Most popular programming language frameworks and tools for machine learning

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“Which is the most popular programming language for machine learning?”

So, now it is the time to explore this topic in the following the article. Ultimately, your shortlisted programming language for machine learning should be sufficient according to your predilections. Therefore, you should concern many meaningful addresses.


machine learning tools

machine learning tools

What languages are common?

Before opting any particular language, do research for machine learning libraries to conclude which languages and platforms are famous and widely accepted by machine learning professionals. According to various surveys, the use of R, Python, and SQL is for data access. The rank of SAS and MATLAB was also very higher than expectations. But this SAS is for the data analysis of large corporate whereas MATLAB is used for research, engineering and student use.

According to the majority, MATLAB or OCTAVE is preferred for matrix operations and it is good when its function with feature matrix.


Also Read: Top 10 Java Frameworks For Startups | 2019

Machine learning languages:

MATLAB/Octave: If you want to work and represent with matrices then MATLAB is the best. Moreover, it is good for linear algebra also.

R: As extension machine learning language, R is used for statistical analysis. Using this platform, you can explore various information utilizing graphs and statistical methods. R has many machine learning algorithms.

Python: Python can get the data analysis mantle from R as it is very famous scientific language. However, various NumPy that handle matrix can challenge MATLAB and various tools that are like a step into the future of reproducibility with most popular Python libraries.

Java or C family: Machine learning is not magic, it depends on algorithms. When there is a need for production implementation, then a robust library is required. Many robust libraries are available like Java has Mahout and Weka.

First of all, you should have an estimation of all machine learning languages and its popularity. According to many researchers, Python is leading this market as 57% of machine learning developers as well as data scientists are utilizing it whereas 33% use it for development. Although R is compared with Python there is no popularity comparison between these two languages. The rank of R is fourth in overall usage (31%) whereas it stands at fifth position in prioritization. Only 17% of developers use it so this language has the lowest prioritization-to-usage ratio. Therefore, R is not a priority of developers, it is just a complementary language. At the same time, Python is at the highest among the five languages with 58% ratio. But it doesn’t mean that only Python is preferred by developers, C or C++ is also the second choice after Python. Java usage is very close to C and C++ whereas JavaScript is fifth in usage preceding AI programming languages.


Get to know more about Top 10 Python Libraries To Set Your Idea of Machine Learning


Python is preferred where Java is not:

Scientists working in machine learning and sentiment analysis prefer Python over Java, JavaScript and R whereas those who are working in cyber attacks, network security and fraud detection prefer Java. In these areas, Python is not prioritized. This fraud detection and network security algorithms are commonly used in a large organization by internal developers. But in less enterprise focused areas like natural language processing (NLP), developers use Python libraries for machine learning to develop high performing algorithms in an easy way because of the collection of specialized libraries including in it.  

Software Solution

What is a programming framework?

Every programming framework is different as it takes much time to learn the features. Frameworks which are mathematically oriented are more geared towards statistical while others are popular for a rich set of linear algebra tools etc. Here is the list of some popular frameworks for machine learning libraries:

(i) TensorFlow: It was developed by Google Brain Team and used for data-based programming with deep learning tools. It can perform classifications, regressions, and neutral networks whereas it is most capable to run on CPUs and GPUs as well. Numpy is the framework of Python that can help to work with n-dimensional arrays. TensorFlow is difficult to understand at early ages because of its complex functions so you must have knowledge of Numpy arrays well.

Benefits of Tensorflow:

(a) Flexibility

(b) Portability

(c) Performance

(d) Auto differentiation

(ii) Apache Spark: Spark was developed at Berkeley’s lab and this framework is written in Scala, Python and R. In this Apache Spark framework, you can work on Spark RDD data structures or Spark SQL data frames.

Benefits of Spark ML:

(a) Simplicity

(b) Scalability

(c) Streamlined

(d) Compatibility

(iii) Scikit-learn: This free to use Python Library is used for building models. Its foundation is on the name of other libraries including Numpy, SciPy, matplotlib, etc. Scikit is an important tool for techniques of statistical modeling named as classification, regression, and clustering as well. Main features of Scikit learn include the supervised as well as unsupervised learning of algorithms and its cross-validation. This language is mainly written in Python whereas some algorithms are usually in Python in order to get performance.

Benefits of Scikit Learn:

(a) Main algorithms

(b) Data mining

(c) Practical tasks supporter

(d) Complex task solution

Professional background plays an important role in choosing any machine learning tools:

Considering professional background is very important when there is matter to select a machine learning language as these top languages come from various backgrounds using programming tools. Python is preferred by those who are from data science profession. So, you can say that Python has become a part of data science. This is not the same for R as only statisticians prefer R. Front end developers use JavaScript for machine learning whereas computer hardware or electronic engineers choose C and C++.

There is nothing like “best machine learning language”

If you are trying to find out the best language then there is nothing like the best language for machine learning as it only depends on the type of your requirement. If you have any experience in machine learning then Python is the best option for you but if your dream job is in an enterprise environment then you should use Java. No matter which language you select, if you work with dedication and care, the journey is going to be guaranteed successful apart from your language selection. So, enjoy this journey!


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Krunal Vyas

I’m Krunal Vyas, IT consultant at iQlance Solutions Pvt. Ltd is one of the leading Website and Apps Developers in Toronto & New York, I have helped more than 200+ clients to bring an idea into reality. I have attended many tech conferences as a company representative and frequently blogs about the search engine updates, technology roll-outs, sales & marketing tactics, etc. for more details visit our

4 CommentsLeave a comment

  • For working in a company, what the employers expect us as a machine learning engineer? As in are we supposed to build algorithms from scratch or are we supposed to use in-built libraries for the same work?

  • Good intro, thanks and I agree with your summary! C++ for speed and performance, python for ease and short learning curve. JavaScript?

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