Why are More Developers Using Python for Their Machine Learning Projects?
To support the creation of new and exciting ML and artificial intelligence (AI) applications, developers need a robust programming language. That’s where the Python programming language comes in.
When human beings learn something new, they typically do it by recognizing patterns through a combination of experience and education. Machines are able to learn too and apply that learning in a way that’s not so different from human decision-making.
Through what we call machine learning (ML), machines rely on a history of data patterns and experiences that inform algorithms and allow them to continually infer new patterns from data. To support the creation of new and exciting ML and artificial intelligence (AI) applications, developers need a robust programming language. That’s where the Python programming language comes in.
Python is one of the most commonly used programming languages that ML and AI developers use, and for good reason. Let’s take a quick look at why Python is the perfect choice when getting started in AI/ML development and some of its different applications for ML.
Let’s cover the definition of ML in a bit more detail so that we can better understand why Python is such an effective language for programming ML applications. Basically, ML refers to the mathematical models – what we call algorithms – that let ML applications predict things such as categories or other continuous values. They modulate their data input to make these predictions, and they use different parameters that they derive from their data inputs to do so.
ML models rely on the above process to continuously and iteratively learn. They take in their input and make their best possible prediction of the continuous value they need to arrive at. They further refine their ability to make predictions by means of a loss function that lets the model know how far off it is from the desired data output. This process is repeated many times to gradually improve accuracy.
Machine learning is what powers artificial intelligence. While these two terms are often used interchangeably, they are not the same. Different types of machine learning algorithms are better suited to different functions, but deep learning and reinforcement learning are the subfields that primarily enable the functions that make up our common understanding of AI.
Now that we understand the basics behind the ML learning process, it’s time to dive into why Python is such a great fit for ML.
Python has become the go-to language for programming ML applications in large part because of its readability. Python is simple to learn, accessible to developers of all experience levels, and highly portable since it can run on all popular operating systems. The biggest factor that lends to its popularity among ML developers, though, is its large ecosystem. The majority of ML algorithms can be implemented with Python as a framework.
This is especially good news for businesses and organizations that deal heavily with data and need ML applications to iteratively learn from those data sets. Most developers will charge at least $75 an hour for work on a development project if they’re reasonably skilled, which means that Python-based projects can at least be relatively inexpensive when handled by a team of experienced developers.
