AI for Enterprise Training

Description of the study

Master AI for collaboration with full training from DreamsPlus. Learn AI techniques, technologies and best practices to drive business innovation and transformation. Get industry-recognized certification and boost your career prospects.

 Highlights/Training Highlights

• AI for workplace training points
• Prerequisite: Basic business skills
• Eligibility: Business leaders, IT professionals and entrepreneurs
• Certification body: DreamsPlus
• Validity: 3 years Raised certification status
• Experimental Design: Case Study, Multiple Choic
• Passing score: 85%

Study Studies

Part 1: Fundamentals of data science and AI

Duration: 16 hours

Observations:
This unit covers basic concepts of data science and AI, and provides a solid foundation in programming, data processing, statistics, and machine learning. He explores advanced topics in deep learning, natural language processing (NLP), and data visualization. By the end of this section, students will have a comprehensive understanding of the basics of data science with both theoretical skills and practical applications.

Subject:

The dragon is the one

Introduction to Python
Core Python implementation
Various activities
The data structure
OOPs (Object Oriented Operations) 1.1.

Written by Panda

Introduction to the panda
Data structures in pandas
class
DataFrame is
catch

Accountability

introduction
Theoretical Accounting
Descriptive Statistics

Machine learning

introduction
Supervised teaching
The distribution of the distribution
Back to the back
Unsupervised teaching
Formation of groups
Reduction of theory

A difficult lesson

introduction
Strong teaching methods
ANN (Neural Networks) .
RNN (Neural Regeneration) .
CNN (Cross-Neural Network) .

NLP (Natural Language Processing) .

introduction
NLP techniques
LSTM and GRU
encoder and decoder
Examples of concentration
converter converter

Graphical data visualization

introduction
Matplotlib available
Sea birth

Part 2: Generative AI concepts and tools

Duration: 16 hours

Observations:
This section introduces the concepts, applications, and tools of generative AI, emphasizing a deeper understanding of its design and capabilities. Students explore different types of generative models such as GANs, VAEs, transformers, and their applications for graphics, text, audio, video, and even 3D models. This section discusses ethics and best practices that ensure the responsible use of AI.

Subject:

Module 1: Introduction to Generative AI

Generative AI overview
History and Development
applications and information
Ethics and Concerns

Module 2: Fundamentals of deep learning

Neural networks and architecture
9. Connected Neural Networks (CNNs) .
Regenerative Neural Networks (RNNs) .
Generative Adversarial Networks (GANs) .

Module 3: Image Generation

GANs for images
Fractional auto-encoders (VAEs).
StyleGAN and development GAN
Image-to-image translation

Module 4: Text generation

Language Models (RNNs, Transformers) .
Text generation with GANs and VAEs
Chatbots and conversational AI
NLP for text generation

Module 5: Music and Audio Generation

Music generation with GANs and VAEs
Audio generation with WaveNet and NSynth
Musical transfer
audio processing and conversion control

Module 6: Video Generation

Video generation with GAN and VAEs
Video transfer
Video-to-video translation
video processing and transformation

Module 7: 3D model generation

3D Model Generation with GANs and VAEs
3D model manipulation and applications
3D printing and manufacturing
3D computer visionNLP (Natural Language Processing) .

introduction
NLP techniques
LSTM and GRU
encoder and decoder
Examples of concentration
converter converter

Graphical data visualization

introduction
Matplotlib available
Sea birth

Part 2: Generative AI concepts and tools

Duration: 16 hours

Observations:
This section introduces the concepts, applications, and tools of generative AI, emphasizing a deeper understanding of its design and capabilities. Students explore different types of generative models such as GANs, VAEs, transformers, and their applications for graphics, text, audio, video, and even 3D models. This section discusses ethics and best practices that ensure the responsible use of AI.

Subject:

Module 1: Introduction to Generative AI

Generative AI overview
History and Development
applications and information
Ethics and Concerns

Module 2: Fundamentals of deep learning

Neural networks and architecture
9. Connected Neural Networks (CNNs) .
Regenerative Neural Networks (RNNs) .
Generative Adversarial Networks (GANs) .

Module 3: Image Generation

GANs for images
Fractional auto-encoders (VAEs).
StyleGAN and development GAN
Image-to-image translation

Module 4: Text generation

Language Models (RNNs, Transformers) .
Text generation with GANs and VAEs
Chatbots and conversational AI
NLP for text generation

Module 5: Music and Audio Generation

Music generation with GANs and VAEs
Audio generation with WaveNet and NSynth
Musical transfer
audio processing and conversion control

Module 6: Video Generation

Video generation with GAN and VAEs
Video transfer
Video-to-video translation
video processing and transformation

Module 7: 3D model generation

3D Model Generation with GANs and VAEs
3D model manipulation and applications
3D printing and manufacturing
3D computer vision

Section 3: Implementation of AI on Azure Platform

Duration: 32 hours

Overview:
This section focuses on implementing AI and generative AI solutions on Microsoft Azure. Learners will be guided through planning, deploying, and managing various Azure AI services, including solutions for computer vision, NLP, speech recognition, and document intelligence. In addition, they will learn to create custom models, integrate AI services into CI/CD pipelines, and manage security and costs on the Azure platform.

 Syllabus:

Plan and manage an Azure AI solution (15–20%)

Select the appropriate Azure AI service

•  Select the appropriate service for a computer vision solution
•  Select the appropriate service for a natural language processing solution
•  Select the appropriate service for a speech solution
•  Select the appropriate service for a generative AI solution
•  Select the appropriate service for a document intelligence solution
•  Select the appropriate service for a knowledge mining solution

Plan, create and deploy an Azure AI service

•  Plan for a solution that meets Responsible AI principles
•  Create an Azure AI resource
•  Determine a default endpoint for a service
•  Integrate Azure AI services into a continuous integration and continuous delivery (CI/CD) pipeline
•  Plan and implement a container deployment

Manage, monitor, and secure an Azure AI service

•  Configure diagnostic logging
•  Monitor an Azure AI resource
•  Manage costs for Azure AI services
•  Manage account keys
•  Protect account keys by using Azure Key Vault
•  Manage authentication for an Azure AI Service resource
•  Manage private communications

Implement content moderation solutions (10–15%)

Create solutions for content delivery

•  Implement a text moderation solution with Azure AI Content Safety
•  Implement an image moderation solution with Azure AI Content Safety

Implement computer vision solutions (15–20%)

Analyze images

•  Select visual features to meet image processing requirements
•  Detect objects in images and generate image tags
•  Include image analysis features in an image processing request
•  Interpret image processing responses
•  Extract text from images using Azure AI Vision
•  Convert handwritten text using Azure AI Vision

Implement custom computer vision models by using Azure AI Vision

• Choose between image classification and object detection models
•  Label images
•  Train a custom image model, including image classification and object detection
•  Evaluate custom vision model metrics
•  Publish a custom vision model
•  Consume a custom vision model

Analyze videos

•  Use Azure AI Video Indexer to extract insights from a video or live stream
•  Use Azure AI Vision Spatial Analysis to detect presence and movement of people in video

Implement natural language processing solutions (30–35%)

 
Analyze text by using Azure AI Language

•  Extract key phrases
•  Extract entities
•  Determine sentiment of text
•  Detect the language used in text
•  Detect personally identifiable information (PII) in text

Process speech by using Azure AI Speech

•  Implement text-to-speech
•  Implement speech-to-text
•  Improve text-to-speech by using Speech Synthesis Markup Language (SSML)
•  Implement custom speech solutions
•  Implement intent recognition
•  Implement keyword recognition

Translate language

•  Translate text and documents by using the Azure AI Translator service
•  Implement custom translation, including training, improving, and publishing a custom model
•  Translate speech-to-speech by using the Azure AI Speech service
•  Translate speech-to-text by using the Azure AI Speech service
•  Translate to multiple languages simultaneously

Implement and manage a language understanding model by using Azure AI Language

•  Create intents and add utterances
•  Create entities
•  Train, evaluate, deploy, and test a language understanding model
•  Optimize a language understanding model
•  Consume a language model from a client application
•  Backup and recover language understanding models

Create a custom question-answering solution by using Azure AI Language

•  Create a custom question answering project
•  Add question-and-answer pairs manually
•  Import sources
•  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
•  Create a multi-language question answering solution

Implement knowledge mining and document intelligence solutions (10–15%)

Implement an Azure AI Search solution

•  Provision an Azure AI Search resource
•  Create data sources
•  Create an index
•  Define a skill set
•  Implement custom skills and include them in a skillset
•  Create and run an indexer
•  Query an index, including syntax, sorting, filtering, and wildcards
•  Manage Knowledge Store projections, including file, object, and table projections 

Implement an Azure AI Document Intelligence solution

•  Provision a Document Intelligence resource
•  Use prebuilt models to extract data from documents
•  Implement a custom document intelligence model
•  Train, test, and publish a custom document intelligence model
•  Create a composed document intelligence model
•  Implement a document intelligence model as a custom Azure AI Search skill

Implement generative AI solutions (10–15%)

Use Azure OpenAI Service to generate content

•  Provision an Azure OpenAI Service resource
•  Select and deploy an Azure OpenAI model
•  Submit prompts to generate natural language
•  Submit prompts to generate code
•  Use the DALL-E model to generate images
•  Use Azure OpenAI APIs to submit prompts and receive responses
•  Use large multimodal models in Azure OpenAI

Optimize generative AI

•  Configure parameters to control generative behavior
•  Apply prompt engineering techniques to improve responses
•  Use your own data with an Azure OpenAI model
•  Fine-tune an Azure OpenAI model

Section 4: AI for Enterprise-Based Industry Jobs

AI for Enterprise Career Opportunities

1. AI Strategist
2. Enterprise Architect
3. Data Scientist
4. AI Engineer
5. Business Intelligence Manager
6. Digital Transformation Consultant
7. Innovation Manager
8. AI Ethicist
9. AI Solutions Architect
10. Business Analytics Manager