Machine learning and data mining online resources collecting conscience recommend

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Front I've posted recommended online some "in image processing and computer vision have blog material resources, or address

Image processing and machine vision acquisition -- go launch cyber source


The research and development of machine learning and data mining, often online search some resources, accumulate over a long period they dug a bunch more cattle bloggers, especially: do a lot of this direction, cattle are also many, but most of these resources mainly emphasizes pragmatism, relevant bloggers will not necessarily be in the field of Mount Rushmore (at least for the most part are not school professor), but their space really material, can learn a lot. Continue to update, but I only keep the most mighty wave crashing on a sandy shore, worthy of recommendation.

1, first of all, is theThe data storehomepage


Because I personally use R to do data mining and analysis, so the content of the home page is very to my appetite. This home page has a lot of content with R to do data mining. I have to steal a lot of bloggers. Although the home pagebacteriumHas stopped updating the content, but the existing part (in fact, a lot of articles) have been able to make a lot of later learned.

2,JerryLeadIn the blog on the home page


If you want to understand the principle of data mining algorithm, and it is from the mathematical aspects do "knows its however knows why the depth of understanding, this blog (and blog below) should be a must see if you. This home page is characterized by a detailed mathematical derivation of many algorithms. Bloggers should be in the Chinese Academy of Sciences, the Chinese Academy of Sciences, although the blog seems to have stopped for a long time, but many of the classic things in fact never outdated. SVM, EM and other series of articles introduced in place, in particular, recommended.

3,PluskidHome page

Http:// Page_id=683?

The blogger is the master of Zhejiang University, seemingly later went to the United States should read bo. This blog with JerryLead is very similar to a large number of mathematical derivation, so that you understand and understand the nature of a lot of obscure data mining algorithm. A lot of people on the network respected July on the CSDN, especially the SVM three state. However, the three level of July but also JerryLead and pluskid left a right section of the graft of it. At the beginning of this article I look at the time, it was found that the content of the image, especially the pluskid drawing of the basic original model is the original model in the July article. Later in the know almost read a post, almost also understand the inside of the matter. In short, I hope you can respect the original bar. July's blog, you can see, after all, the ultra million visits, he East in search of the West together continue to organize no credit also have Gulao, everybody can be in as a collection.

4,Dragon heart dust & small YangHome page


Data mining and machine learning blog in the new force, read a few articles, feel good strength. Neural network and deep learning part of the content is recommended.

Other public resources

R, Weka, Python and Matlab are used to do data mining tool (or even SPSS, STATA, SAS can also be used to complete a number of data mining tasks). As a result, the fact that the software or the language of the public home page or forum also contains a large number of good content (including some of the program code and application examples).

In the end, there are a lot of open classes about machine learning and data mining. If you want to learn one point one point system, then you should not miss these resources. I mainly recommend two:

One is Standford's open class.machine learningBy Andrew Ng, speaker. I believe that EM's JerryLead blog is a reference.Ng AndrewTeaching content. This course is taught in English, the domestic web site is also equipped with Chinese subtitles, if you have perseverance and determination, then eating this course is a very good choice. Netease open class to visit the country to learn, the address is as follows


If you still feel uncomfortable listening to English, then the Chinese Coursera course (also known as MOOC), which is recorded by National Taiwan University professor Lin Xuantian, is a great resource for machine learning. The course is divided into two parts, for beginners to learn -"Machine learning foundation"curriculum

Http:// Cid=938?

Listen to the name, and you can know that the course is based on the above. If you want to learn advanced content (of course, the prerequisite is the foundation of knowledge you have completely mastered), then you can choose Professor Lin also a MOOC courses --"Machine learning techniques"curriculum

Http:// Cid=1664?

Finally, thanks to the above resource providers selfless dedication. Also sincerely hope that readers learn, learn something!

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