Top Java Machine Learning Libraries and Tools

Top Java Machine Learning Libraries and Tools

Java is very powerful language . Although in Data Science , People prefer Python and R than Java . Still in some scenario Java is preferable and much better than other programming stuffs in data science.Good news is , Now java has so many machine learning and deep learning modules . Here is the list of Top java machine learning Libraries –

Top Java Machine Learning Libraries and Tools-


Every body is aware of deep learning and its advantages  over machine learning learning . If not please refer the article – Difference Between Deep Learning and Machine Learning .Although both have different significance and importance .Deeplearning4j is a java framework for deep learning which distributed , commercial and open-source . It has capability for making connection with big data framework like Apache spark and hadoop . Distributive framework is very much important for machine learning and deeplearning stuffs . Actually both involves too much computation .


Java Machine Learning Libraries – DeepLearning4j

2. Weka-

Weka has large scale collection for machine learning algorithms . The best part is weka provides an GUI environment to run machine learning algorithm on your data set  Along with it , It also provide java libraries for the same . Good news is Weka s open source under GNU license .Weka is mature machine learning libraries and tool hence you will get multiple tutorials and documentation for Weka over the Internet . It has a good community support which is very useful in case you stuck somewhere .

Java Machine Learning Libraries - Weka
Java Machine Learning Libraries – Weka

3. Massive Online Analysis (MOA)-

Massive online Analysis is mostly used for real time data processing . Its architecture make it more time  and memory efficient .Like others Java Machine Learning Libraries it also provide classification , regression , clustering and other machine learning stuffs . Specially it is for streaming data processing .

Java Machine Learning Libraries - MOA
Java Machine Learning Libraries – MOA

4. Rapid Miner-

Rapid Miner also provide Java API and Tools for machine learning algorithm simulation .I love rapid miner because it gives every thing which a data scientist needs in a single platform . From data preparation to modeling and deployment , Rapid miner could be a good solution . It also provide no coding workflow  for applying data science in popular big data framework like (hadoop and Spark ) .Like it contains rapid word in its name , the Rapid miner team is consistently releasing the improved version . So if you have tried rapid miner few month back , May be rapid miner is much better than before .

Java Machine Learning Libraries RAPID Miner
Java Machine Learning Libraries RAPID Miner

5. MLib –

Another Big name in the list .MLib contains  variety of machine learning algorithms . It has big tale for classification , regression , clustering , association and recommendation system .MLib comes default with Apache spark .Obviously when we talk about any Machine learning library , We can’t ignore the community size behind it . MLib also have a active community behind it .

Java Machine Learning Libraries - Mlib

6. Other popular java machine learning library –

Apart form above , there is a big list for java machine learning library . I have not put them in the top list , It doesn’t mean they  are  worthless . Actually every framework or library has its own unique benefits over other . Anyways here are the links for them –

  1. MEKA
  2. ELKI
  4.  Java -ML


Difference between java machine learning tool and java machine learning library –

tools simulate the model where you have some graphic user interface and import connections  for importing data .I mean there you need to just select the data source type and import the data . Once you have data , you can also perform the cleaning and other type of pre processing stuffs . The idea is to perform all machine learning steps using configuration only .

In the opposite side , Machine learning libraries have all code . You need to understand the documentation part and call in order as you want . Now the question gets up ! Which is most convenient and useful ?

The Answer is not that simple , Actually  its depend on the objective . Mostly when you do not have many modification in default machine learning model or existing  Pre processing steps . In this scenario you should choose a machine learning tool because it will do most of the required thing in very less time .  Although if you we need to change a lot in existing or pre defined methodology , It is best to go with machine learning libraries . It will give you full control and debugging power.

Conclusion –

I hope this article ” Top Java Machine Learning Libraries and Tools ” is enough for answering your query .  If you need to know more about Top Java Machine Learning Libraries and Tools , You can comment below . We are also available through social media channels like data science learner facebook page .

As you know the technology is changing very rapidly so the tool which or the top may or may not be on the top if new one come and replace it . Its not only true with Top Java Machine Learning Libraries and Tools but all others field . Right ! Although we keep update the above list for Top Java Machine Learning Libraries and Tools  but if we feel while reading the any new name should be added in Top Java Machine Learning Libraries and Tools . please let us know .

I have also discussed in the starting of the article Java for Machine Learning ? Is a most discussed  question in communities . There could be multiple reasons to use java for  machine learning . Like – the Some time due to business reasons and the running project technology stack . We have to use java We have also design a related article on this  , please have a look –
How a Java Engineer can Transform his career into Data Science?

Data Science Learner Team.

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Meet Abhishek ( Chief Editor) , a data scientist with major expertise in NLP and Text Analytics. He has worked on various projects involving text data and have been able to achieve great results. He is currently manages, where he and his team share knowledge and help others learn more about data science.
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