When it comes to coding, Python happens to be one of the most popular languages. It is true that there are alternatives, but Python has been steadily growing in terms of its usability.
At the same time, it is important to note that while Python is popular, it still has some downsides, such as performance and a somewhat disorganized build system.
Regardless, these cons can be overcome, and Python offers more than enough for its users, particularly if they are working on something related to machine learning.
The purpose of this article is to cover some of the best Python libraries for machine learning. Let’s start with Caffe.
Just to be clear, this name has nothing to do with coffee. The acronym stands for Convolutional Architecture for Fast Feature Embedding. Quite a mouthful, right? It is natural that an abbreviation makes more sense. The idea behind Caffe is to have a library that lets you classify images and do other related tasks. It helps to have such a library for sorting so that you do not have to spend time searching on a mac or another computer you use and struggle to find the necessary images and other files.
Originally, Python was not developed with machine learning in mind despite having solid data structures and types. Numpy comes in handy because it lets coders take care of vast multi-dimensional data sets that come with equations.
Other than processing large data, Numpy is also rated for its efficiency in handling vectorization. However, the library uses the MATLAB style, meaning that it takes a while to familiarize yourself with it to get the hang of how Numpy works.
A simple library (as far as Python goes) that lets users manage 2D and 3D. As the name suggests, the library is open-source, and it is actively developed not just because of the original creators but users who chip in now and then.
If you need to scrape information from websites or create crawlers for scraping, Scrapy is the go-to library. It has also introduced a new feature recently that allows users to connect to APIs and gain access to data.
Available on Apache MXNet, Gluon is a library developed by Microsoft and AWS. The purpose of using Gluon is to have an information-rich library for learning. Learning to utilize Gluon should not take too long given how intuitive and consistent it is.
Built on PyTorch, FastAi is used to create neural networks. Thanks to its consistency in APIs and integrated support for visual content and text, FastAi is one of the best libraries and excels at what it does.
Explain like I am 5 years old might not seem like the aptest name for a Python library, but it is exactly what ELI5 stands for.
While some algorithms used in Python are pretty self-explanatory, it does not apply to every algorithm. More complicated equations require extra tools to figure out. ELI5 debugs classifiers and offers explanations for certain predictions.
Microsoft also developed LightGBM. GBM stands for gradient boosting machines, and the library is mostly used to identify node splits. Some users also take advantage of LightGBM to boost their computer’s GPU performance by utilizing them.
You might encounter XGBoost, a library that is similar to LightGBM. While the two are not that distinguishable, LightGBM is not as resource-heavy, particularly when it comes to memory usage.
Whether that is something you care about comes down to personal preference, but having a lower-end computer to code on might make you pay more attention to how many resources are dedicated to the tools you are using.
Pandas is built on Numpy, and it is one of the best solutions when it comes to data manipulation. Whether you are developing a website or doing some other work, you need to pay attention to the numbers or different information.
By using Pandas, you can manipulate data by incorporating it from different sources, such as SQL databases or Excel. There are features to split frames and series, manage values, add automated updates, and so on.
Another library built on Numpy, SciPy is one of the most commonly used libraries. Users rely on it to create and manage modules and algorithms for linear algebra, image optimization, interpolation, and statistics, to name a few.
So there you have it—the list of some of the most prominent and recognized libraries for Python’s machine learning. Keep in mind that this list is not the be-all and end-all. There are plenty of other valuable libraries that offer various features. Not to mention the development of even more libraries thanks to how popular Python and machine learning are becoming.