Python is one of the leading programming languages for its simple syntax and readability. Machine learning algorithms can be complicated, but having flexible and easily read code helps engineers create the best solution for the specific problem they're working on. Python supports a variety of frameworks and libraries, which allows for more flexibility and creates endless possibilities for an engineer to work with. Top libraries: sci-kit image, OpenCV, TensorFlow, PyTorch, Keras, NumPy, NLTK, SciPy, sci-kit learn, Seaborn, Matplotlib.
Python

The R programming language focuses primarily on numbers and has a wide range of data sampling, model evaluation, and data visualization techniques. It's a powerful language — especially if you're dealing with large volumes of statistical data. Top packages: Dplyr, tidyr, CARET, Ggplot2, MICE, PARTY, rpart, Shiny, Rmarkdown, randomFOREST.
R Tutorials

Java and JavaScript are some of the most widely used and multipurpose programming languages out there. Most websites are created using these languages, so using them in machine learning makes the integration process much simpler.
Top libraries for Java: JavaML, Arbiter, Neuroph, Weka.
Top libraries for JavaScript: Math.js, TensorFlow.js, OpnCV.js, Synaptic.
Java Tutorials JavaScript Tutorials

C++ is another popular programming language widely used for performance-critical applications that need memory management and speed at the forefront. These features make it an ideal programming language to use when working in machine learning.
Top libraries: Caffe, LightGBM, DyNet, Turi Create .
C++ Tutorials

Shell can be used to develop algorithms, machine learning models, and applications. It uses mathematical models to collect and prepare data. Shell supplies you with an easy and simple way to process data with its powerful, quick, and text-based interface.
Top libraries: Ml-notebook, Dl-machine, Docker-prediction.
Shell Tutorials