Python, no doubt, is the leading coding language in machine learning. Due to its immense popularity, it can help you perform numerous tasks and activities. Apart from Python, Julia is available for developers who want well-established and unique machine learning projects. You can perform numerous activities and design algorithms for statistics, predictive modeling, and visualization, etc. In the future, it’s likely that Julia will take over Python’s position as a number one developing language.

Julia is an amazing, powerful, and popular analytical tool. Many of the biggest corporations rely on it for analytics purposes. Here are some top giants using Julia:

  • NASA
  • Oracle
  • Amazon
  • Facebook
  • Disney
  • Google
  • IBM
  • Microsoft

If you want to start your career in any of these companies as a Machine Learning specialist, you should learn about Julia. This means knowing how you can perform tasks and activities through this programming language. You may wonder why this application is not mainstream, despite many top companies using it. The problem is about shootouts.

Julia as a Developing Language

Julia is still a developing language, but it is rapidly making its way to different industries. Although Julia developers are working consistently to release new updates and solve the problems, various problems still affect its popularity. After so many updates and changes, this tool is now stable. Julia’s grammar has undergone various changes to enhance the performance and make it easier to remember.

The efficiency with Julia is satisfactory. However, you need to learn how to write codes instead of copy-pasting them to Julia. If you are new to Julia, you have to spend some time understanding the codes for satisfactory results and efficiency.

Machine Learning in Julia

  • Python and Spark

When you know how to design a machine learning project in Python, you can easily understand the concept with Julia as both languages are similar. Furthermore, you can also include a big-data manipulating power by using Julia with Spark. If you are starting your career as a data scientist, you can start with Julia. Julia has a similar syntax as Microsoft Basic and MATLAB, so you can easily transfer to Julia.

  • Parallelism

Manufacturers designed Julia as a Machine Learning tool, and it helps with scientific computing with a high level of compatibility. If you want a tool for extensive computations and complicated tasks, you can start using Julia. Julia enables you to perform activities with speed using C and C++ as add on. These frameworks will help you to construct a reliable model. You can use Julia for any industry as the applications enable you to perform a wide range of activities. Julia’s applications mostly require parallel and scientific computation.

  • Scalability

Julia enhances your computing abilities drastically. Furthermore, Julia is faster and scalable than R and Python. You can create large clusters with Julia even if you are using big data in a distributed environment.

Julia offers you numerous frameworks and libraries so you can create powerful artificial intelligence and machine learning projects. Libraries such as Flux.jl, MLBase.jl, and many others are built-in with the tool enabling you to perform different tasks and activities.

Top 7Libraries for Machine Learning In Julia

  1. Flux

You can use this library for your machine learning and deep learning projects and create intuitive and amazing models. You can easily differentiate and combine other libraries from Julia with Flux. Here are some of Flux’s features:

  • ONNX
  • GPU support
  • Differentiable programming
  • Compiled eager code
  1. jl

MLBase is an amazing library you can use for machine learning in Julia. MLBase isn’t an algorithm or help in creating one. But it includes various tools to support various activities such as model tuning, score-based classification, preprocessing, performance evaluation metrics, and other functions. You can seek help from MLBase documentation as it includes numerous code examples for every tool available in the collection of the library.

  1. scikitLearn.jl

You can also use the Scikit-learn library for Julia. Developers use this library popularly with Python. ScikitLearn enables you to implement algorithms and interface in Julia. Furthermore, it provides an interface to train your models. You can also use the models, set the tools, evaluate, and tune the model.

  1. tensorFlow.jl

You can use this library as a Julia wrapper and learn TensorFlow. The wrapper also performs various other activities such as rapid post-processing the results, fast ingestion of your data (common or uncommon formats). This tool enables you to perform statistics and visualization calculations for vectored implementation.

  1. Merlin

Merlin is a Julia framework that you can use for deep learning models. You can avail this flexible, fast, and compact library to design and use your machine learning model. You can run this library on CUDA GPUs and CPU.

  1. Mocha.jl

This deep learning framework inspired by the popular Caffe, which is a C++ framework, helps in performing tasks with Julia. You can train neural networks with the help of Mocha. This is an efficient and gradient solving tool. You can pre-train your model with an unsupervised model with the help of auto-encoders.

  1. Strada 

The components of the Strada library are based on the Caffe framework. You can use this open-source library for a deep learning project in Julia. You can use this library to design a convolutional and recurrent artificial neural network for GPUs and CPUs. This library supports Caffe features, and you can integrate them with Julia. Furthermore, you will find this library flexible.


The popularity of Python lies in the reason that it is simple to use and has an impressive collection of modules and libraries to work on. Another reason why most people know about Python is that it has been available for a longer time. However, Julia is a new language that needs more attention. But we can see a brighter future ahead, i.e., data computation.

Julia is also a fast and simple tool similar to Python. However, developers should focus more on understanding this language and make changes to perfection. If you want to join Julia and share your contribution to make it a better language, then you should definitely understand what Julia is offering.