Pedro Domingos also has a fantastic mooc at https://www.coursera.org/course/machlearning
This is a very useful guide. Although I'd imagine that you'd have to have atleast some experience with ML before you truly appreciate what's being explained in the paper.
This is great, wish this was around about 6 months ago. I'm going to go over this and fill in some knowledge gaps.
Is anyone else getting an untrusted cert?
"So there is ultimately no replacement for the smarts you put into feature-engineering."
Recently, deep learning changed this. Finding the right network architecture allows the net to learn the features by itself.
Just finished reading this. Brilliant!
When I first read this paper, I found it to be immensely helpful. I'm new to Machine Learning but I was so inspired by this paper when I first found it that I wanted to build up resources around it.
Here's the result: https://github.com/hangtwenty/dive-into-machine-learning
I want this guide to be a good resource for other people like me, who are curious to get into Machine Learning by this process:
1) hacking 2) coming to understand what you hacked 3) more structured, in-depth learning
It can be intimidating to approach Machine Learning this way. For a long time it felt like I couldn't do steps 1 or 2... and had to start with 3. That's intimidating!
Pull requests welcome, I want this to be a good resource! Thanks all.