Pass the AI Fundamentals certification
This repository is part of a course will give you everything you need to pass the Azure AI Fundamentals (AI-900) certification. This is the foundational certification for doing Machine Learning and Data Science using Azure and its specialized services. The course will go into every content that is relevant to pass the certification including how to create a study guide and strategy to pass the exam.
This course includes and covers all of my study notes so that you can use them in addition to your own notes.
- Get Started with AI
- Visual Tools for ML
- Computer Vision
- Natural Language Processing
- Decision Support
This course contains a complete walkthrough over all of the content relevant for the AI-900 certification with a summary and specialized notes after each sections, following the same content that is available as Learning Paths. Aside from the video content, create your own study notes and strategy using the Study notes for the Azure AI Fundamentals Certification
Start by creating your own study strategy. This repository has my own notes but you should create yours as part of studying for the course. I show you how I did it below π
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Download the skills outline
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Go through the 5 Learning Paths:
- Get Started with AI
- Explore Visual Tools for ML
- Explore Computer Vision
- Explore Natural Language Processing
- Explore Decision Support
Next, go through each Learning Path and create a markdown file for each that includes every learn module.
- Summarize
Sample repo structure:
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βββ 1-get-started-with-ai.md
βββ 2-visual-tools-for-ml.md
βββ 3-computer-vision.md
βββ 4-natural-language-processing.md
βββ 5-decission-support.md
βββ README.md
- Create a repository and add the markdown files, one for each section
- Start summarizing, adding the Learning Path and Learning Module for each section
- Capture services, definitions, and any key differentiators
Sample section:
## Main topic
### Service Name
Definition: Some definition
Features:
* Feature 1
* Feature 2
Find key differences for similar services. For example, IoT Hub vs. IoT Central:
IoT Hub: More control, less managed (no dashboard). Telemetry analysis.
IoT Central: Less control, more managed (dashboard included). Telemetry analysis.
If you are looking for more resources, here are some that I found helpful: