9 Best Python Libraries for Machine Learning

Key takeaways

• Top Python libraries for machine learning include NumPy, Scikit-learn, TensorFlow, and Pandas, among others.

• A machine learning library for Python includes collections of code and functions that you don't need to write from scratch for your projects.

• Some Python machine learning libraries like TensorFlow and Pytorch help you train models, while other libraries like Numpy and Pandas help you clean data for training.

TensorFlow is a Python library for machine learning that allows you to develop machine learning models. Learn more about Python libraries, and explore nine that are best for machine learning. Afterward, if you want to learn to build and train deep learning models using PyTorch, consider enrolling in DeepLearning.AI's PyTorch for Deep Learning Professional Certificate. In as little as two months, you could learn to optimize model accuracy, performance, and efficiency for deployment.

What is a Python library?

Python libraries are collections of modules that contain useful code and functions, eliminating the need to write them from scratch. There are tens of thousands of Python libraries that help machine learning developers, as well as professionals working in data science, data visualization, and more.

Python is the preferred language for machine learning because its syntax and commands are closely related to English, making it efficient and easy to learn. Compared with C++, R, Ruby, and Java, Python remains one of the simplest languages, enabling accessibility, versatility, and portability. It can operate on nearly any operating system or platform.

9 best Python libraries for machine learning

If you’re working with machine learning and deep learning projects, there are thousands of Python libraries to choose from, and they can vary in size, quality, and diversity. Here is a curated list of the best Python libraries to help you get started on your machine learning journey. This list is based on popularity, derived from their reputation among Python library users.

1. NumPy

NumPy is a popular Python library for multi-dimensional array and matrix processing because it can be used to perform a great variety of mathematical operations. Its capability to handle linear algebra, Fourier transform, and more makes NumPy ideal for machine learning and artificial intelligence (AI) projects, allowing users to manipulate the matrix to easily improve machine learning performance. NumPy is faster and easier to use than most other Python libraries.

For an introduction to Python libraries like Numpy, SciPy, Matplotlib, and Seaborn, check out the University of Michigan’s Statistics with Python Specialization.

2. Scikit-learn

Scikit-learn is a very popular machine learning library that is built on NumPy and SciPy. It supports most of the classic supervised and unsupervised learning algorithms, and it can also be used for data mining, modeling, and analysis. Scikit-learn’s simple design offers a user-friendly library for those new to machine learning.

3. Pandas

Pandas is another Python library that is built on top of NumPy, responsible for preparing high-level data sets for machine learning and training. It relies on two types of data structures, one-dimensional (series) and two-dimensional (DataFrame). This allows pandas to be applicable in a variety of industries, including finance, engineering, and statistics. Unlike the slow-moving animals themselves, the pandas library is quick, compliant, and flexible.

4. TensorFlow

TensorFlow’s open-source Python library specializes in what’s called differentiable programming, meaning it can automatically compute a function’s derivatives within a high-level language. Both machine learning and deep learning models are easily developed and evaluated with TensorFlow’s flexible architecture and framework. TensorFlow can be used to visualize machine learning models on both desktop and mobile.

DeepLearning.AI's project-based TensorFlow: Advanced Techniques Specialization is perfect for anyone ready to deep-dive into applied machine learning. Customize ML models with four hands-on courses.

5. Seaborn

Seaborn is another open-source Python library based on Matplotlib (which focuses on plotting and data visualization) but featuring Pandas’ data structures. It is often used in ML projects because it can generate plots of learning data. Of all the Python libraries, it produces the most aesthetically pleasing graphs and plots, making it an effective choice if you also use it for marketing and data analysis.

6. Theano

Theano is a Python library that focuses on numerical computation and is specifically made for machine learning. It is able to optimize and evaluate mathematical models and matrix calculations that use multi-dimensional arrays to create ML models. Theano is almost exclusively used by machine learning and deep learning developers or programmers.

7. Keras

Keras is a Python library that is designed specifically for developing neural networks for ML models. It can run on top of Theano and TensorFlow to train neural networks. Keras is flexible, portable, user-friendly, and easily integrated with multiple functions.

Practice your Python skills independently with tutorials

Not ready to commit to a course or a boot camp yet? You can read step-by-step guides for troubleshooting Python basics like syntax, if-else statements, exceptions, and working with loops in Coursera's free programming tutorials.

8. PyTorch

PyTorch is an open-source machine learning Python library based on the C programming language framework, Torch. It is mainly used in ML applications that involve natural language processing or computer vision. PyTorch is known for being exceptionally fast at executing large, dense data sets and graphs.

Read more: TensorFlow or PyTorch: What’s the Difference?

9. Matplotlib

Matplotlib is a Python library focused on data visualization and primarily used for creating beautiful graphs, plots, histograms, and bar charts. It is compatible with plotting data from SciPy, NumPy, and Pandas. If you have experience using other types of graphing tools, Matplotlib might be the most intuitive choice for you.

Machine learning libraries for other programming languages

In artificial intelligence and machine learning, some languages are more widely used than others. While Python is particularly popular, Java and C++ are also often used. Additional machine learning libraries you should consider include:

Deeplearning 4j: 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.

Caffe: 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 60 million images per day. Caffe also gives users access to various types of neural networks, including convolutional neural networks.

Explore free resources for Python programmers

Subscribe to Career Chat on LinkedIn to stay current with the latest trends in your career field. Then, continue your learning journey with Python with our other free digital resources:

• Watch on YouTube: How Long Does It Really Take to Learn Python?

• Bookmark a cheat sheet: Python Glossary: Key Terms & Definitions

• Hear from an expert:AI Problem-Solving Tactics: Expert Insights from Vanderbilt’s Dr. Jules White

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