Machine learning made its way in almost all the lists of IT predictions for the year 2017. While these predictions point to a good future for machine learning enthusiasts, it also poses a question of what to learn. This blog will help you narrow down your choices in terms of what you need to learn and why.
The Programming Language
Just like Software development, a machine learning library would also require you to be aware of a programming language. Almost all popular programming languages have their set of machine learning libraries. Java is undoubtedly the most popular programming language and therefore learning to code for machine learning using Java will always have prospects for you.
Open-Source or Closed-Source
Another factor that would come into consideration is whether to choose an open-source library or a closed-source one. Each of these have a benefit. But, while learning, it is always better to opt for an open-source library because of the availability of resources. These resources facilitate learning and a community presence helps you network with professionals and maybe get a mentor.
Eventually, based on the discussion above regarding Java and open-source, here is a list of machine learning libraries that in my opinion stand out from the others.
MLib by Apache Spark: The reason that this is on the top in my list is because it is compatible with popular programming languages besides Java. These include Scala, Python, etc. It is an open-source library with plethora of resources. It is loaded with algorithms and therefore is fast and easy to deploy. Download the MLib and read the user guide from http://spark.apache.org/mllib/.
Weka 3: This is the most trusted machine learning library for Java. Other than having a pool of algorithms, this allows you to customize codes and create your own. Weka 3 claims to be the perfect solution for processing, classifying, clustering, etc. You can check out more information about Weka 3 from http://www.cs.waikato.ac.nz/ml/weka/.
Massive Online Analysis (MOA): Another great machine library with a user-friendly interface. The massive in name of this library suggests big things and the library would not disappoint you when you start processing your batch of big data. Apt for regression, clustering, classifying data, you can check out more about this library from http://moa.cms.waikato.ac.nz/.
ELKI: The small form of the big name Environment for Developing KDD-Applications Supported by Index-Structures is exactly what you need for big data. Like all other libraries mentioned here, this is open-source Java supported version. Lets you do everything that all other libraries listed above are doing. Check out more about this library at https://elki-project.github.io/.
The machine learning libraries listed here are just a few examples. If you think none of these suit you, there are plenty of other libraries that can fit your need. The basic requirement of algorithms and data mining techniques remain same. What really differs is the interface and the working style. If your preferred library is not in the list, let us know in the comments section below.
Joseph Macwan – A Technical writer with a keen interest in business, technology and marketing topics. He is also associated with Aegis softwares which offers Java Application Development Services.
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