A Python machine learning library is a library of functions and methods that enables easy scaling of scientific and numerical computations to streamline machine learning workflows. This article explains the fundamentals of a Python machine learning library and reveals the top 10 Python machine learning libraries used in 2022.

A Python machine learning library encompasses functions and methods that enable easy scaling of scientific and numerical computations to streamline machine learning workflows. It is a framework allowing developers to design ML models in less time without needing to get into the intricacies of base algorithms.

With a Python-based machine learning library, developers and data science professionals can accomplish complex tasks without rewriting lengthy pieces of code. In simple words, this machine learning library offers an easier way to define, build, and deploy machine learning models with the help of pre-built library components that simplify machine learning and are developer-friendly.

Today, with technological advancement, several AI, ML, and deep learning applications tend to use a Python machine learning library. Such extensive use of Python libraries relates to the efficiency with which Python makes AI-based applications more scalable and extensible. It provides plenty of built-in libraries and packages that facilitate faster application development.

Python is one of the most popular and fastest-growing programming languages that outperforms several other languages such as PHP, C#, R language, JavaScript, and Java. According to a Feb. 2022 report published by Statista, Python is the third (48.24%) most commonly used programming language by developers across the globe.

Owing to its simplicity and readability, programmers prefer Python over other languages. Moreover, when engineers intend to develop intelligent algorithms that machines can interpret, Python takes the top spot explicitly.

Key benefits of using a Python machine learning library

Here are the key benefits of using a Python machine learning library, making it a prevalent choice.

1. Simple to learn: The Python language offers descriptive and interactive code that is easy to learn, interpret, and understand. The understandable language makes it suitable for beginners. Moreover, the simplicity of the Python library allows programmers to design reliable systems.

2. Platform-independent: Python is a platform-independent language. This implies Python can run programs on platforms such as Linux, Windows, and macOS without requiring a Python interpreter on respective operating systems.

3. Free and open-source: Python libraries are free and open-source. This makes them open to constant improvements and updates.

4. Exhaustive libraries: A python library provides a wide array of libraries that allows users to address every existing problem.

5. Community support: A Python library is easy to implement and integrate with other tools. Moreover, the library is accessible to any individual and does not require particular skills. The community makes Python library implementation easier for beginners as the members within the community share, discuss, and resolve issues quickly.

6. Reduces coding & debugging times: The Python library enhances the overall productivity of application development as it uses pre-compiled codes, thereby reducing coding and debugging times significantly.

7. Applications: Python libraries find applications in soft computing and natural language processing.

8. C and C++ integration: Python libraries are easy to integrate with other language modules such as C and C++.

See More: What Is Artificial Intelligence (AI) as a Service? Definition, Architecture, and Trends

Top 10 Python Machine Learning Libraries in 2022

The Python programming language is a popular choice for most professionals and entrepreneurs who intend to develop data science projects, ML-based systems, or add ML functionalities to existing software products. It allows users to design quality ML models, quickly employ them in the production process, and start collecting results for the deployed models.

Now that we know the benefits and value of a Python library to machine learning, let’s dive into the top 10 Python machine learning libraries in 2022.

1. TensorFlow

TensorFlow is a free and open-source library that is used for numerical computations. The Google Brain research team developed it in 2015. It offers an exhaustive math library suitable for neural network applications and large-scale systems. The library supports probabilistic methods such as Bayesian models by providing access to several distribution functions like Bernoulli, Chi2, Gamma, and others.

TensorFlow processes data at high speeds and accuracy. It is typically suitable for parallel processing applications and distributed computing. Advantages of TensorFlow include scalability, better graphical visualizations, frequent updates and feature releases, seamless library management and compatibility with GPU, ASIC, etc.

TensorFlow is used extensively by companies such as Airbnb, Airbus, PayPal, VSCO, and Twitter.

Critical reasons for choosing TensorFlow include:

• Resourceful: TensorFlow offers many tools and resources that allow engineers to build and deploy ML-based systems with ease.
• Deployment on several platforms: TensorFlow enables the execution of machine learning models on many platforms, be it cloud, on-premise, browser, or even on-device deployment.
• Strong tech support. As TensorFlow is subject to frequent updates with timely new feature releases, the library can meet the changing demands of development teams. For example, Google recently launched ‘TensorFlow Enterprise‘ which provides long-term support for the development of ML solutions.

2. PyTorch

PyTorch is a free and open-source library typically used for computer vision and natural language processing applications. The library was developed by Facebook’s AI research group and adopted by companies such as Microsoft, Walmart, Uber, and Facebook. Moreover, PyTorch is used to build several deep learning software, such as Uber’s Pyro, which is used for deep probabilistic modeling.

Some of the key reasons why PyTorch is a popular ML library include:

• Easy prototyping: The model developed through PyTorch is most suited to handle fast-paced projects that need high development speed.
• Handles large data volumes: Several ML systems can handle vast data volumes and still provide optimized performance with the aid of the PyTorch library.
• Cloud accessibility: PyTorch can be accessed on any device, from anywhere, and at any point in time without requiring any specific hardware or software. Also, the library promotes easy scaling by offering its availability on cloud platforms such as Amazon Web Services, Alibaba Cloud, Microsoft Azure and Google Cloud.

3. Keras

Keras is an open-source and standalone Python ML library suitable for neural network computations. Keras extends support to convolutional and recurrent neural networks, apart from standard neural nets. The library can operate over known frameworks of TensorFlow and Theano. It enables faster experimentation as the library is easy to interpret, modular, and even extensible.

Keras provides a wholesome toolset that makes the handling of image data and text much more efficient. This is why companies such as Uber, Netflix, Square, Yelp, and others prefer Keras over other libraries when it comes to managing image and text data.

Some of the key benefits offered by the Keras library include:

• Fast development process: Keras offers a user-friendly interface that reduces the load on programmers while coding. The API facilitates a speedier development process with reduced stress on the developer community.
• Strong backend support: Keras provides strong backend support, allowing developers to integrate multiple backend components while ensuring system stability seamlessly.
• Modular architecture: With Keras, professionals can reduce development time by opting for ready-made modules and customizing them. Also, the library supports other deep learning languages, libraries, packages, and tools that play a crucial role in improving the performance of the target software product.

4. Orange3

Orange3 is an open-source ML, data mining, and data visualization tool. It was initially developed by researchers at the University of Ljubljana with the help of the C++ language in 1996. In 1997, owing to the growing need for more elaborated modules, professionals started applying Python modules to the previously developed framework.

Key features that highlight the importance of Orange3 include:

• Suitable for predictive modeling: Orange3 is tailor-made for recommendation systems and predictive models where accuracy is the main focus. The library has a toolset that supports algorithm testing and is more relevant to biomedicine and informatics.
• Widget-supported architecture: The Orange3 library supports a widget structure apart from data mining and visualizations. This enables programmers to quickly develop predictive ML models, helping the concerned personnel make appropriate business forecasts.
• Easy to learn: The Orange3 ML library is a part of the educational and professional curriculum. Corporate companies use it in training programs to train their employees, while schools and universities include it in their study syllabus as it is easy to learn and understand.

5. NumPy (Numerical Python)

NumPy is an open-source Python library designed to support scientific and numerical computations. The library has many mathematical functions and allows multi-dimensional array and matrix computations.

The NumPy library has the following benefits:

• Enhanced computing capabilities: The NumPy library allows developers to design and develop intelligent systems capable of giving real-time responses to real-life scenarios. This is due to the enhanced computing capabilities provided by the library, such as handling complex math computations related to Fourier transform, linear algebra, and random number generation.
• Powerful performance: The library supports multi-dimensional arrays, and mathematical functions run on them. This facilitates the faster execution of Python algorithms, thereby improving the overall performance of the target system.
• Community support: When developers run into technical issues, they can share their problems on the NumPy community. Community members can give solutions to specific problems. This offers programmers easy access to solutions that can resolve their issues in no time.

6. SciPy

Like the NumPy library, the SciPy library is suitable for scientific and engineering tasks that predominantly include mathematical computations. The SciPy library is also known to support image manipulation tasks.

Besides these factors, key reasons why Python experts rely on the SciPy library are:

• Enhanced computational speed: SciPy is specialized in handling complex math problems, including numerical interpolation, integration, linear algebra, and statistics. Since the library is designed to manage various math operations, it does so at neck-break speed. As a result, the overall computational rate of ML model development and its integration into the existing system increases significantly.
• Ease of use: The SciPy library is simple and easy to interpret and understand. This makes it user-friendly, thereby enabling professionals to grasp the set of features offered by the library quickly. This consequently speeds up the development process of machine learning models.
• Combination of NumPy and SciPy: As NumPy and SciPy are designed to handle scientific and mathematical scenarios, the combination of these two libraries inevitably improves computations and gives faster results. Fundamentally, SciPy is developed on top of NumPy. As such, it is capable of performing faster calculations, handling a range of arrays, and at the same time, ensuring the quality of operations.

7. Scikit-Learn

Scikit-learn is a free machine learning library that is an efficient data mining and analysis tool. It is built on SpiPy with support for classification, clustering, and regression algorithms. The library ensures that top machine learning algorithms (Support Vector Machines, Random Forest, K-Means, Gradient Boosting) can operate internally with Python-based scientific and numerical libraries.

Today, the Scikit-Learn library is popular on GitHub and is used by companies across varied platforms such as online music streaming (Spotify), accommodation bookings (Booking.com), and dating sites (OkCupid).

Scikit-Learn is a simple ML library that is fast, easy to use and has a user-friendly API. Moreover, the library has powerful tech support with technical documentation. It also has a developer community that can help when a user encounters problems while using the library.

Key advantages of the Scikit-Learn library include:

• Suitable for handling classical ML algorithms: Typical classical algorithms include classification, regression, and clustering algorithms. These algorithms are used to address specific applications that improve the overall accuracy of computations. For example, applications include customer segmentation (clustering), spam detection (classification), and prediction-making systems (regression).
• Easy integration with SciPy tools: The Scikit-Learn library complements the already existing set of scientific and numeric Python libraries. It integrates easily with other libraries and helps improve the features and functionality of the target software product.

8. Pandas

Pandas is primarily designed to perform data manipulation and analysis. It is known that dataset preparation is essential before the training phase. The Pandas library comes in handy in such a scenario as it provides a variety of data structures, functions, and components that help in data extraction and preparation tasks. Data preparation refers to data organization, wherein various methods are employed to a group, combine, reshape, and filter out different datasets.

Key advantages of the Pandas library include:

• Valid data frames: While the Pandas library has more utility for data analysis, it is also used to handle machine learning operations through data frames. Data frames refer to two-dimensional data similar to what is used in SQL tables or spreadsheets. It enables programmers to get an overview of the data, thereby improving the software product’s quality.
• Easy dataset handling: The Pandas library is typically helpful for professionals intending to handle (structure, sort, reshape, filter) large datasets with ease.

9. Matplotlib

Similar to Pandas library, Matplolib is not a machine learning heavy library. It is typically used for data visualization where developers can derive insights from the visualized data patterns. Some of its modules, such as Pyplot, provide functionalities to control line styles, manage fonts, and others while plotting 2D graphs and plots.

The features offered by Matplotlib are in line with those of MATLAB, and all the Python packages are freely available in this library.

Key reasons for the popularity of Matplotlib include:

• Wide range of plotting tools: Using the Matplotlib library, plotting various 2D charts, 3D diagrams, histograms, error charts, bar charts, and graphs is possible. It allows experts to perform detailed data analysis.
• Builds reliable ML models: Several plots allow thorough data analysis, which further ensures that the developers have enough relevant data to build reliable ML models.

10. Theano

The Theano Python library manipulates, evaluates, and optimizes mathematical models. It was developed by the Montreal Institute for Learning Algorithms (MILA), University of Montreal, in 2007, to define and execute mathematical expressions. The library uses multi-dimensional arrays to process these expressions.

The library is useful in developing deep learning neural networks, and has several other benefits:

• Handles multiple computations: The Theano library is capable of handling simultaneous computations without hampering their performance. The library also supports code reuse, wherein, certain code snippets are allowed to be reused for similar functions. As a result, ML model development happens at a faster pace.
• Excellent computational speed: The Theano library performs well for tasks requiring faster execution speed for both CPU and GPU architectures. This reduces the development time of an ML model.
• Offers stability: The library can handle unstable mathematical expressions and replace them with stable ones. This results in the development of good quality ML systems.

source: https://www.spiceworks.com/tech/artificial-intelligence/articles/top-python-machine-learning-libraries/