The programming language Python was released in 1991, it is one of the most widely used languages today . It’s efficient and easy to learn, and one of its greatest features is its open-source libraries available for users. The libraries allow users to choose from frameworks that they can build off of to produce new machine learning (ML) models.
Whether you are already familiar with machine learning in your career, or want to get started, this field holds plenty of growth opportunities. In fact, machine learning engineer was ranked #1 best job in 2019, according to Indeed, because it experienced 344 percent job growth between 2015 and 2018 .
Learn more: What Is Python Used For? A Beginner’s Guide
Python libraries are collections of modules that contain useful codes 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.
For an introduction to Python libraries Numpy, SciPy, Matplotlib, Seaborn, check out this video from the University of Michigan’s Statistics with Python specialization:
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.
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.
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.
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.
TensorFlow’s open-source Python library specializes in what’s called differentiable programming, meaning it can automatically compute a function’s derivatives within 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.
This project-based TensorFlow specialization from DeepLearning.AI is perfect for anyone ready to deep-dive into applied machine learning. Customize ML models with four hands-on courses:
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Seaborn is another open-source Python library, one that is based on Matplotlib (which focuses on plotting and data visualization) but features Pandas’ data structures. Seaborn 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’ll also use it for marketing and data analysis.
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.
Keras is a Python library that is designed specifically for developing the neural networks for ML models. It can run on top of Theano and TensorFlow to train neural networks. Keras is flexible, portable, and user-friendly, and easily integrated with multiple functions.
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.
Matplotlib is a Python library focused on data visualization and primarily used for creating beautiful graphs, plots, histograms, and bar charts. It is compatible for 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.
It is true that anybody can learn Python. Start your Python journey today and use Coursera’s free trial to start Python for Everybody from the University of Michigan. You’ll learn to develop programs to clean, analyze, and visualize data.
This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of ...
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How Long Does it Take to Learn Python? (+ Tips for Learning)
1. UC Berkeley. “11 Most In-Demand Programming Languages in 2022, https://bootcamp.berkeley.edu/blog/most-in-demand-programming-languages/.” Accessed June 23, 2022.
2. Indeed. “The Best Jobs in the U.S. in 2019, https://www.indeed.com/lead/best-jobs-2019.” Accessed June 23, 2022.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.