Finally acquired the statement of accomplishment for Machine Learning on Coursera by Andrew Ng. Actually it took me more than 2 years to make it since the first opening of the class in the year of 2012. I don’t wanna make any excuse for such procrastination but, as others would do, I used to have some difficulty sparing the time for the class solely. Yeah, shame on me but I finished the course with a record of 100%. 🙂
Anyway, I’m personally proud of what I’ve done for the last couple months. And deep appreciation to Andrew Ng for this encouraging class.
Statement of accomplishment: Coursera ml 2014
i just found a blog on computer vision and machine learning, and got a chance to have a fundamental and clear understanding of the curse of dimensionality.
the posting above provides a very thorough and fundamental explanation about the essence of the curse of dimensionality. the other postings on that blog also seem to be definitely must-read articles for ML newbies, just like me. 🙂
it would be really worth reading the whole materials on that blog, and that’s what i’m gonna do for the next week.
I happened to find these undergraduate ML course video clips opened on youtube by UBC. I think I really have to thank to UBC & the professor Nando de Freitas for this valuable sharing. Anyone with strong interest or something in ML should definitely be better to take a look through the whole sessions. That’s what I’m gonna do for months now.
Here’s a community-driven and educational guidance with current focus on machine learning and probabilistic AI – Metacademy. It will make a great assistance for those with strong interest and enthusiasm in machine learning, just like me. 🙂 It draws an intuitive map of sequential steps towards the concept you queried about. You’ll see once you give it a try.
Machine Learning Subreddit with News, Research Papers, Videos, Lectures, Softwares and Discussions on:
- Machine Learning
- Data Mining
- Information Retrieval
- Predictive Statistics
- Learning Theory
- Search Engines
- Pattern Recognition
(quoted from Machine Learning Subreddit page)
Machine Learning and Probabilistic Graphical Models Course provided by Department of Computer Science and Engineering, University at Buffalo.