article1Sujith Kumar

Best Languages For Machine Learning in 2021!



Python is the most popular and promising programming language for machine learning. There are lots of benefits of using Python for Machine learning. It ranks first among all other languages. Here is why: 

  • Python has a great library ecosystem that offers you various tools 
    1. SciKit-learn handles classical ML algorithms
    2. Pandas for high-level data structures and analysis.
    3. Keras and TensorFlow for deep learning
    4. Matplotlib for visualization.
  • You can easily use and learn Python because it's flexible and has fewer possibilities for error.
  • Python is gaining momentum in the Analytics domain since the language is open-source.
  • You can implement any changes and quickly see the results. There is no need to recompile the source code in Python.
  • Python offers efficient exchange of algorithms and so, there’s no confusion, errors, or conflicting paradigms.
  • Tools like IPython provide extra features like testing, debugging, tab completion.  


We know Java is also a popular language. Java is used for larger projects in machine learning. Here is how Java contributes to ML 

  • Java has efficient Libraries for implementing Machine Learning. A few of them are:
    1. Weka supports deep learning.
    2. JavaML maintains good documentation with clear interfaces. 
    3. Apache Mahout provides implementations of machine algorithms for the Apache Hadoop platform. 
    4. ADAMS (Advanced Data Mining and Machine Learning Systems) maintain data-driven, performs retrieval, processing, mining, and visualization of data.
  • You can easily use and debug Java since it has high performance and is faster. 
  • Java is effectively used for large-scale project development as you can experience better user interaction.


C++  would be the ‘first language’ we might have used. C++ is used in machine learning even though several programming languages came later. Let’s check how C++ perform in Machine learning :

  • C++ is used in search engines, computer games, building neural networks as it facilitates faster execution of complex algorithms. 
  • You can save time and costs as C++ supports re-use of programs, 
  • C++ finds easy solutions for complex AI problems.


R Programming is a graphics-based language used for statistical computing and visualizations in machine learning. It is a popular programming language among statisticians. Let’s check how R operates in machine learning.

  • R develops bioengineering, bioinformatics, and biomedical statistics. 
  • R is suited for one-off projects such as reports, research papers, and artifacts.
  • You can create predictive models efficiently using the libraries and tools in R.



Scala provides functionalities for big data processing and machine learning analysis. Advantages of Scala are:

  • You can develop, design, code, and deploy machine learning algorithms
  • Scala programming language suitable for linear algebra, random number generation, and scientific computing.
  • Scala can be used in conjunction with Apache Spark to deal with a large volume of data which can also be called Big Data. 


Which ML Language should you Opt for?

Machine Learning requires and uses more than one programming language for its output. The above-mentioned are widely used languages in ML and AI. If you ask us the best of all, we would say it is Python, but every language has its specific benefits. Being an AI developer, it’s important that you choose the programming languages wisely for a better output.

Read More How to perform Machine Learning in Python?

article1Sujith Kumar


He is a professional IT Faculty having more than 18 years of experience with Edoxi Training Institute Dubai. He schedules classes into facilities and provides software instructions.  He spends most of his free time learning new software skills and also interested in driving and reading.

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