Is Python Really a Perfect Language for Machine Learning?
Nowadays, we cannot imagine our lives without artificial intelligence-based programs and apps. Almost every suggestion in our news feed is personalized and we are used to the wide-range functionality of our devices (like an all-knowing Siri). And one of the key elements of today’s tech progress is the advancement of machine learning.
The fast development of machine learning technology promotes artificial intelligence growth and AI, in turn, makes a significant impact on all industries. To keep improving and evolving ML technology, developers and data scientists use different programming languages and tools. Among all the variants, a lot of developers consider Python as the most suitable for machine learning. But why is that?
What is machine learning and how does it work?
We’ve already written numerous articles on machine learning - have a look below:
- Machine Learning Trends 2022: What to Expect
- How Machine Learning Impacts Logistics and Supply Chain Management
- Top Use Cases of Machine Learning in Marketing
But for now, let’s briefly refresh our memory on the topic.
Machine learning is based on the idea that machines can learn to identify patterns and make decisions with minimal human intervention. Basically, It allows you to give the program the ability to independently build cause-and-effect relationships on the basis of provided examples. In simple words, machine learning is used to make machines “think” like humans.
Machine learning algorithms shape the framework and lay out the foundations behind how the machine or system will learn. There are three main types of ML:
- Supervised Learning: machines use labeled training datasets containing both the inputs and the desired outputs. In supervised machine learning, a data scientist labels the data before “feeding” it to the machine and this is how the machine learns and is able to classify new unlabelled data sets in the future.
- Unsupervised Learning: algorithms only use input variables without outputs. The machines learn from the datasets that are not categorized or labeled in advance by a data scientist and thus, independent learning is happening and the machine itself classifies data sets and finds relations and dependencies.
- Reinforcement Learning is based on the “trial and error” method, where a machine or computer program needs to choose the optimal path based on previously learned information.
Machine learning is used for a wide variety of use cases but primarily for analytics, research, and statistical problems. This technology also enables the automation of various functions of machines or services which, in turn, allows businesses to cut costs and optimize the efficiency of their operations. As for real-life examples of ML use, they include:
- Self-driving cars
- Speech recognition
- Face recognition
- Online behavior analysis
- Financial trading
- Personalized recommendations
- Medical diagnosis and predictions
ML algorithms are created with multiple sets of tools and different programming languages. However, many believe that Python is the best choice when it comes to ML development - let’s find out the possible reasons for that.
Why Python is considered the #1 option for ML
The primary reason why developers love Python for their ML projects is simply that a vast majority of software tools for Machine Learning were originally created with Python. Therefore, most ecosystems for processing, analyzing, and visualizing data are written in this language, although there are also many machine learning libraries in Java, R, and C ++. But it’s not the only reason why Python is so popular in the machine learning community. Let’s look at other advantages that Python offers.
Simplicity and short learning curve
Python has a simple and easy-to-read syntax. It is an interpretable language, so it has wide use in web development and automation. The simplicity of the language makes it highly appealing not only to beginners but to experienced developers as well. And since it’s very concise, code written in Python is very easy to write and understand.
A variety of libraries and frameworks
Python has an amazing ecosystem of libraries that makes machine learning easy to work with. Python libraries provide base-level items so developers don’t have to code them from the very beginning every time. For example, ML requires continuous data processing, and Python’s libraries let you gain it.
The most commonly used Python libraries for ML are:
- Scikit-learn: one of the most popular ML libraries used for data mining and analysis;
- Pandas: another popular library for data analysis;
- Numpy: multi-dimensional arrays computation library;
- Keras: great for beginners, allows easy and fast prototyping;
- TensorFlow: is used for working with deep learning by setting up, training, and utilizing artificial neural networks with massive datasets;
- pyTorch: one of the most popular ML framework covering all phases of research and development
Python is not only comfortable to use and easy to learn but is also very versatile. It is flexible and can run on any platform, such as Windows, Linux, Mac, and others. So, you can code in Python anywhere, and if you need to transfer your work from one platform to another, only some small-scale changes and modifications will be enough to execute the process. As well, such versatility contributes to better collaboration between developers that might be working on different platforms but on a single project.
A supportive community and growing popularity
First released in 1991 by Guido van Rossum, today Python is named the 4th most popular programming language according to the Stack Overflow Developer Survey 2022. And needless to say, throughout the years, Python has gained an impressively big community.
Due to the size of the community, you have better chances of finding a solution to your issue as you can rely on the experience of hundreds of developers who have already faced similar issues before and are ready to share their knowledge. This is another great benefit behind Python’s popularity.
But what about other languages?
Aside from Python, which is often prioritized by data scientists and machine learning developers, there are other programming languages used to build projects with artificial intelligence and machine learning. Let’s have a quick look at them.
R is an open-source data visualization-driven language that focuses on statistical computing. It is especially useful when you need to analyze and manipulate data for statistical purposes. R also includes support for object-oriented programming and finds popularity in a machine-learning environment. Contrary to other languages, R is mainly popular with non-programmers, including analysts, data miners, and statisticians. It supports not only a command line but also provides multiple tools to manage your libraries or to draw better graphs.
While it may not be as flexible and versatile as Python, when it comes to data exploration and prototyping, this language truly shines. In machine learning, R is often used as a supplementary tool to support other languages. It is well-suited to any machine-learning task that depends on statistics.
Like R and Python, Java is an object-oriented language. It is a solid multi-purpose programming language, which allows you to create applets for web pages, large-scale enterprise systems, and apps for the Android mobile platform. Java also has several machine learning libraries and tools, such as Weka or Massive Online Analysis (MOA).
Even though Java is great for creating complex, large-scale ML algorithms, it is vulnerable to bad practices that can make the entire code unstable. And because of its complexity and multiple implemented policies, coding can be significantly slower for Java developers. In comparison with Python, it is just not the most suitable tool for machine learning.
First released in the 1980s, C++ is a general-purpose programming language. It is quite complex to learn but has proven popular for performance-critical jobs. The language has expanded significantly over time and now has object-oriented and functional features. Because of its syntax, C++ is easily readable by the machine and gained popularity in game development. The fast execution and delivery speed make it rather good for machine learning.
But unlike Python, C++ is harder to learn and use in practice. Given the complexity of machine learning algorithms, a more simple and easy-to-read language such as Python is preferable for developers.
To sum it up
Artificial intelligence and machine learning are one of the most demanded technologies today so developers need to use the most suitable tools to work on them. And while there are new languages and frameworks emerging on a regular basis, Python remains the number one choice because of its undoubted benefits.
Q: What is machine learning?
A: Machine Learning is a branch of Artificial Intelligence that allows machines to accurately draw forecasts and make predictions based on historical data. In simple words, machine learning is aimed at teaching machines how to “think” in a way humans do by defining relationships and dependencies between the data and making assumptions based on that.
Q: Why use Python for machine learning?
A: Python is so widely used in machine learning due to a number of reasons. First, a lot of the most popular ML tools were originally developed in Python and hence are 100% compatible with ML projects. Second, Python is very concise, easy, and feature-rich so it makes the development process highly convenient.
Q: What is Python?
A: Python is a high-level, object-oriented programming language that is perfect for beginners because of its simplicity. Nowadays, it is one of the most popular languages with a large community. Python can be used for a large variety of development tasks and provide support for many functions and external toolkits. Fun fact, its name is a tribute to the British comedy group Monty Python!