examples of machine learning projects

a machine learning course at Stanford University. Take more course notes, complete course assignments as much as possible, and ask more questions. Read some books: This refers not to textbooks, but to the books listed above for beginners of programmers. Master a tool: Learn to use an analysis tool or class library, such as the python Machine

definitely not the result we expected. After discussing the "brush list", we will discuss the effect of "hoarding goods". The electric Dealer's various creation festival has created batch after batch of Chop hand party, these chopping hands often advance to buy goods in advance into the shopping cart, may have mother and child, may have men's, may have children's books, or literature, social science, and so on, and so on the day of the list. If you use these orders to calculate the association

Turn: Http://www.infoq.com/cn/news/2014/04/learn-open-source Some suggestions for learning open source projectsAuthor Ben Linders released on April 11, 2014 | Note: GTLC Global Technology Leadership Summit, 500+CTO technical leadership redefined! 1 Discussions Share to: Weibo facebooktwitter Youdao Cloud Note email sharing Read later My list of reading With the development of open source communities and

wallpaper, a live wallpaper example featuring selectable backgrounds and a fingerprint. Current Android applications: Skylight1 TDC-A Test Data Capture Application for Android phones-Available here for download Skylight1 game the initial app, a game for Android phones called "BalanceThe beer "-available on the market. NYC events-an app that lists NYC parks, art and music events.-availableOn the market Toast-an app that helps you make a toast with your phone! -Available onMarket Projec

. Comprehensive Classification The historical origins, knowledge representation, reasoning strategies, similarity of results evaluation, the relative concentration of researchers, and application fields of various learning methods are comprehensively considered. Machine learning methods are divided into the following six categories: 1) Empirical inductive

Reprint Please specify source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectory machine Learning Cornerstone Note When you can use machine learning (1) Machine learning Cornerstone Note 2--When you can use

In the introduction of recommendation system, we give the general framework of recommendation system. Obviously, the recommendation method is the most core and key part of the whole recommendation system, which determines the performance of the recommended system to a large extent. At present, the main recommended methods include: Based on content recommendation, collaborative filtering recommendation, recommendation based on association rules, based on utility recommendation, based on knowledge

Earlier, we mentioned supervised learning, which corresponds to non-supervised learning in machine learning. The problem with unsupervised learning is that in untagged data, you try to find a hidden structure. Because the examples

http://sourceforge.net/projects/numpy/files/download the corresponding version of the NumPy, everywhere, find a not python2.7Use Pip, please.Pip Install NumPyDownload finished, the hint does not install C + +, meaning is also to install VS2008, but installed is VS2012, had to download a VC for Pythonhttp://www.microsoft.com/en-us/download/confirmation.aspx?id=44266Re-pip, wait for the most of the day, the final count is successfulInput command introdu

Bayesian Introduction Bayesian learning Method characteristic Bayes rule maximum hypothesis example basic probability formula table Machine learning learning speed is not fast enough, but hope to learn more down-to-earth. After all, although it is it but more biased in mathematics, so to learn the rigorous and thoroug

-ported unix-like operating system that is the most current portable platform operating system and can be executed on many platforms, from 64bit Alpha servers to handheld devices and embedded devices. The slogan for the NetBSD program is: "Of course it runs NetBSD". Its simple design, code specification, and many advanced features make it highly acclaimed in the industry and academia. Due to its concise design and advanced features, it has excellent performance in both production and research, a

plots and other rich media.3) support for interactive data visualization and use of GUI toolkits.4) Flexible, embeddable interpreters to load into your own projects.5) Easy-to-use, high-performance tools for parallel computing. "IPython is a python interactive shell that works much better and more powerful than the default Python shell. She supports syntax highlighting, auto-completion, code debugging, object introspection, support for Bash shel

framework (orch), and Julia does not exist. Which language is the most popular programming language? The answer should be clear. Python, Java, and R are the most popular skills when it comes to machine learning and data science. If you want to focus on deep learning instead of general machine

Java libraries and projects that address the problems of the child domain from machine learning, called Natural Language Processing (NLP). Natural language processing is not my domain, so I just point out the key libraries. Opennlp:apache OPENNLP (HTTP://OPENNLP. apache.org/) is a toolkit for handling natural language texts, which provides methods for natural la

We have developed a false news detector using machine learning and natural language processing, which has an accuracy rate of more than 95% on the validation set. In the real world, the accuracy rate should be lower than 95%, especially with the passage of time, the way the creation of false news will change. Because of the rapid development of natural language processing and

http://blog.jobbole.com/67621/This article by Bole Online-xiaoxiaoli translation. without permission, no reprint!English Source: Jason Brownlee. Welcome to join the translation team.There are many ways to learn machine learning, and most people choose to start with the theory.If you're a programmer, you've mastered the ability to split the problem into components and prototype small

, the above classification idea is the idea of SVM. Can be expressed as: SVM is trying to find a super plane to split the sample, the sample in the positive and inverse examples with the super-plane, but not very perfunctory simple separation, but do the best to make the interval between the positive and inverse of the largest margin. In this way, the results of the classification are more credible, and for the unknown new samples have a good classifi

. It is used to organize clusters of large computers. The second application is the analysis of social networks. There is also market segmentation. Many companies have large databases that store consumer information. So, you can retrieve these customer data sets, automatically discover the market classification, and automatically divide the customer into different market segments so that you can automatically and effectively sell or sell together in different segments of the market. Finally, uns

is that only the input paradigm is provided for this network, and it automatically identifies its potential class rules from those examples. When the study is complete and tested, it can also be applied to new cases. A typical example of unsupervised learning is clustering. The purpose of clustering is to bring together things that are similar, and we do not care what this class is. Therefore, a cluste

Deep Learning SpecializationWunda recently launched a series of courses on deep learning in Coursera with Deeplearning.ai, which is more practical compared to the previous machine learning course. The operating language also has MATLAB changed to Python to be more fit to the current trend. A study note on this series o