Machine Learning is a domain of computers that 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 is 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 the 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 the initial stage is very difficult to learn subject practically. In such cases, there are the 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 the 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. The major competitor of Shogun is Mlpack, released in the 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 include 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 data scientists, mathematicians, and statisticians have developed. It went in coordination with Hadoop for a long time. The recent versions have increased support for the Spark framework, with improved support for the ViennaCL library.
4. Accord .NET Framework.
It is a 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.
5. Tensor flow
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 the largest community, as well as most projects.