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It has some very high-rated ML libraries including MI-Notebook, DI-Machine, and Docker-predictionio. Another great aspect of Shell is its speed – what would take over ten minutes to achieve via a graphical interface, Shell can accomplish in one minute! Hence, it is a beginner-friendly option for those who wish to explore the basics of ML app development. Just like Python, Shell features a simple and neat syntax.
Machine programming language list code#
While Julia’s mathematical syntax allows you to express algorithms as you would on a paper, Flux converts the code into trainable models with automatic differentiation, GPU acceleration, and support for large datasets through JuliaDB. Julia Computing maintains that Julia has the best-in-class support for ML frameworks such as TensorFlow and MXNet, which makes the adaption to existing workflows much more manageable. It was created to cater to the need for high-performance numerical model analysis essential for ML applications, and hence, it is highly suitable for Machine Learning. Julia is the perfect match for ML developers who are always on the lookout for languages that will allow them to write ML algorithms as code. Apart from this, it also has Accord.NET and ML-Agents. NET developers to work on ML applications. ML.NET is a cross-platform, open-source ML framework that allows. NET Core machine learning platform – ML.NET. It is a versatile language as it allows developers to build varied applications like web apps, mobile apps, consoles, and even backend frameworks.Īs for Machine Learning, C# has a dedicated.
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Java also has a host of ML libraries like Weka, ADAMS, JavaML, Mahout, RapidMiner, Neuroph, JSTAT, DL4J, to name a few.Ĭ# is a general, flexible, and open-source, object-oriented programming (OOP) language primarily used for web development and networking.
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In fact, large corporations (both in the public and private sector) have a massive Java codebase that leverages JVM as the primary computing environment.
Machine programming language list software#
Java is one of the most extensively used programming languages for developing Big Data ecosystems and also for software development. Thanks to these wonderful ML libraries, JavaScript has now found applications in enterprise-level and personal projects. It has numerous libraries for training and deploying ML models including TensorFlow.js, Brain.js, stdlib-js, machinelearn.js, math.js, face-api.js, and R-js. JavaScript is a very most popular web scripting language. It also comes with numerous other low-level features like the choice of the memory management system. Thus, if implemented correctly, C++ can help create fast and well-coded algorithms.įurthermore, C++ allows you to implement advanced computer vision and ML systems from ground-up. Speed and efficiency are the two key aspects of C++. One of the oldest programming languages, C++ is highly suited for Machine Learning, thanks to its ML repositories like TensorFlow, LightGBM, and Turi Create. Python comes with an assortment of excellent libraries and tools for ML, including Scikit Learn, TensorFlow, ChatterBot, and much more. It is a highly dynamic, open-source language that supports object-oriented, imperative, functional, as well as procedural development paradigms. Python is extremely popular in the developer and coding community. Without further ado, let’s dive into the list of the top ten programming languages for ML! However, there’s often much confusion surrounding the question, “what are the best programming languages for Machine Learning?”
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After all, ML applications and ML algorithms are written and designed using programming languages. Programming is a pivotal aspect of Machine Learning. Home > Artificial Intelligence > Best Programming Languages for Machine Learning: Top 10 List