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 | Graph Theory with Applications to Engineering & Computer Science |
| 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. |
| 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 | --- |
| Oliver Theobald | Machine Learning For Absolute Beginners: A Plain English Introduction | This is an absolute beginners ML guide.No mathematical background is needed, nor coding experience — this is the most basic introduction to the topic for anyone interested in machine learning.“Plain” language is highly valued here to prevent beginners from being overwhelmed by technical jargon. Clear, accessible explanations and visual examples accompany the various algorithms to make sure things are easy to follow. |
| John Paul Mueller and Luca Massaron | Machine Learning For Dummies | This book aims to get readers familiar with the basic concepts and theories of machine learning and how it applies to the real world. And "Dummies" here refers to absolute beginners with no technical background.The book introduces a little coding in Python and R used to teach machines to find patterns and analyze results. From those small tasks and patterns, we can extrapolate how machine learning is useful in daily lives through web searches, internet ads, email filters, fraud detection, and so on. With this book, you can take a small step into the realm of machine learning and we can learn some basic coding in Pyton and R (if interested) |
| John D. Kelleher, Brian Mac Namee and Aoife D'Arcy | Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) | This book covers all the fundamentals of machine learning, diving into the theory of the subject and using practical applications, working examples, and case studies to drive the knowledge home. |
| Peter Harrington | Machine Learning in Action | (Source: https://github.com/kerasking/book-1/blob/master/ML%20Machine%20Learning%20in%20Action.pdf) This book acts as a guide to walk newcomers through the techniques needed for machine learning as well as the concepts behind the practices. |
| Richard S. Sutton and Andrew G. Barto | Reinforcement Learning: An Introduction | Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. |
| Jeff Heaton | Artificial Intelligence for Humans | This book helps its readers get an overview and understanding of AI algorithms. It is meant to teach AI for those who don’t have an extensive mathematical background. The readers need to have only a basic knowledge of computer programming and college algebra. |
| Chandra S.S.V | Artificial Intelligence and Machine Learning | This book is primarily intended for undergraduate and postgraduate students of computer science and engineering. This textbook covers the gap between the difficult contexts of Artificial Intelligence and Machine Learning. It provides the most number of case studies and worked-out examples. In addition to Artificial Intelligence and Machine Learning, it also covers various types of learning like reinforced, supervised, unsupervised and statistical learning. It features well-explained algorithms and pseudo-codes for each topic which makes this book very useful for students. |
| Tom Taulli | Artificial Intelligence Basics: A Non-Technical Introduction | This book equips you with a fundamental grasp of Artificial Intelligence and its impact. It provides a non-technical introduction to important concepts such as Machine Learning, Deep Learning, Natural Language Processing, Robotics and more. Further the author expands on the questions surrounding the future impact of AI on aspects that include societal trends, ethics, governments, company structures and daily life. |
| --- | Reinforcement Learning: An Introduction | --- |
| --- | Reinforcement Learning | --- |
| --- | Machine Learning | --- |
| --- | Understanding Machine Learning From Theory to Algorithms | --- |
| --- | Machine Learning Yearning | --- |
| --- | A Course in Machine Learning | --- |
| --- | Machine Learning | --- |
| --- | Neural Networks and Deep Learning | --- |
| --- | Deep Learning Book | --- |