Top Programming Languages AI Engineers Need to Grab in 2020

The AI race has just begun.

Programming skills have become an integral part of every tech professional. Not to mention, aspiring data scientists, data engineers, machine learning engineers, AI specialists, and AI engineers are to gain huge benefits after the lockdown. With technology on the rise, programming skills have become a prime skill set for all technology professions.

AI needs no introduction today. It is the present and the future.

The technology that stands to change our lives for better. With the promise of a prosperous future, AI has been at the vanguard of the list of new and emerging technologies along with big data, IoT, and robots. Many experts said that the role of a technology innovator will keep improving in the days to come.

AI is now slowly making its way into popularity, a technology many futurists say will provide considerable benefits to our lives.

Employers these days want to hire AI engineers who can do their jobs right, people who can handle multiple clients while ensuring the projects are well-maintained. The majority of the organizations are already working their way into deploying AI, however, reports show that we’re still falling short on the supply of skilled AI professionals.

Let us further consider the skills and programming languages that a budding AI professional needs: –

  • Java

One of the oldest and best programming languages across the globe. Well, it survived for two decades, isn’t that enough proof of evidence?

Java is flexible, platform-independent, and user friendly and has been an integral part of AI in multiple ways.

Let us take a glance,

  1. Deep Java Library – developed by Amazon to help create and deploy deep learning abilities through Java.
  2. TensorFlow – TensorFlow offers a long list of programming languages that involve Java with API. However, the support is not feature-rich compared to the other supported programming languages. Though it still exists it is improving at breakneck speed.
  3. Java Machine Learning Library – the Java machine learning libraryoffers several machine learning algorithms for developers to choose from.
  4. OpenNLP – the Apache’s OpenNLP is a machine learning tool for NLP.
  5. Kubeflow – the Kubeflow augments deployment of models and helps in managing machine learning stacks on Kubernetes. Thus, offering readily available machine learning solutions.
  • Python

The most powerful and easy language known by all developers.

Python for AI engineers is now the most preferred programming that can solve multiple complex problems. In addition to this, Python has become the top priority for AI professionals in helping them make informed decisions.

Since Python is easy to learn, the language offers an easy path for data scientists and data engineers to enter the world of AI development.

As Guido van Rossum says,

“Python is an experiment in how much freedom programmers need. Too much freedom and nobody can read another’s code; too little and expressiveness is endangered.”

Having Python as a skill set not only offers you strong community support but offers multiple sets of libraries you can choose from based on the type of project you’re working.

The most popular Python libraries are:

  1. Keras – used for operations and complex mathematical calculations.
  2. Scikit-learn – used while training machine learning algorithms.
  3. Theano – helps to define, optimize, and evaluate mathematical expressions.
  4. SparkMLlib – it is like Apache Spark’s Machine Learning library, and it helps in making machine learning smooth and easy offering tools such as utilities and algorithms.
  5. PyBrain–this library is used for powerful machine learning algorithms.
  6. TensorFlow – used if working with large datasets and machine learning workloads.
  7. PyTorch – for NLP and computer vision.
  • R

R programming is extensively great while crunching numbers. It keeps itself in a much better position as compared to Python. With R programming, you get to work on the varied platform of programmings such as object-oriented, vectorial computation, and functional programming.

Besides the above-mentioned languages, you can opt for Julia, Haskell, Lisp, and Prolog.

With such packages to choose from, it gets very easy for AI engineer to choose their programming language that caters to the need of their projects.

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About the Author: Alen Parker

Alen Parker is a critically-acclaimed writer who has generated a wide range of content during his professional career. His industry-wide experience into writing for different niches is certainly an admirable aspect that empowers him to create enriching, informative write-ups.

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