AI-900 Microsoft Azure AI Fundamentals, Part 1 of 5: AI Workloads and Consideration
with expert Eshant Garg
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Course at a glance
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| Release date | 4/6/2022 | |
| Level | Beginner | |
| Runtime | 1h 48m | |
| Closed captioning | N/A | |
| Transcript | N/A | |
| eBooks / courseware | N/A | |
| Hands-on labs | N/A | |
| Sample code | Included | |
| Exams | Included |
Course description
The AI-900 exam is intended for candidates with both technical and non-technical backgrounds to demonstrate knowledge of Machine Learning and AI on the Azure platform.
This course covers an introduction to the topic as well as the Domain "Describe AI workloads and considerations (15-20%) "
Prerequisites
Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.
Meet the expert
Eshant Garg has 16 years of extensive professional experience with expertise in Database and Business Intelligence Solutions, Advanced Analytics, Design and Solution Architect, Reporting, and Cloud Computing Technologies (Azure & AWS).
As a developer and architect, he has worked closely with customers, users, and colleagues to support business solutions across a variety of industries including healthcare, insurance, finance, and government ranging from small companies to fortune 500 companies.
Course outline
Module 1
Course Introduction (11:06)
- Introduction (00:08)
- Course Overview (01:17)
- Azure Certificate Journey (00:36)
- Certification Walkthrough (02:46)
- Demo: Create Azure Free Subscription (06:10)
- Summary (00:08)
Azure Portal Overview (19:42)
- Introduction (00:08)
- Azure Portal Overview (01:22)
- Demo: Azure Portal (13:19)
- Demo: How to use Azure Portal Free (04:43)
- Summary (00:08)
Learning Objectives (13:01)
- Introduction (00:08)
- Learning Objectives (02:12)
- What is Artificial Intelligence (00:45)
- Define AI (02:27)
- Applications of AI (02:25)
- Prediction and Forecasting (01:54)
- Demo: Cost Analysis (01:08)
- Possible Questions (01:51)
- Summary (00:08)
Anomaly Detection (24:44)
- Introduction (00:08)
- Anomaly Detection (01:51)
- Anomaly Detection Algorithms (01:52)
- Anomaly Detection Questions (00:25)
- Anomaly Detection Answers (03:50)
- Computer Vision (06:00)
- Computer Vision Questions (01:51)
- Natural Processing Language (06:27)
- NPL Attributes (02:09)
- Summary (00:08)
Knowledge Mining (10:48)
- Introduction (00:08)
- Knowledge Mining (00:45)
- Knowledge Mining Steps (01:28)
- Knowledge Mining Takeaways (01:18)
- Conversational AI (06:59)
- Summary (00:08)
Guiding Principles (29:02)
- Introduction (00:08)
- Guiding Principles of responsible AI (06:07)
- Fairness (04:21)
- Reliability and Safety (05:27)
- Privacy and Security (02:26)
- Inclusiveness (03:09)
- Transparency (03:35)
- Accountability (03:36)
- Summary (00:08)