Interactive Explanations and Courses
- Machine Learning 101 – A comprehensive overview of AI and machine learning with numerous resources for additional research.
- Intro to Machine Learning – A detailed, video-based, interactive course into ML concepts. Prerequisites include strong algebra skills as well as proficiency in programming basics, including Python, using Tensor Flow.
- Making Sense of Artificial Intelligence – This A-Z guide offers a series of simple, bite-sized explainers to help anyone understand what AI is, how it works and how it’s changing the world around us.
- new google resource
Udacity, Intro to Artificial Intelligence – A detailed course on the basic concepts of AI.
University of Helsinki, Elements of Artificial Intelligence – Free online course in 6 parts, six weeks based on5 hours per week, but can be skimmed for specific areas and interests.
Blog, Brandon Rohrer, Data Science and Robots – A series of posts and videos exploring a detailed breakout of topics about how Machine Learning works, reviewed types of ML, uses, and an overview of Artificial Intelligence applications
Trailhead, Artificial Intelligence Basics – Two fifteen minute courses on the fundamentals and applications of Artificial Intelligence.
Harvard University, ICML Tutorial – Slide show demonstrating types of Machine Learning, interpretability, and model selection process. Highly Technical.
Machine Learning Mastery, A Tour of Machine Learning Algorithms – A discussion about the various types of algorithms used in Machine Learning.
Medium, Machine Learning for Humans – An extensive, five-course lesson on Machine Learning
- Part 1: Why Machine Learning Matters
- Part 2.1: Supervised Learning
- Part 2.2: Supervised Learning II
- Part 2.3: Supervised Learning III
- Part 3: Unsupervised Learning
- Part 4: Neural Networks & Deep Learning
- Part 5: Reinforcement Learning
- Appendix: The Best Machine Learning Resources
3Blue1Brown, But What *Is* A Neural Network? – A three-part video series explaining deep learning, gradient descent, and backpropagation.
R2D3, A Visual Introduction to Machine Learning – Demo of how to apply various statistical methodologies to differentiate homes in NYC v. SF.
Microsoft, Professional Program for Artificial Intelligence – Ten extensive, 8-16 hour online courses, ranging from a broad overview of Artificial Intelligence to instructions on how to code for machine learning.
Google, AI Experiments – A showcase for simple experiments that make it easier for anyone to start exploring machine learning, through pictures, drawings, language, music, and more.
FAT/CV, Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision at CVPR 2020 – workshop examining the ethical implications of deploying this technology.
Fierce Electronics, What is Artificial Intelligence (AI)? – provides a history of AI, the future of AI, along with several educational resources.
UC Berkeley: Center for Long-Term Cybersecurity, ML Fairness Mini-Bootcamp: Learning to Identify Algorithmic Bias – a series of Python labs designed to train folks to identify, discuss, and address the risks posed by machine learning algorithms.
Analytics India Mag, Top Resources to Learn About Federated Learning – A list of resources to help kickstart an understanding of federated learning.
