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!
Kindly use only those Resources with NO CODE
| Link to Issue | Description |
|---|---|
| Top Trending Technologies | Youtube Channel to master top trending technologyies including artificial intelligence |
| 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 |
| Visual Introduction to Machine Learning | Visual introduction to Machine Learning is a beautiful website that gives a comprehensive introduction and easily understood first encounter with machine learning |
| 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. |
| 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 |
| How far is too far? The age of AI | This is a Youtube Orignals series by Robert Downey |
| Fundamentals of Artificial Intelligence | This course is for absolute beginners with no technical knowledge. |
| Bandit Algorithm (Online Machine Learning) | No requirement of technical knowledge and a basic understending of Probability theory would help |
| AI and Machine Learning: A Nontechnical Overview | AI and Machine Learning: A Nontechnical Overview from OREILLY themselves is a guide to learn anyone everything they need to know about AI, focussed on non-tech people |
| An Executive's Guide to AI | This is an interactive guide to teaching business professionals how they might employ artificial intelligence in their business |
| What business leaders need to know about artifical intelligence | Short article that summarizes the essential aspects of AI that business leaders need to understand |
| AI Business School | Series of videos that teach how AI may be incorporated in various business industries |
| How Will No-Code Impact the Future of Conversational AI | A humble explanation to the current state of converstational AI i.e.Chatbots and how it coul evolve with the current trend of no coding. |
| Artificial Intelligence Tutorial for Beginners | This video will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples. |
| Indonesian Machine Learning Tutorial | Turorial Teachable Machine to train a computer for beginner |
| Indonesian Youtube Playlist AI Tutorial | Youtube Playlist AI Tutorial For Beginner |
| Artificial Intelligence Search Methods For Problem Solving By Prof. Deepak Khemani | These video lectures are for absolute beginners with no technical knowledge |
| Author | Book | Description & Notes |
|---|---|---|
| Ethem Alpaydin | Machine Learning: The New AI | Graph Theory with Applications to Engineering & Computer Science. A concise overview of machine learning—computer programs that learn from data—which underlies applications that include recommendation systems, face recognition, and driverless cars. |
| Charu C. Aggarwal | Neural Networks and Deep Learning | This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. |
| Hal Daumé III | A Course in Machine Learning | The purpose of this book is to provide a gentle and pedagogically organized introduction to the field. A second goal of this book is to provide a view of machine learning that focuses on ideas and models, not on math. |
| 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) This book acts as a guide to walk newcomers through the techniques needed for machine learning as well as the concepts behind the practices. |
| 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. |
| 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. |
| Deepak Khemani | [A First Course in Artificial Intelligence] | It is an introductory course on Artificial Intelligence, a knowledge-based approach using agents all across and detailed, well-structured algorithms with proofs. This book mainly follows a bottom-up approach exploring the basic strategies needed problem-solving on the intelligence part. |
| Maxim Lapan | Deep Reinforcement Learning Hands-On - Second Edition | Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. |
| Tom M Mitchell | Machine Learning | This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning. |
| 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) |
| Andrew NG | Machine Learning Yearning | This book is focused on how to make ML algorithms work. Discusses various strategies to choose the right one among many possible directions. |
| Michael Nielsen | Neural Networks and Deep Learning | Introduction to the core principles of Neural Networks and Deep Learning in AI |
| Simon Rogers and Mark Girolami | A Course in Machine Learning | A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC. |
| 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. |
| 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. |
| Alex Smola and S.V.N. Vishwanathan | Introduction to Machine Learning | Provides the reader with an overview of the vast applications of ML, including some basic tools of statistics and probability theory. Also includes discussions on sophisticated ideas and concepts. |
| Shai Shalev-Shwartz and Shai Ben-David | Understanding Machine Learning From Theory to Algorithms | The primary goal of this book is to provide a rigorous, yet easy to follow, introduction to the main concepts underlying machine learning. |
| 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. |
| 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. |
| 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. |
| Cornelius Weber, Mark Elshaw, N. Michael Mayer | Reinforcement Learning | Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. |