Lecture 1 | Machine Learning (Stanford)

I'd suggest going back to the original youtube[1] and course materials[2]. Coursera version is nothing but a hand-wavy watered down "feel good" version of the original class. I also really like the Caltech's take "Learning from data"[3] [1] [VIDEO] [2] [LINK] [3] [LINK]

Start leaning from the guy right now: [VIDEO] Stanford class on machine learning by Andrew Ng, 20 lectures.

I second the nomination of Bishop. It is the standard text. It is only two years old, and Bishop will teach you machine learning the way that the field practices it nowadays. In my lab of fourteen people, we must have six or so copies of Bishop. I don't understand what is impractical about Bishop. If you are looking blindly to use an off-the-shelf machine learning implementation, that's one thing. Machine Learning has been described as the study of bias. If you want to understand when to pick certain techniques, and develop appropriate biases, then read Bishop. "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman gives more of a statistician's approach. The treatment is simply less broad, and also more dated. You can also look at Andrew Ng's video lectures: [VIDEO] He is very well-respected in the field. For certain students, watching a lecture may be preferable to reading a book.

Build a better chess rating system and enter your system into the following competition: [LINK] . You may want to use machine learning techniques, which you can learn using the Andrew Ng's Stanford lectures ( [VIDEO] ).

This reminds me of CS229, of which there's an online version: [VIDEO] It's considerable more focused in scope, presenting the mathematics behind some of the more popular algorithms extensively used in machine learning, which is a subset of artificial intelligence. The course starts off pretty slow, but quickly gains speed and momentum. By the end, you should be fairly comfortable with clustering and classification/regression, among other topics. The lecture notes are also fantastic.

Thanks for posting the other two course. I can't wait till October. The Machine Learning course has a great a playlist on youtube as well. [VIDEO]

One caveat though: the Coursera ML course is CS229A(pplied), which focuses on applying machine learning, rather than the mathematics behind it. The "real" CS229 lectures are on YouTube [1], and go much more in depth. 1. [VIDEO]

Nope, it's still around on youtube and on the Stanford Engineering Everywhere site. The coursera version of the class is much more introductory and skips significant parts of the full Stanford version. [VIDEO] [LINK]