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.
Machine Learning Explained
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.
Why Python is Great for ML Applications
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. And in the unlikely event that a development team runs into problems, Python’s active community can readily provide support on question-and-answer platforms such as StackOverflow, Reddit, and Yahoo.
Machine learning applications, be they Python-based or not, are particularly valuable to B2B organizations that need to better analyze customer trends for competitive research and predictive planning. The larger the sets of data a development team has access to, the more accurately ML applications can predict customer trends using behavioral analytics. ML-powered analysis of customer behavior requires continuously growing sets of data, which means development teams must create powerful computational processing models to keep up.
Let’s take a look at some of the core concepts of machine learning and how they relate to applied data analytics.
ML Core Concepts in Action
There are some basic concepts that provide useful context for developers who are exploring ML with Python. One of the most fundamental of these concepts is that of supervised and unsupervised ML algorithms.
Supervised algorithms handle data that are labeled manually by developers and that use target variables for each data point. Supervised algorithms are relatively expensive compared to unsupervised ones, which handle unlabeled data and discern underlying regularities in their input by themselves.
Supervised and unsupervised learning applications contribute to the ways ML improves customer analytics. Both types of algorithms can pull data to predict behavioral patterns and detect precisely when customers need assistance while shopping. This ensures a greater number of customers continues along their sales process without encountering complications. It can even help organizations identify and contact prospects before those prospects contact them, improving sales success and enhancing the overall customer experience.
Another concept is that of classification versus regression, which applies to ML applications that predict categories and continuous values. Classification algorithms use classifiers that handle binary problems (for example, true or false) or multiclass distinctions to predict categories (for example, categorical animal types). In contrast, regression models predict continuous values such as house prices.
Such ML applications are often used in a cybersecurity context, such as identifying fraudulent behavior or suspicious network activity. Hackers are relentless, and once they’ve gained entry or access to a website, they are likely using the site to either infect other people or providers. Machine learning powers AI-based security monitoring systems that help security professionals detect and respond to breaches, decreasing the risk that an attack will spread throughout the network.
Machine learning is an umbrella term for a wide range of models and applications, and it makes use of many important concepts that developers can explore with Python. Although this article covers some of the most common ML concepts, it’s important that developers gain a larger understanding of ML in practice by choosing problems that interest them or their organizations. From there, developers can both work on their own ML projects and apply their improved understanding of ML models in the workplace.