Python is the most popular programming language for data science projects. It has also become the number one choice of many entrepreneurs who want to get ML-based systems or add them to their existing software products. The secret is simple – a lot of machine learning solutions are made with Python because it helps to develop high-quality models, quickly put them into production, and start getting the results.
For long, Python has been competing with R for the title of the main language for scientific programming and currently wins the competition. The number of its auxiliary tools steadily grows, their quality improves, and more specialists prefer to use this language. Consequently, it’s easier to find an experienced Python data scientists rather than a developer using R or any other language. Based on our experience and high popularity of this language, we’ve made a list of 10 most important Python packages for machine learning that help us deliver desired software products to their owners.
Best Python Machine Learning Libraries
Developers consider Python as one of the most efficient general-purpose languages. This language is simple enough to let specialists create almost anything their clients want. With the rise of big data and artificial intelligence, Python’s popularity started to grow in the realm of data-related development as well. Based on our experience in data science projects, we want to highlight our 10 best Python packages for machine learning.
TensorFlow is an open-source numerical computing library for machine learning based on neural networks. In 2015, the Google Brain research team created it to use internally in Google products. Soon, its popularity among businesses has grown, so many startups and mature companies like Airbnb, Airbus, PayPal, VSCO, Twitter, and others started using it in their technology stacks. Several crucial criteria caused this library to get the first place on our list:
- A resourceful library. TensorFlow has a flexible ecosystem of tools and community resources. Such toolsets let software engineers conduct machine learning and deep learning research as well as easily build and deploy ML-powered solutions of high quality;
- Easy deployment of ML models on various platforms. TensorFlow allows putting ML models in production mode across various platforms: in the cloud or on-premises, in the browser or on-device;
- Google support. With every new release, Google introduces a variety of useful tools to meet higher demands and expectations of entrepreneurs and development teams. The evidence of powerful Google’s support is the recent launch of “TensorFlow Enterprise” that allows building machine learning solutions on a large scale.
PyTorch is one of the largest machine learning libraries developed by Facebook’s AI research group. It is generally used for computer vision, natural language processing, and similar complex tasks. This library is a choice of such companies as Facebook, Microsoft, Uber, Walmart, and others. The three major factors put PyTorch on this list:
- The fast path from prototyping to production. The TorchScript model accelerates the speed of development and is especially powerful at handling fast-paced projects;
- Optimized system performance. Many systems rely on PyTorch distributed back-end side to optimize their performance when dealing with a large amount of data;
- Available in the cloud. The library supports Alibaba Cloud, Amazon Web Services, Google Cloud, and Microsoft Azure to ensure easy scaling. It’s accessible from any device at any time and place, so no costs are required for specific hardware or software tools.
Keras was originally a platform for fast experimentation with deep neural networks but soon transformed into a standalone Python ML library. It has a comprehensive ML toolset that helps Netflix, Uber, Yelp, Square, and other companies handle text and image data efficiently. The advantages of the Keras library include:
- User-friendly interface. Keras API was designed in a way to reduce developers’ actions and cognitive load while coding, which ensures a faster development process;
- Multi-backend support. High-level abstractions allow programmers to create and integrate deep learning models within various back-end sides, keeping systems stability high;
- Modular and extensible architecture. Specialists use modules containing ready-to-use patterns that shorten the time of development. Keras is also highly compatible with other libraries, low-level deep learning languages, and third-party tools that help to improve a software product with more useful features.
This software package includes tools for machine learning, data visualization, and data mining. In 1996, the scientists at the University of Ljublijana created it with C++. A year later, specialists started to apply Python modules and widgets actively to develop more elaborated models with ease. The features that make Orange 3 qualify for this top are:
- Powerful prediction modeling and algorithm testing. Orange3 was made specially for making high-accuracy recommendation systems and predictive models. It has a varied collection of tools for testing new ML algorithms in various industries, in particular, biomedicine and informatics;
- Widget-based structure. Widgets include various functionalities for different purposes. Besides focusing on data visualization tasks, they help developers create predictive ML models that provide entrepreneurs with precise business forecasts;
- Ease of learning. Orange3 is included in school, university, and professional training programs because it’s easy to learn and understand. More and more specialists choose this library to deliver ML-powered quality solutions to clients efficiently.
Libraries of the SciPy Stack
The Python’s libraries included in the SciPy stack is a family of packages for scientific and technical computing. Altogether they form a comprehensive toolset for machine learning. Each standalone package here is powerful for specific data tasks but works even better when used in combination with other tools of the stack. The main libraries include NumPy, SciPy, Scikit-Learn, Matplotlib, Pandas, etc.
Python wasn’t initially developed as a tool for numerical computing, based on which machine learning solutions would be built. However, the advent of NumPy was the key to expanding Python’s abilities with mathematical functions. Using this library is beneficial because of:
- Robust computing capabilities. It deals with linear algebra, matrix computations, random number generation, etc. that help developers create smart and responsive systems;
- High performance. The high-level mathematical functions are running on arrays, which makes Python algorithms execute faster;
- Large programming community. Should any issue occur, programmers can turn to the NumPy community and share experience with each other or find a ready-made solution to their issues.
Along with NumPy, this library is a core tool for accomplishing mathematical, scientific, and engineering computations. The three main reasons why Python specialists appreciate SciPy are:
- Fast computational power. SciPy deals with such math operations as numerical interpolation, integration, linear algebra, statistics, etc. in a short time; thus, increasing the speed of ML models development and integration;
- Easy-to-use library. The library is easy to understand, so specialists got quickly familiarized with its set of features and create machine learning models faster;
- NumPy + SciPy = improved computations. SciPy is built on top of NumPy and can operate on its arrays, ensuring higher quality and faster execution of computing operations.
Scikit-learn was initially made as a third-party extension to the SciPy library. Now, it’s a standalone and one of the most popular libraries on GitHub. This library is an indispensable part of the technology stacks of Spotify, Booking.com, OkCupid, and others. Scikit-learn also found a place on our list because it is:
- Great at сlassical machine learning algorithms. The range of traditional ML components here includes classification algorithms for spam detection and image recognition; regression algorithms for prediction-making; clustering algorithms for customer segmentation and similar operations; model selection for improved accuracy of computations, etc.;
- Easily interoperable with other SciPy stack tools. Scikit-learn is an addition to the main numeric and scientific Python libraries. In combination with them, it helps to include more features in a software product and improve the existing ones.
Pandas is a low-level Python library built upon NumPy. Everything started with the AQR financial company that needed help with quantitative analysis of its financial data. For this purpose, in 2008, Wes McKinney, a developer at this company, started the creation of Pandas. Before leaving the company, he convinced the management to make this library open-source. These are the crucial points of why pandas is also on our list:
- Powerful dataframes. Pandas is mostly used for data analysis and manipulation, as well as for machine learning operations in the form of dataframes. By using dataframes, developers can conveniently overview data to ensure a higher quality of the product;
- Flexible data handling. Developers use this library to structure, reshape, and filter large sets of data with ease.
A unity of NumPy, SciPy, and Matplotlib supposed to replace the need to use the proprietary MATLAB statistical language. This fact explains why the functionalities of the mentioned libraries are similar to those of MATLAB. However, Python packages are available for free and more flexible, which makes them a choice of many data scientists. The reason to include Matplotlib in the list is:
- A comprehensive set of plotting tools. Charts, 2D and 3D diagrams, graphs, and other tools for visualization allow scientists to conduct detailed data analysis. Based on the analysis, a programmer can build reliable machine learning models.
At this point, the list of SciPy stack libraries is over. However, there’s still a final position to mention in our rating, which belongs to Theano.
In 2007, the Montreal Institute for Learning Algorithms (MILA) created Theano to manipulate and evaluate various mathematical expressions. Based on these expressions, this Python machine learning library allows building optimized deep learning neural networks. Although Theano isn’t so efficient for ML as TensorFlow, it still has a few undeniable benefits:
- Stable simultaneous computing. Theano handles multiple computations keeping its performance high and allows reusing code pieces for similar functions, which decreases the time of model development;
- Fast execution speed. This library shows high performance on both CPU and GPU architectures, saving the time of development;
- Optimized stability. Theano can define unstable expressions and replace them, ensuring a better quality of systems.
This was our rating of 10 most important Python libraries for machine learning. Considering all positions on this list, it is possible to define three fundamental reasons why data science engineers appreciate them:
- They are open-source. Python libraries are available at no cost. Besides, any member of the Python community can freely share solutions to specific ML tasks with other specialists;
- They are extensive. By using these libraries, developers get a plethora of computational and scientific features for different purposes. All packages can interoperate with each other to allow adding more useful features in a software product and improving the existing ones;
- They ensure faster development and implementation of ML models. When installing a yet unfamiliar library, a skilled Python developer spends minimum time learning how to use it. The intuitive interfaces of the libraries help programmers work more productively, so the development process goes smoother and faster;
- They improve Python’s performance. Compared to the standard Python’s stack, the optimized libraries increase the language performance. Thus, code execution runs quickly, so such packages are great for high-speed solutions that are already under production.