Popular Machine Learning Libraries to Know

Written by Coursera Staff • Updated on

Learn more about the top machine learning libraries used in artificial intelligence and the programming languages you can use to access them.

[Featured Image] An employee sits at a laptop and does research in various machine learning libraries for their job.

Machine learning is a subcategory of artificial intelligence (AI) that allows computers to use algorithms to process data and recognize patterns and trends. In doing so, computers can “learn” over time. Although machine learning models can’t mirror the creativity that the human brain is capable of, their computational power far exceeds our abilities. Machine learning models can also operate independently without the need for human intervention.

The insights provided by machine learning models can help businesses and organizations in several areas, such as decision-making processes, customer service, personalizing offerings for consumers, fraud detection, prediction development, and more. Machine learning's benefits span a wide variety of industries, with applications in health care, finance, government, transportation, education, and manufacturing.

In this article, you will learn about the machine learning libraries you can use to help build powerful machine learning models for different purposes.

What is a machine learning library?

When writing a program, rather than beginning with a blank slate each time, programming libraries allow you to start out with some of the code already written for you. This ability means that rather than spending time working on the necessary but basic algorithms, you can instead focus your efforts on the more complex details of the project.

Programming libraries come in a number of different forms depending on the specific task you are working on, including building machine learning models. Ultimately, machine learning libraries exist to help simplify this process, making it easier for you to develop and train a working model. When selecting a machine learning library, you should consider several factors, such as the scale of the project, whether you’re using structured or unstructured data, and if your model will include deep learning and neural networks, as well as the language you are programming in.

Programming languages with machine learning libraries

In artificial intelligence and machine learning, some languages are more widely used than others. Popular programming languages in this space offer several machine learning libraries. Examples include Java, C++, and Python. The following provides a closer look at these three languages:

  • Java: Java is an object-oriented programming language that can run on several different types of operating systems, including Windows and MacOS. In addition to having specific libraries for machine learning, other features that make Java a good option for machine learning tasks are its robust security, fast run time, and scalability.

  • C++: While applications running on C++ are highly efficient, writing code in C++ is more challenging than other languages. However, with an extensive machine learning library available, you can still reap the benefits of this powerful language.

  • Python: Widely used for machine learning, Python is a beginner-friendly language with numerous machine learning libraries to assist with areas such as data analysis, computer vision, natural language processing, and more.

Other programming languages of choice for machine learning include Julia, Lisp, R, and JavaScript.

Machine learning libraries

Utilizing machine learning libraries is useful when developing machine learning models, saving you time while writing code and debugging and allowing you to create several types of applications. You have several options available to you based on the specific needs and purpose of the machine learning model you’re building and the programming language you’re using.


An open-source machine learning library, you can use TensorFlow to build machine learning models, specializing in developing neural networks and deep learning applications. Although initially designed for Python, you can access TensorFlow when programming in Python, C++, and JavaScript. Another benefit of TensorFlow is its scalability, allowing you to develop sizable applications.


Keras is an expansion of TensorFlow, with users of TensorFlow getting additional benefits from Keras for machine learning projects. With TensorFlow's scalability, Keras prioritizes deep learning, assisting you in all stages of building your machine learning model, from the initial data processing to implementation.


If you work with Java but are looking for a machine learning library that will integrate smoothly with Python, Deeplearning4j is an excellent option. Deeplearning4j also allows you to work with unstructured data and is useful for retraining models. Some specific applications you can build with this machine learning library include image recognition and recommender systems.

Sci-Kit Learn

A machine learning library for Python that is also accessible with C++, Sci-Kit Learn is excellent when working with standard machine learning algorithms, including clustering, linear and logistic regression, and classification. You can use Sci-Kit Learn alongside other Python machine learning libraries as well, in addition to offering a large community you can turn to for support if you ever run into problems.


Accessible in C++, Caffe is an efficient machine learning library that allows you to solve your machine learning problems quickly. While you can use Caffe for a variety of purposes, it’s especially effective for image classification, processing sixty million images per day. Caffe also gives users access to different types of neural networks, such as convolutional neural networks.


The PyTorch machine learning library allows you to use Python to build deep learning models. One reason for PyTorch's popularity among machine learning professionals is its support for prototyping, allowing you to quickly experiment with different ideas. Another feature of PyTorch is its ability to support C++ application development. Use cases for PyTorch include language processing and image recognition.

Getting started with Coursera

On Coursera, you can find highly-rated courses to help you learn more about machine learning and develop your programming skills. Learn more about different machine learning algorithms and identify the right time to use each, in addition to using Python libraries such as Sci-Kit Learn, with IBM's Machine Learning with Python.

Another great option is the Introduction to Machine Learning course from Duke University. With this course, you'll learn more about machine learning models and their practical applications. You will also learn how to use the PyTorch machine learning library for implementing algorithms. 

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