Artificial Intelligence and Machine Learning Resources

Interactive Explanations and Courses


  • Machine Learning 101A 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

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.

Author: Carla

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