I am collecting material for teaching AI-related issues to non-tech people. The links should provide for a general understanding of AI without going too deep into technical issues. Please contribute!
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Make this Issue your First Issue I am collecting material for teaching AI-related issues to non-tech people. The links should have provide for a general understanding of AI without going too deep into technical issues. Please contribute!
Please only Resources with NO CODE
| Link to Issue | Description |
|---|---|
| AI4All | AI 4 All is a resource for AI facilitators to bring AI to scholars and students |
| Elements of AI | Elements of AI is a free open online course to teach AI principles |
| Teachable Machine | Use Teachable Machine to train a computer to recognize your own images, sounds, & poses |
| Crash course for AI | This is a fun video series that introduces students and educators to Artificial Intelligence and also offers additional more advanced videos. Learn about the basics, neural networks, algorithms, and more. |
| Indonesian Machine Learning Tutorial | Turorial Teachable Machine to train a computer for beginner |
| Youtuber Channel Machine Learning Tutorial | Youtube Channel Turorial Teachable Machine for beginner |
| Machine Learning Crash Course | A Machine Learning crash course using Tensorflow APIs by Google |
| eCraft2Learn | Resource and interactive space (Snap, a visual programming environment like Scratch) to learn how to create AI programs |
| LIAI | A detailed introduction to AI and neural networks |
| Layman's Intro | A layman's introduction to AI |
| The Non-Technical AI Guide | One of the good blog post that could help AI more understandable for people without technical background |
| Artificial Intelligence (AI) | ULearn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems |
| AI For Everyone by Andrew Ng | AI For Everyone is a course especially for people from a non-technical background to understand AI strategies |
| Author | Book | Description & Notes |
|---|---|---|
| Ethem Alpaydin | Machine Learning: The New AI | --- |
| Ian Goodfellow and Yoshua Bengio and Aaron Courville | Deep Learning | The book starts with a discussion on machine learning basics, including the applied mathematics and algorithms needed to effectively study deep learning from an academic perspective. There is no code covered in the book, making it perfect for a non-technical AI enthusiast. |
| Peter Harrington | Machine Learning in Action | (Source: https://github.com/kerasking/book-1/blob/master/ML%20Machine%20Learning%20in%20Action.pdf) |
| Stuart Russel & Peter Norvig | Artificial Intelligence: A Modern Approach, 3rd Edition | This is the prescribed text book for my Introduction to AI university course. It starts off explaining all the basics and definitions of what AI is, before launching into agents, algorithms, and how to apply them. Russel is from the University of California at Berkeley. Norvig is from Google. |
| Shai Shalev-Shwartz and Shai Ben-David | Understanding Machine Learning From Theory to Algorithms | --- |
| Richard S. Sutton and Andrew G. Barto | Reinforcement Learning: An Introduction | --- |
| --- | Reinforcement Learning: An Introduction | --- |
| --- | Reinforcement Learning | --- |
| --- | Machine Learning | --- |
| --- | Understanding Machine Learning From Theory to Algorithms | --- |
| Andrew Ng | Machine Learning Yearning | AI, machine learning, and deep learning are transforming numerous industries. But building a machine learning system requires that you make practical decisions |
| --- | A Course in Machine Learning | --- |
| --- | Machine Learning | --- |
| --- | Neural Networks and Deep Learning | --- |
| --- | Deep Learning Book | --- |