Which Programming Language Is Considered to Be Perfect for Machine Learning?

Deep learning, artificial intelligence and machine learning are slowly revolutionizing every aspect of our lives, from music streaming to industrial automation.

Python emerges as the leading language for machine learning development. Python's appeal stems from its versatility—it is not limited by technology constraints. High-quality documentation and simplicity make Python accessible to developers. The language benefits from extensive open-source libraries including TensorFlow, Apache Singa, Torch, and Brainstorm.

R programming language offers advantages for data-focused projects. R focuses on better, user-friendly data, statistics and analysis compared to Python's emphasis on readability and productivity. However, R's steeper learning curve and performance limitations make Python more popular among developers.

Java remains relevant for security-focused projects, particularly fraud detection or cyber attacks. Java's Weka framework parallels Python's scikit-learn library. However, Java requires significantly more code for the same operations.

C++ excels when speed matters most. It is valuable for robot locomotion and artificial intelligence in games due to high performance and speed of operation.

No single perfect programming language exists. Project requirements ultimately determine the best choice among Python, R, Java, C++, Julia, Scala, Lua, and other specialized languages.