Machine Learning is a domain of computer which deals with designing products and software. Further, these are capable to perform certain tasks which are beyond things that were instructed to the current product and software. Also, there are some free machine learning software for use.
Initially, we construct/program machines to do fixed tasks. Therefore on these machines, we provide input data and acquire output data for a particular purpose. Hence, Products/Software made using Machine Learning is entirely different from conventional techniques.
In Machine Learning applications, we provide input data and output data. Then we design this machine to identify the relationship between input and output data so that the machine, using the relation technique can find output of coming input data based on the capability to find relations between inputs and outputs.
But when we talk about knowing about Machine Learning, we deal with data sets, learning techniques, classification and regression techniques, decision tree learning, etc which at initial stage is very difficult to learn the subject practically. In such cases, there are following free machine learning software we can use for practically learning the subject -:
Shogun was first released in 1999. The first version was developed using C++ programming language. Further versions are compatible with C#, Java, Python, Octave, R, Matlab and Lua. So, the current version is 6.0.0 which is compatible with Windows and Scala. Major competitor of Shogun is Mlpack, released in year 2011 which is also written in C++.
Scikit-Learn consists of a set of Python libraries using which packages like NumPy, Matplotlib and Scipy are built for science and math works. The libraries form either includes in software or interactive platforms for applications. It is completely reusable and open.
3. Apache Mahout
Apache Mahout consists of a set of independent algorithms which the data scientists, mathematicians and statisticians have developed. It went in coordination with Hadoop for a long time. The recent versions have increased support for Spark framework, with improved support for the ViennaCL library.
4. Accord.Net Framework.
It is framework based on machine learning and signal processing focused on .net extensions. For image streams and audio signal processing, various libraries are provided. For tracing moving objects, pinning images together and face detection, we can utilize various algorithms based on vision. Various libraries are also provided for machine learning functions which are especially more conventional ranging from neural networks to decision-tree systems.
It is an open source library compatible with python for ease of use. The introduction section provides machine learning sections for beginners as well as for professionals. It has some experimental APIs in Java and Go. On GitHub, it is the leading open source machine learning tool. It has largest community, as well as the most projects.