As the use of machine learning (ML) in different applications increases, many programming languages have had to adapt and introduce new ML packages and libraries into their offering to stay relevant. There are many ML programming languages, however, three of the most popular languages include Java, Python and R.
Java is a well-known language that is popular with developers who are familiar with it. Java is a typed, object-based programming language that can be used for many different development projects. Java uses third party ML libraries that provides users with a number of diverse ML algorithms that can easily hyper-parameter tuned according to your use-case. Python is another versatile programming language that is a popular choice for ML, and is built on an ecosystem that allows for developers to easily extract, transform and load their data for their machine learning applications. Python makes it easy to build out integrated workflows that are flexible, allowing for machine learning applications to be easily integrated into production environments. R is a popular choice to do ML especially for statisticians and data scientists, who require an easy to work with language that can perform complex data analysis, data mining, machine learning and provide users with intuitive graphs presenting their findings. This can be done using R-Studio which is a user-friendly integrated development environment (IDE) designed to be used with R. There are a number of tools and libraries available in R that make it possible to train and test machine learning models for prediction and classification.
Let’s compare the strengths between Java, Python and R in terms of their machine learning capabilities and ease of use. One of the strengths of Java is that it can be easily integrated with tools that are extensively used in organizations for big data processing, such as Hadoop and Spark. Both frameworks are involved with data processing which is the foundation of machine learning. Java provides users with a simplistic interface to improve productivity and make it easier to perform debugging. Java virtual machine (JavaVM) makes it possible for developers to build machine learning applications for multiple platforms, making it scalable and well-suited for large scale systems. Out of the three mentioned programming languages, Java is the best of the three for large-scale machine learning applications. However, in terms of statistical modelling, Java is inferior to R and Python. This is because Java is not built for machine learning applications but rather to build out complex, large and integrated systems because it’s a typedm, object-orientated language. One of Python’s strong points is ML, because it is flexible, allows for continuous data processing, is dynamic in nature and possesses a vast number of popular ML libraries. These libraries include Sci-Kit learn, PyBrain and Tensorflow, all allowing users to build intelligent, accurate and efficient machine learning models. Python is superior to R in terms of its automation and data processing capabilities for ML, however, when it comes to specialized data science use-cases, R is a more popular choice. However, Python is more production friendly and a lot easier to learn than R. R provides users with an easy approach to machine learning, covering an extensive number of machine learning tools and techniques for detailed statistical research, analysis and reporting. R has a much steeper learning curve than Python and Java and it is inferior to both when it comes to high performance and scalability. However, R is more capable of performing complex calculations than Java making it a popular language choice for statisticians.
If you want to build small-scale tools that can be used for data analysis and specialized machine learning applications, R is a good programming language choice although it does have limitations. However, if you require a more general purpose language that can make it easy to build production-ready, high-performance machine learning models, Python is well-suited to your use-case. If you require machine learning models for large, complex and highly distributed systems, then Java can prove quite useful. It is evident that each programming language for ML has their strong points and limitations, however, you have to choose the programming language that is best suited to your use case based on your ML and business requirements.
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