Here’s a list of some of the better videos I’ve stumbled across over the past couple of years. They range from forward-looking glimpses into the future, to software tutorials. I’d love to grow this list, so if you have a favorite video you don’t see listed here please add it in the comments section below.
Note: Depending on the speed of your Internet connection, these may take a while to load.
Jeremy Howard: The Data Science Revolution – From the Exponential Finance series of videos. Howard covers the history of data science, starting with a checkers-playing playing program from the 1950’s.
Ron Bekkerman: Machine Learning: The Basics – Bekkerman claims not to be an expert on Machine Learning, but does a great job explaining the essentials of Classification, Clustering, and Regression.
MathematicalMonk: K-means Clustering, Part 1 – A simple, effective explanation of one of the most-used (and misused) statistical concepts.
Quant Concepts: The Easiest Introduction to Regression Analysis – A humorous dive into the math of linear regression.
Dr. Will Hakes: Big Data Analytics: The Revolution has Just Begun – Good talk about how analytics and Big Data are changing business. The first 6:20 he talks about his company, and from 6:20 to 9:30 is a promotional video. After that is the real meat of the presentation.
Geoffrey Hinton: Brains, Sex, and Machine Learning – Neural networks applied to some interesting biological problems, including sexual reproduction.
Kenneth Cukier: Big Data is Better Data – Great high-level view on the usefulness of large amounts of data. One of the most popular videos regarding data on YouTube.
Martin Fowler: Introduction to NoSQL – SQL has ruled the database roost for a long time, but its rigidity can make it the wrong choice for some uses. Fowler discusses Not Only SQL (NoSQL) databases and why you might want to use one.
El Chief: RapidMiner Extract, Transform, and Load – An excellent tutorial on how to combine dissimilar datasets into one using RapidMiner. A bit dated but still relevant.
Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning – How a computer can figure out whether a picture is of a motorcycle.