AI-900 Course Azure AI Fundamentals with Virtual Simulations

We really hope you'll agree, this training is way more than the average course on Udemy!

Have access to the following:

• Training from an instructor of over 25 years who has trained thousands of people and also a Microsoft Certified Trainer

• Lecture that explains the concepts in an easy to learn method for someone that is just starting out with this material

• Instructor led hands on and simulations to practice that can be followed even if you have little to no experience

TOPICS COVERED INCLUDING HANDS ON LECTURE AND PRACTICE TUTORIALS:

Introduction

• Welcome to the course

• IMPORTANT Using Assignments in the course

• Creating a free Azure Account

• Order of concepts covered in the course

• Introduction to artificial intelligence terminology

Identify features of common AI workloads

• Understanding features of anomaly detection workloads

• Example of univariate anomaly detection

• Example of multivariate anomaly detection

• What is computer vision workloads?

• Conceptual usage of natural language processing workloads

• Visualizing knowledge mining principals

Identify guiding principles for responsible AI

• Introduction to responsible AI

• Fairness and Inclusiveness in an AI solution

• Reliability and safety in an AI solution

• Privacy and security in an AI solution

• Transparency in an AI solution

• Accountability in an AI solution

Identify common machine learning types

• Create an Azure Machine Learning workspace for machine learning scenarios

• What is regression machine learning?

• Building a pipeline with regression machine learning for cleaning a dataset

• Implement a regression machine learning scenario

• Evaluating the results of regression machine learning scenarios

• What is classification machine learning?

• Implement a classification machine learning scenario in Azure

• Understanding labels on a confusion matrix

• Clustering machine learning example

Describe core machine learning concepts

• Understanding features and labels in a dataset for machine learning

• How training and validation datasets are used in machine learning

Describe capabilities of visual tools in Azure Machine Learning Studio

• Using Automated machine learning

• Understanding Azure Machine Learning Designer

• Cleaning up our existing Azure resources

Identify common types of computer vision solutions

• What are the Azure computer vision solutions?

• Creating an Azure computer vision resource

• Image classification and object detection solutions in vision studio

• Optical character recognition solutions in vision studio

• Facial detection and facial analysis solutions in vision studio

• Spatial analysis solutions in vision studio

Identify Azure tools and services for computer vision tasks

• Using the POSTMAN tool for interacting with Azure AI Services

• Implementing the capabilities of the Computer Vision service

• Implementing the capabilities of the Custom Vision service

• Implementing the capabilities of the Face service

• Implementing the capabilities of the Form Recognizer service

Identify features of common NLP Workload Scenarios

• What are the Azure AI Language features?

• Creating a language service resource in Azure

• Trying out key phrase extraction

• Trying out key entity recognition

• Trying out key sentiment analysis

• Trying out key language modeling

• Trying out key speech recognition and synthesis

• Trying out key translation

Identify Azure tools and services for NLP workloads

• Exploring the capabilities of the Language service

• Exploring the capabilities of the Speech service

• Exploring the capabilities of the Translator service

• Configuring Azure AI language to support questions and answers support

Identify considerations for conversational AI solutions on Azure

• Understanding the features and uses for bots

• Capabilities of Power Virtual Agents and the Azure Bot service

• Remove existing resource

Identify features and capabilities of generative AI & the Azure Open AI Service

• Features of generative Open AI models

• Common scenarios for generative Open AI

• Responsible Open AI considerations for generative AI