Overview
Machine Learning(ML) projects can be done in Java but there are some reasons why Java is not as popular as Python. Java is not the preferred first choice of Data Scientists and Machine Learning engineers for creating ML models.
Java is mainly used in large data processing and engineering parts of a typical ML life cycle. The processed and engineered data is used by the ML models for both supervised and unsupervised learning tasks. ML deals with large collections of data and datasets that need to be processed in an systematic and effective way. The quick learning curve in Python and the availability of frameworks allows more data scientists to quickly pick up Python.
Some common ML libraries available in Java.
Deeplearning4j
Deeplearning4j is an open-source, distributed and deep learning library for the JVM which is written in Java. It is compatible with any JVM language, such as Scala, Clojure or Kotlin.
TensorFlow-Java
TensorFlow provides a Java API. Though it is not as developed and stable as TensorFlow’s Python API it can run on JVM and has support for both CPU and GPU.
Apache OpenNLP
OpenNLP is an open source Natural Language Processing Java library. It has features for entity recognition, parts of speech detection and tokenization.
ADAMS
The Advanced Data Mining And Machine learning System (ADAMS) is a flexible workflow engine. It is used for quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes.
One of the reasons Python has gained popularity is the availability of libraries and frameworks. There are frameworks and libraries for almost every aspect of machine learning. This makes Python more popular with Data Scientists and ML engineers.
Some popular frameworks and libraries for machine learning.
| Tensorflow | For Machine Learning, Deep Learning and heavy computations. |
| Scikit-Learn | For handling complex data, clustering, linear and logistic regressions, classifications. |
| NumPy | For the computation of scientific or mathematical data. |
| Theano | For computing mathematical expressions with multi-dimensional arrays. |
| Keras | For calculations and prototyping and offers functionalities for computing models, data-sets, visualising graphs, etc. |
| NLTK | For Natural Language recognition and processing, text analysis, and text mining. |
| Pandas | For handling large data structures and analysis. |
| Matplotlib | For the creation of visualising objects such as 2D plots, histograms, and charts. |
Rapid Model Development
ML projects require a lot of experimentation, evaluation and testing in an iterative and incremental manner. For a ML engineer to write simple block of scripts and executing them is quite easy using Python. Tools such as Jupyter Notebook and Google Colab also facilitate rapid model development. These tools make the life of a ML engineer easier and less complex.
Model Evaluation and Visualisation
Python offers a lot of libraries for data and model evaluation and visualisation. It is important to highlight that in machine learning, it is quite important to be able to represent and visualise data in a human understandable format.
Conclusion
With the advancement and availability of out of the box platforms to manage end to end data science life cycle projects, going forward this field of Machine Learning will become more automated and language agnostic or a low code /no code affair.
