Predictive Analytics, Data Mining, Self-service, Open source - RapidMiner. Open for Innovation. Java Machine Learning. Are you a Java programmer and looking to get started or practice machine learning?
Writing programs that make use of machine learning is the best way to learn machine learning. You can write the algorithms yourself from scratch, but you can make a lot more progress if you leverage an existing open source library. In this post you will discover the major platforms and open source machine learning libraries you can use in Java. Environments This section describes Java-based environments or workbenches that can be used for machine learning. Weka Waikato Environment for Knowledge Analysis (Weka) is a machine learning platform developed by the University of Waikato, New Zealand. Weka Explorer Interface with the Iris dataset loaded The Konstanz Information Miner (KIME) is an analytics and reporting platform developed by Konstanz University, Germany.
RapidMiner RapidMiner used to be called Yet Another Learning Environment (YALE) and was developed at Technical University of Dortmund, Germany. Java-ML. Octave for Microsoft Windows - Octave. GNU Octave is primarily developed on GNU/Linux and other POSIX conform systems.
The ports of GNU Octave to Microsoft Windows use different approaches to get most of the original Octave and adapt it to Microsoft Windows idiosyncrasies (e.g. dynamic libraries, file paths, permissions, environment variables, GUI system, etc). Bear this in mind and don't panic if you get unexpected results. There are a lot of suggestions on the mailing lists for tuning your Octave installation. GNU Octave standalone ports for Windows are independently compiled using either the MinGW or Microsoft Visual Studio development environments. Note: GNU Octave 3.8.2 is the current stable release. About older version numbers (versions older than 3.8 don't come with a graphical user interface): The 3.6.x are previous releases.
MXE Builds Untouched MXE builds for the current 3.8.x release can be found here. However, these are unofficial builds! The plot command also doesn't work well in case of Windows 8 system. Start Here - Machine Learning Mastery. What is the Weka Machine Learning Workbench - Machine Learning Mastery. Machine learning is an iterative process rather than a linear process that requires each step to be revisited as more is learned about the problem under investigation.
This iterative process can require using many different tools, programs and scripts for each process. A machine learning workbench is a platform or environment that supports and facilitates a range of machine learning activities reducing or removing the need for multiple tools. Some statistical and machine learning work benches like R provide very advanced tools but require a lot of manual configuration in the form of scripts and programming. The tools can also be fragile, written by and for academics rather than written to be robust and used in production environments.
Best Machine Learning Resources for Getting Started. This was a really hard post to write because I want it to be really valuable.
I sat down with a blank page and asked the really hard question of what are the very best libraries, courses, papers and books I would recommend to an absolute beginner in the field of Machine Learning. I really agonised over what to include and what to exclude. I had to work hard to put my self in the shoes of a programmer and beginner at machine learning and think about what resources would best benefit them.
4 Self-Study Machine Learning Projects. There are many paths into the field of machine learning and most start with theory.
If you are a programmer then you already have the skills to decompose problems into their constituent parts and to prototype small projects in order to learn new technologies, libraries and methods. Basic final project submission. Hi, I've completed the basic final project.
The architecture is the same used for the examples in the course: Storm retrieves tweet from Twitter using Twitter4J, then makes some computation on the data and publishes them to Redis; the webapp reads from Redis the data and displays them on the page. This is the topology I used: As you can see, it's very similar to the ones used during the course for counting top hashtags; the main difference is that we then use the top hashtags to filter the tweets to display. Let's see them in detail: ParseTweetBolt: receives every tweet emitted by the TweetSpout, parses them to find hashtags and emits each of them (if any). Bing Maps: Create a Bing Maps Key. Try Bing Maps for 90 days The free 90-day Trial Key allows you to evaluate Bing Maps for development of any type of application, including enterprise applications.
You can use the Trial Key for up to 10,000 billable transactions within any 30-day span during the evaluation period. If your production applications will qualify for limited free use, you can go directly to a Basic Key. Trial Key application types and parameters Public Website Private Website Public Windows App Private Windows App Public Windows Phone App Private Windows Phone App Other Public Mobile App Education Not-for-profit Business asset management Maximum 10,000 cumulative billable transactions in any 30-day period. Expires in 90 days. Get a Basic Key.