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.
What Is a Python Machine Learning Library?
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.
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.
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++.
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.
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:
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:
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:
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:
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:
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:
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:
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:
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:
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: