About this course
Build a conceptual starting point for artificial intelligence with David Carmona’s AI 101 series. The short whiteboard lessons introduce machine learning and classification through accessible explanations. This is an orientation course for understanding the ideas before moving into a longer programming or mathematics curriculum.
What you can learn
- Understand the purpose of machine learning
- Explore how a model learns from examples
- Recognize a classification problem
Course at a glance
| Provider | Microsoft |
|---|---|
| Platform | YouTube · Microsoft Developer |
| Access | Free Video |
| Level | Beginner — WikiFree audience assessment |
| Language | English |
| Selected lessons | 4 |
| Video duration | 34m · allow extra time for reflection and practice |
| Details checked | 9 October 2026 |
Try this learning exercise
Write three everyday questions and decide which could be framed as classification tasks. For one example, define the input, possible labels, and a situation in which the model should not make a confident prediction.
How to follow the course
Choose a lesson from the list or search for a specific topic. Use Previous and Next to continue, and mark lessons complete as you go. Progress is saved in this browser without a WikiFree account.
Free access, tools and certificates
The selected public YouTube videos are free to watch. No paid subscription, payment card, examination, or software purchase is required for this video learning path. This listing does not include a Microsoft certification, assessed assignment, or instructor feedback.
The learning exercise above can be completed as a written plan without provisioning services. Reproducing demonstrations with Azure, hosted models, enterprise products, hardware, or optional software may require an account, separate access, or paid resources. Those activities are not required to complete the video course. Recorded interfaces and commands may differ from current versions.
Official Microsoft Developer playlist source ↗
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Professional
Compare course focus, level and study time
| Course | Level | Focus | Study time |
|---|---|---|---|
| This course — Free AI Fundamentals Course: Machine Learning Concepts | Beginner | Understand the purpose of machine learning · Explore how a model learns from examples · Recognize a classification problem | 34m of video; practice time is additional |
| Google Introduction to Machine Learning: Free Beginner Course | Beginner | Google Introduction to Machine Learning: Free Beginner Course | 20 minutes (Google estimate); self-paced. |
| Free Machine Learning Course: Learn the Fundamentals | Beginner | Classical machine learning · Regression · Python notebooks | 1h 9m of video; practice time is additional |
| Machine Learning Crash Course | Intermediate | Machine learning fundamentals · Model evaluation · Data preparation | Self-paced modular course; prerequisites and practice time vary by learner. |
| Free MIT Course: Machine Vision and Image Understanding | Professional | Lecture 2: Image Formation, Perspective Projection, Time Derivative, Motion Field · Lecture 7: Gradient Space, Reflectance Map, Image Irradiance Equation, Gnomonic Projection · Lecture 12: Blob Analysis, Binary Image Processing, Green's Theorem, Derivative and Integral | 32h 52m of video; practice time is additional |
Provider: Microsoft. Videos play from the Microsoft Developer channel on YouTube.
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