According to a 2017 Developer Economics survey of over 2,000 machine learning developers and data scientists, Python is the most popular programming language for machine learning. 1 However, popularity alone doesn't indicate why Python is an excellent choice. In this post, we'll take a look at the top reasons the sophilabs team prefers Python when working on machine learning projects.
Wealth of Libraries
A lot of great work has already been done with Python in machine learning, meaning that there are high-quality, time-saving resources available. Some Python libraries that can be used for machine learning include TensorFlow, PyTorch, Scikit-learn, Theano, Pandas, Matplotlib, and Seaborn. 2 Python is especially popular in academia, resulting in an abundance of publicly available, highly specialized machine learning models.
Python libraries provide a competitive edge to developers, who can spend less time coming up with totally new machine learning algorithms and more time focusing on the specific solutions their projects require. 3 Sophilabs considers Python libraries a major advantage for our clients because they enable a faster product turnaround.
The Python community is very active in online forums, providing invaluable support. If a developer encounters a problem while writing code in Python, chances are, someone else has experienced the same issue and is happy to help. At sophilabs we consider it important to be involved the developer community, and we encourage our team members to contribute their own knowledge as well.
Python is an interpreted language. Among other characteristics, interpreted languages translate programs one statement at a time. They stop running when they come across an error and immediately display it. In contrast, compiled languages like C++ or Java must scan the entire program before generating an error message. This means that it's easier to tackle bugs in an efficient way when writing code in Python. At sophilabs we're in favor of anything that allows us to deliver high-quality products to our clients in less time. Being able to debug as we go also complements the way our detail-oriented teams like to work.
Python is known for its accessibility and simple syntax. It's easily readable for human programmers, making collaboration straightforward since engineers can quickly understand each other's work. The fact that it's dynamically typed also allows developers to adapt to changing requirements with less code than other languages. Sophilabs believes in the power of teamwork and in the great things that become possible when we put in a joint effort. We see Python as a significant factor in making these high achievements possible.
We're Excited About the Possibilities
Machine learning, together with other AI capabilities, has the potential to transform industries. Our team at sophilabs has applied its skills to create apps that use machine learning to best fulfill user needs. Contact us if you want to find out more about how our expertise can help your company reach its goals!
What is the Best Programming Language for Machine Learning? by Christina Voskoglou ↩
Essential Libraries for Machine Learning in Python by Shubhi Asthana ↩
What's the Best Programming Language for Machine Learning Applications? by William Goddard ↩
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A software design and development agency that helps companies build and grow products by delivering high-quality software through agile practices and perfectionist teams.