Imagine if you could make Java machine learning app development faster, and see an instant boost in your workflow.
or even better:
What if there were the best Java machine learning library to choose from.
…and each of them could make your development faster and easier?
You’d probably be pumped to read about them.
Lucky for you, that’s exactly what I’m going to share with you in this post:
Best Java machine learning library can become an unparalleled ally when it comes to increasing productivity and workflow while developing our own machine learning app using Java.
That being said, let’s highlight the best Java machine learning library or libraries you can use currently.
1. Weka
Weka is a collection of machine learning algorithms for data mining tasks.
The algorithms can either be applied directly to a dataset or called from your own Java code.
Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Why The Best?
- It has 4 modes – GUI, command line, Experimenter, Knowledge Flow.
- Weka also implementations of most of machine learning algorithms.
- It lets you run learning algorithms on text files.
- Weka is open-source and has decent documentation.
- You can extend it using your code.
- It has advanced features to support setting up long-running mining runs as well as experiment and compare various algorithms.
2. Rapid miner
RapidMiner offers a suite of products that allow data analysts to build new data mining processes, set up the predictive analysis, and more. The list of products includes: RapidMiner Studio, RapidMiner Server, RapidMiner Radoop, and RapidMiner Streams.
Why The Best?
- It has 4 modes – GUI, command line, Experimenter, Knowledge Flow.
- Weka also implementations of most of machine learning algorithms.
- It lets you run learning algorithms on text files.
- Weka is open-source and has decent documentation.
- You can extend it using your code.
- It has advanced features to support setting up long-running mining runs as well as experiment and compare various algorithms.
Visit site 3. Massive Online Analysis (MOA)
MOA is the most popular open-source framework for data stream mining, with a very active growing community.
It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection, and recommender systems) and tools for evaluation.
Why The Best
- It has 4 modes – GUI, command line, Experimenter, Knowledge Flow.
- Weka also implementations of most of machine learning algorithms.
- It lets you run learning algorithms on text files.
- Weka is open-source and has decent documentation.
- You can extend it using your code.
- It has advanced features to support setting up long-running mining runs as well as experiment and compare various algorithms.
4. Deeplearning4j
Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala.
Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.Skymind is its commercial support arm.
Why The Best?
- It has 4 modes – GUI, command line, Experimenter, Knowledge Flow.
- Weka also implementations of most of machine learning algorithms.
- It lets you run learning algorithms on text files.
- Weka is open-source and has decent documentation.
- You can extend it using your code.
- It has advanced features to support setting up long-running mining runs as well as experiment and compare various algorithms.
5. ELKI
ELKI is an open-source data mining software written in Java.
It is designed to be easy to extend for researchers and students in this domain and welcomes contributions of additional methods.
ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms.
Why The Best?
- It has 4 modes – GUI, command line, Experimenter, Knowledge Flow.
- Weka also implementations of most of machine learning algorithms.
- It lets you run learning algorithms on text files.
- Weka is open-source and has decent documentation.
- You can extend it using your code.
- It has advanced features to support setting up long-running mining runs as well as experiment and compare various algorithms.
6. cv4j
Its target is to set up high quality and real-time image process and machine learning library which is implemented in pure java.
The framework can run the application on java desktop and android platform.
Why The Best?
- It has 4 modes – GUI, command line, Experimenter, Knowledge Flow.
- Weka also implementations of most of machine learning algorithms.
- It lets you run learning algorithms on text files.
- Weka is open-source and has decent documentation.
- You can extend it using your code.
- It has advanced features to support setting up long-running mining runs as well as experiment and compare various algorithms.
7. JSAT
SAT is a library for quickly getting started with Machine Learning problems.
Part of the library is for self-education, as such – all code is self-contained. JSAT has no external dependencies and is pure Java.
Why The Best?
- It has 4 modes – GUI, command line, Experimenter, Knowledge Flow.
- Weka also implementations of most of machine learning algorithms.
- It lets you run learning algorithms on text files.
- Weka is open-source and has decent documentation.
- You can extend it using your code.
- It has advanced features to support setting up long-running mining runs as well as experiment and compare various algorithms.
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Conclusion
As a Java developer having and using the right Java machine learning library will help you in the quest for putting together an algorithm that will tap into the strengths and capabilities of the machine learning project of your choice.
Further reading:
☞ Deep Learning Tutorial with Python | Machine Learning with Neural Networks
☞ Building an Image Classification Model in 10 Minutes
☞ How To Prepare Your Dataset For Machine Learning In Python
☞ How to Deploy Machine Learning Models on Mobile and Embedded Devices
☞ Learn Machine Learning with Python for Absolute Beginners
☞ Speed Up Your Python Code with Cython
☞ Codeless ML with TensorFlow and AI Platform
☞ TensorFlow vs. PyTorch vs. Keras for NLP
#machine-learning #java
