AI-900 Microsoft Azure AI Fundamentals Training - OEM Certkit

• Language: English
• Audience: The AI-900 Microsoft Azure AI Fundamentals Training is designed for anyone interested in understanding and exploring artificial intelligence (AI) and its application in Microsoft Azure. The training is suitable for IT professionals, developers, data scientists and business professionals who want to understand how AI technologies work and how to deploy them for their projects and organizations.
• Required Knowledge Level:The AI-900 Azure AI Fundamentals Training does not require any specific prior knowledge or experience. It is an entry-level training designed to reach a broad audience, including beginners with no prior knowledge of artificial intelligence (AI) or Microsoft Azure. It is helpful to have a basic understanding of cloud computing concepts and a general interest in AI technologies, but this is not a requirement. The course begins with the basics of AI and Azure AI services.
• Course duration: 16:15 hours(depending on personal speed).
• Course load: Variable due to practical assignments.
• Online access: 24/7, 365 days after activation
• Learning methods: Instructional video, Demonstration video, Knowledge Base
• Contact with instructor by email and chat
• Award Winning E-learning
• Complete training for the official Exam
• User-friendly environment
• Certificate of Participation included

AI-900 Microsoft Azure AI Fundamentals Training

This CertKit introduces fundamentals concepts related to Artificial Intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them.

Prerequisites:
Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services.

• Experience using computers and the internet.
• Interest in use cases for AI applications and machine learning models.

Course outcome:
In this course, you will learn to:

• Describe Artificial Intelligence workloads and considerations.
• Describe fundamental principles of machine learning on Azure.
• Describe features of computer vision workloads on Azure.
• Describe features of Natural Language Processing (NLP) workloads on Azure.
• Describe features of conversational AI workloads on Azure.

Who should attend:
The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don’t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts

Demo AI-900 Microsoft Azure AI Fundamentals Training

CertKit content

E-learning courses

AI-900: Azure AI Fundamentals: : Artificial Intelligence & Machine Learning

Course: 1 Hour, 44 Minutes

• Course Overview
• Artificial Intelligence
• Machine Learning
• Datasets and Data Manipulation
• Labeled vs. Unlabeled Data
• Features in Data
• Machine Learning Using Regression
• Machine Learning Using Classification
• Machine Learning Using Clustering
• Supervised Machine Learning
• Unsupervised Machine Learning
• Reliability and Safety in AI Algorithms and Use
• Transparency in AI Algorithms and Use
• Privacy and Security in Responsible AI
• Inclusiveness in AI Algorithms and Use
• Fairness in AI Algorithms and Use
• Accountability in AI Algorithms and Use
• Course Summary

AI-900: Azure AI Fundamentals: Machine Learning with Azure Services

Course: 1 Hour, 52 Minutes

• Course Overview
• Machine Learning Services Provided by Azure
• Azure Machine Learning Studio
• Signing up for an Azure ML Studio Account
• Inspecting the Azure Machine Learning Features
• Features of the Computer Vision Service
• Features of the Custom Vision Service
• Features of the Face Service
• Features of the Form Recognizer Service
• Identifying Azure ML Workloads
• Compute Resources
• Creating and Managing Datasets in Azure ML Studio
• Managing Pipelines in Azure ML Studio
• Algorithms Used for Model Training
• Azure ML Services Experiments
• Interpreting Evaluation Metrics for Classification
• Interpreting Model Evaluation Metrics for Regression
• Course Summary

AI-900: Azure AI Fundamentals: Using Azure Machine Learning Studio

Course: 1 Hour, 31 Minutes

• Course Overview
• Machine Learning Services Provided by Azure
• Azure Machine Learning Studio
• Signing up for an Azure ML Studio Account
• Inspecting the Azure Machine Learning Features
• Features of the Computer Vision Service
• Features of the Custom Vision Service
• Features of the Face Service
• Features of the Form Recognizer Service
• Identifying Azure ML Workloads
• Compute Resources
• Creating and Managing Datasets in Azure ML Studio
• Managing Pipelines in Azure ML Studio
• Algorithms Used for Model Training
• Azure ML Services Experiments
• Interpreting Evaluation Metrics for Classification
• Interpreting Model Evaluation Metrics for Regression
• Course Summary

AI-900: Azure AI Fundamentals: Authoring with the Azure ML Studio Designer

Course: 1 Hour, 11 Minutes

• Course Overview
• Creating and Using a Dataset in Azure ML Designer
• Selecting Dataset Features in Azure ML Designer
• Adding Data Transformations in Azure ML Designer
• Normalizing and Cleaning Data in Azure ML Designer
• Viewing Normalized & Transformed Data in ML Designer
• Splitting a Dataset for Testing and Evaluation
• Using a Regression Model in Azure ML Designer
• Using a Classification Model in Azure ML Designer
• Using a Clustering Model in Azure ML Designer
• Course Summary

AI-900: Azure AI Fundamentals: Evaluating Models with the ML Designer

Course: 2 Hours, 3 Minutes

• Course Overview
• Adding and Using Scoring on Models
• Model Evaluation Types
• Using Evaluators on the Model
• Running a Pipeline
• Analyzing the Evaluation Results Output and Logs
• Exploring the Evaluation Results Details
• Visualizing the Data in the Scoring Model
• Investigating Results from a Regression Model
• Interpreting Results from a Classification Model
• Investigating Results from a Clustering Model
• Creating an Inference Pipeline
• Adding a Web Service Output
• Deploying a Predictive Service
• Testing a Predictive Service
• Course Summary

AI-900: Azure AI Fundamentals: Anomaly Detection

Course: 1 Hour, 28 Minutes

• Course Overview
• Anomaly Detection
• Challenges of Anomaly Detection
• Azure Anomaly Detector Cognitive Service
• Navigating the Azure Anomaly Detector Service
• Creating an Azure Anomaly Detection Service Project
• Integrating Time Series Data
• Training a Best-fitting Detection Model
• Deploying Anomaly Detection as a Service
• Testing and Evaluating the Detection Service
• Customizing the Anomaly Detection Service
• Course Summary

AI-900: Azure AI Fundamentals: Natural Language Processing

Course: 1 Hour, 6 Minutes

• Course Overview
• Natural Language Processing
• Uses of Natural Language Processing
• Azure NLP Services
• Intents and Entities
• Training a Language Model
• Using the Text Analytics Service
• Azure Translator Service
• Using the Translator Service to Translate Text
• Using the Translator Service to Translate Speech
• Course Summary

AI-900: Azure AI Fundamentals: Creating a Conversational AI Bot

Course: 2 Hours, 2 Minutes

• Course Overview
• Conversational AI Bots
• Uses for a Conversational Bot
• Creating a Knowledgebase Using QnA Maker
• Populating and Training a Knowledgebase
• Publishing and Testing a Knowledgebase
• Creating a Bot for a Knowledgebase
• Extending a Bot Framework
• Testing a Bot
• Configuring and Using Application Insights
• Publishing a Bot
• Connecting the Bots to Standard Channels
• Connecting an Application Directly to the Bot
• Digital and Virtual Assistants
• Creating a Simple Virtual Assistant
• Deploying and Testing a Virtual Assistant
• Course Summary

AI-900: Azure AI Fundamentals: Computer Vision

Course: 1 Hour, 23 Minutes

• Course Overview
• Computer Vision
• Azure Computer Vision Services
• Computer Vision Models
• Classify and Detect Objects
• Analyzing Images Using Computer Vision Service
• Describing and Tagging Images
• Training a Classifier Model
• Evaluating a Classifier Model
• Testing and Using a Model for Prediction
• Training an Object Detection Model
• Evaluating an Object Detection Model
• Using the Model as a Service
• Semantic Segmentation
• Course Summary

AI-900: Azure AI Fundamentals: Face & Optical Character Recognition

Course: 1 Hour, 55 Minutes

• Course Overview
• Face Detection
• Using Computer Vision for Face and Age Detection
• Using Video Indexer to Identify Faces in Videos
• Using the Face Service to Detect and Analyze Faces
• Optical Character Recognition API
• Using Computer Vision to Read Text
• Using Computer Vision to Read Text from Photographs
• Reading Text from Digitized Documents
• Form Recognizer Service
• Read API
• Using the Pre-built Receipt Model
• Using a Custom Form Recognizer Model
• Identifying Specific Fields and Types on a Receipt
• Processing Tables on Forms
• Course Summary

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What is the AI-900 Microsoft Azure AI Fundamentals Training?

The AI-900 Azure AI Fundamentals training is a course focused on providing an introductory understanding of Artificial Intelligence (AI) and Machine Learning (ML) capabilities within the Azure cloud. The training covers topics such as AI-related workloads, Machine Learning techniques, data preparation, AI services in Azure, and other related topics. The goal of this training is to help participants understand how to use Azure to build AI-based solutions and successfully pass the AI-900 exam.

10 benefits of AI-900 Microsoft Azure AI Fundamentals Training

1. Fundamental Knowledge: The training provides a basic understanding of AI and Machine Learning, familiarizing participants with the concepts and terminology.
2. Azure AI Services: The training introduces the various AI services available in Azure, including Cognitive Services, Azure Machine Learning and Azure Bot Service.
3. Application areas: The training explores the various application areas of AI, such as image and speech recognition, natural language processing, predictive analytics and chatbots.
4. Architecture and workloads: The training covers the architecture of AI solutions in Azure and helps in understanding the different AI workloads and their implementation.
5. Data preparation: The training covers the importance of preparing and processing data for training and implementing AI models.
6. Machine Learning Concepts: The training explains the basics of Machine Learning, including algorithms, datasets, training and evaluation of models.
7. Using Azure tools: The training shows how Azure tools such as Azure Notebooks, Azure Databricks and Azure ML Studio can be used to develop and deploy AI solutions.
8. Ethics and Trust: The training discusses ethics and best practices regarding the use of AI and the protection of privacy and data.
9. Integration and scalability: The training emphasizes how AI solutions can be integrated into existing systems and how they can scale to meet the needs of organizations.
10. Preparation for the AI-900 exam: The training is designed to prepare participants to pass the AI-900 exam, which will demonstrate their knowledge and understanding of Azure AI Fundamentals.

Why is AI-900 Microsoft Azure AI Fundamentals Training important?

The AI-900 Microsoft Azure AI Fundamentals training is important because it enables professionals to gain a basic understanding of AI and its application in the Azure platform. It provides insight into the capabilities and applications of AI and lays a foundation for further specialization.

Earning the AI-900 certification enhances career opportunities and enables professionals to add value to organizations seeking to implement AI technologies. It also promotes business growth by improving processes and identifying new opportunities using AI.

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