The Top 10 Machine Learning Languages to Know in 2019

About Machine Learning Languages to Know in 2019

In the last few years, Machine learning (ML) has really come into its own. From tech giants like Google and Facebook to early-stage startups, everyone is using Machine learning with Python and other algorithms to build cutting-edge tools and products. With the median salary for a machine learning engineer reaching a whopping $111,000, it's no surprise that many young people are looking at learning critical machine learning skills.

While there's a lot to machine learning algorithms, you can't really start without being comfortable with the right programming language. It makes sense to start your journey towards mastering machine learning by learning those languages that are used most frequently in ML algorithms. GitHub recently published their list of the top 10 Machine Learning languages. This list is based on the primary languages that are tagged as related to Machine Learning in GitHub's code repositories. While the usual suspects like Python and R top the list, there are some surprises as well. Here's a rundown of the top 10 programming languages used in Machine Learning.

Python

WPython dominates the scene when it comes to ML languages, and it's not going away anytime soon. Python's strength lies in the fact that it's very adaptable. Plus, its syntax is incredibly simple, making it a beginner- friendly language. Python also has amazing libraries like NumPy, SciPy, Matplotlib, Pandas, TensorFlow and Scikit-learn that make scientific computing very easy. It's no wonder then that 57% of machine learning engineers use Python and 33% prioritize it for development.

R-Programming

WPython dominates the scene when it comes to ML languages, and it's not going away anytime soon. Python's strength lies in the fact that it's very adaptable. Plus, its syntax is incredibly simple, making it a beginner- friendly language. Python also has amazing libraries like NumPy, SciPy, Matplotlib, Pandas, TensorFlow and Scikit-learn that make scientific computing very easy. It's no wonder then that 57% of machine learning engineers use Python and 33% prioritize it for development.

JavaScript

WPython dominates the scene when it comes to ML languages, and it's not going away anytime soon. Python's strength lies in the fact that it's very adaptable. Plus, its syntax is incredibly simple, making it a beginner- friendly language. Python also has amazing libraries like NumPy, SciPy, Matplotlib, Pandas, TensorFlow and Scikit-learn that make scientific computing very easy. It's no wonder then that 57% of machine learning engineers use Python and 33% prioritize it for development.

C

WPython dominates the scene when it comes to ML languages, and it's not going away anytime soon. Python's strength lies in the fact that it's very adaptable. Plus, its syntax is incredibly simple, making it a beginner- friendly language. Python also has amazing libraries like NumPy, SciPy, Matplotlib, Pandas, TensorFlow and Scikit-learn that make scientific computing very easy. It's no wonder then that 57% of machine learning engineers use Python and 33% prioritize it for development.

C#

WPython dominates the scene when it comes to ML languages, and it's not going away anytime soon. Python's strength lies in the fact that it's very adaptable. Plus, its syntax is incredibly simple, making it a beginner- friendly language. Python also has amazing libraries like NumPy, SciPy, Matplotlib, Pandas, TensorFlow and Scikit-learn that make scientific computing very easy. It's no wonder then that 57% of machine learning engineers use Python and 33% prioritize it for development.

Java

WPython dominates the scene when it comes to ML languages, and it's not going away anytime soon. Python's strength lies in the fact that it's very adaptable. Plus, its syntax is incredibly simple, making it a beginner- friendly language. Python also has amazing libraries like NumPy, SciPy, Matplotlib, Pandas, TensorFlow and Scikit-learn that make scientific computing very easy. It's no wonder then that 57% of machine learning engineers use Python and 33% prioritize it for development.

UNIX Shell Scripting

WPython dominates the scene when it comes to ML languages, and it's not going away anytime soon. Python's strength lies in the fact that it's very adaptable. Plus, its syntax is incredibly simple, making it a beginner- friendly language. Python also has amazing libraries like NumPy, SciPy, Matplotlib, Pandas, TensorFlow and Scikit-learn that make scientific computing very easy. It's no wonder then that 57% of machine learning engineers use Python and 33% prioritize it for development.

Julia

WPython dominates the scene when it comes to ML languages, and it's not going away anytime soon. Python's strength lies in the fact that it's very adaptable. Plus, its syntax is incredibly simple, making it a beginner- friendly language. Python also has amazing libraries like NumPy, SciPy, Matplotlib, Pandas, TensorFlow and Scikit-learn that make scientific computing very easy. It's no wonder then that 57% of machine learning engineers use Python and 33% prioritize it for development.

Scala

WPython dominates the scene when it comes to ML languages, and it's not going away anytime soon. Python's strength lies in the fact that it's very adaptable. Plus, its syntax is incredibly simple, making it a beginner- friendly language. Python also has amazing libraries like NumPy, SciPy, Matplotlib, Pandas, TensorFlow and Scikit-learn that make scientific computing very easy. It's no wonder then that 57% of machine learning engineers use Python and 33% prioritize it for development.

Type Script

WPython dominates the scene when it comes to ML languages, and it's not going away anytime soon. Python's strength lies in the fact that it's very adaptable. Plus, its syntax is incredibly simple, making it a beginner- friendly language. Python also has amazing libraries like NumPy, SciPy, Matplotlib, Pandas, TensorFlow and Scikit-learn that make scientific computing very easy. It's no wonder then that 57% of machine learning engineers use Python and 33% prioritize it for development.

So which is the best programming language in terms of Machine Learning? The answer is, it depends. If you're a complete beginner who aspires to become a Machine Learning engineer, machine learning with Python is the best place to start. Machine learning with Python is very easy to learn and has the best libraries for Machine Learning. If you're already a software developer, the answer may not be as simple. If, for instance, you are already a competent JavaScript developer, then mastering TensorFlow.js might be a better approach. There is no one right path to becoming a machine learning engineer. The only key is to have a plan to get there, and the dedication to see it through.

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