the Computer Vision API

Module 2: Extract text from images

• extract text from images or PDFs by using the Computer Vision service 

• extract information using pre-built models in Form Recognizer

• build and optimize a custom model for Form Recognizer

Module 3: Extract facial information from images

• detect faces in an image by using the Face API

• recognize faces in an image by using the Face API

• analyze facial attributes by using the Face API

• match similar faces by using the Face API

Module 4: Implement image classification by using the Custom Vision service

• label images by using the Computer Vision Portal

• train a custom image classification model in the Custom Vision Portal

• train a custom image classification model by using the SDK

• manage model iterations

• evaluate classification model metrics

• publish a trained iteration of a model

• export a model in an appropriate format for a specific target

• consume a classification model from a client application

• deploy image classification custom models to containers

Module 5: Implement an object detection solution by using the Custom Vision service

• label images with bounding boxes by using the Computer Vision Portal

• train a custom object detection model by using the Custom Vision Portal

• train a custom object detection model by using the SDK

• manage model iterations

• evaluate object detection model metrics

• publish a trained iteration of a model

• consume an object detection model from a client application

• deploy custom object detection models to containers

Module 6: Analyze video by using Azure Video Analyzer for Media (formerly Video Indexer)

• process a video

• extract insights from a video

• moderate content in a video

• customize the Brands model used by Video Indexer

 • customize the Language model used by Video Indexer by using the Custom Speech service

• customize the Person model used by Video Indexer

• extract insights from a live stream of video data

Domain 3: Implement natural language processing solutions

Module 1: Analyze text by using the Language service

• retrieve and process key phrases

• retrieve and process entity information (people, places, urls, etc.)

• retrieve and process sentiment

• detect the language used in text

Module 2: Manage speech by using the Speech service

• implement text-to-speech

• customize text-to-speech

• implement speech-to-text

• improve speech-to-text accuracy

• improve text-to-speech accuracy

• implement intent recognition

Module 3: Translate language

• translate text by using the Translator service

• translate speech-to-speech by using the Speech service

• translate speech-to-text by using the Speech service

Module 4: Build a initial language model by using Language Understanding Service (LUIS)

• create intents and entities based on a schema, and add utterances

• create complex hierarchical entities

• use this instead of roles

• train and deploy a model

Module 5: Iterate on and optimize a language model by using Language Understanding

• implement phrase lists

• implement a model as a feature (i.e. prebuilt entities)

• manage punctuation and diacritics

• implement active learning

• monitor and correct data imbalances

 • implement patterns

Module 6: Manage a Language Understanding model

• manage collaborators

• manage versioning

• publish a model through the portal or in a container

• export a LUIS package

• deploy a LUIS package to a container

• integrate Bot Framework (LUDown) to run outside of the LUIS portal

Module 7: Create a Questions Answering solution using the Language service

• create a question answering project

• import questions and answers

• train and test a knowledge base

• publish a knowledge base

• create a multi-turn conversation

• add alternate phrasing

• add chit-chat to a knowledge base

• export a knowledge base

• add active learning to a knowledge base

 

Domain 4: Implement knowledge mining solutions

Module 1: Implement a Cognitive Search solution

• create data sources

• define an index

• create and run an indexer

• query an index

• configure an index to support autocomplete and autosuggest

• boost results based on relevance

• implement synonyms

Module 2: Implement an enrichment pipeline

• attach a Cognitive Services account to a skillset

• select and include built-in skills for documents

 • implement custom skills and include them in a skillset

Module 3: Implement a knowledge store

• define file projections

• define object projections

• define table projections

• query projections

Module 4: Manage a Cognitive Search solution

• provision Cognitive Search

• configure security for Cognitive Search

• configure scalability for Cognitive Search

Module 5: Manage indexing

• manage re-indexing

• rebuild indexes

• schedule indexing

• monitor indexing

• implement incremental indexing

• manage concurrency

• push data to an index

• troubleshoot indexing for a pipeline

Domain 5: Implement conversational AI solutions

Module 1: Design and implement conversation flow

• design conversation logic for a bot

• create and evaluate *.chat file conversations by using the Bot Framework Emulator

• choose an appropriate conversational model for a bot, including activity handlers and dialogs

Module 2: Create a bot by using the Bot Framework SDK

• use the Bot Framework SDK to create a bot from a template

• implement activity handlers and dialogs

• use Turn Context

• test a bot using the Bot Framework Emulator

• deploy a bot to Azure

Module 3: Create a bot by using the Bot Framework Composer

 • implement dialogs

• maintain state

• implement logging for a bot conversation

• implement prompts for user input

• troubleshoot a conversational bot

• test a bot

• publish a bot

• add language generation for a response

• design and implement adaptive cards

Module 4: Integrate Cognitive Services into a bot

• integrate a question answering model

• integrate a LUIS service

• integrate a Speech service resource