Data science is a booming field—in no small part due to the incredible range of excellent open-source machine learning libraries available for both beginners and advanced users to try. As a programming language, Python has not only emerged as a clear frontrunner but has carved out a niche as the leading language for machine learning.
Among the thousands of libraries out there, I’ll look at 16 favorites according to the most recent Stack OverFlow Survey. These curated libraries each cover what every data professional uses in their day-to-day work.
In this article, I’ll first explain what Python machine learning libraries are before diving into 16 of the best python libraries. I’ll look at classics like scikit-learn and PyTorch alongside newer specialized gems like STUMPY and PyMC3.
Here’s what I’ll cover:
- What are Python machine learning libraries?
- 16 Python machine learning libraries
1. What are Python machine learning libraries?
You can think of Python machine learning libraries as pre-built tools and frameworks that you can plug into projects for different uses.
Many of these libraries have been designed to simplify the coding process and machine learning workflows; this makes machine learning much more accessible to both users who may be Python beginners or new to machine learning.
These pre-built tools accelerate the iteration process, which speeds up the time between experimentation (e.g. A/B testing) and deployment to production. Different libraries serve different purposes. You’ll need to “pip install” different libraries based on whether you’re building an image recognition application or recommendation system.
Some libraries are created for specialized tasks: the transformer library is great when you’re working with natural learning processing architectures. Other libraries integrate better with certain tech stacks: scikit-learn works well with facilitating data analysis in pandas and data visualization in plotly.
2. The top 16 Python machine learning libraries
Let’s turn to these libraries, which cover a range of machine learning domains, including deep learning, time-series forecasting, and natural language processing.
I’ve grouped them by five categories: firstly the classic machine learning libraries that everyone knows and loves, then deep learning, forecasting, natural language processing, and statistics and technical computing.
Classic Python machine learning libraries
What’s it good for: Kickstarting machine learning projects with its well-documented and easy-to-use tool.
Scikit-learn is a comprehensive framework for easy predictive data analysis. It’s built on other popular Python ML and data analytics libraries such as NumPy, SciPy, and matplotlib.
It’s beloved for its ease of use and well-designed tutorials that help users implement machine learning and teach concepts such as model selection, parameter tuning, and performance evaluation.
What’s it good for: Helps improve performance in structured data problems with its gradient boosting algorithm.
XGBoost is a gradient boosting library that is well-known for its high speed and performance. It’s used by Kagglers in many competitions as it’s one of the most versatile Python machine learning libraries, suitable for a variety of tasks, including classification, regression, and ranking.
On top of that, XGBoost can run computation in parallel, making it an incredibly fast ML library.
What’s it good for: Great for performing machine learning on large datasets.
Similar to XGBoost, LightGBM is also an efficient gradient boosting framework. It’s known for its speed and is an especially effective library for big data or if the project has resource constraints.
LightGBM grows trees leaf-wise, in contrast to other gradient boosting libraries that grow trees level-wise. While this means that results can be more accurate, it also risks overfitting on smaller datasets.
What’s it good for: A gradient boosting algorithm on decision trees that’s specialized for handling categorical data.
CatBoost is a boosting algorithm known for producing high performance accuracy. It’s the preferred library for categorical data, as it doesn’t require extensive pre-processing or encoding. This makes it easier for users to get up and running with a machine learning model with categorical features directly.
Deep learning Python machine learning libraries
What’s it good for: Dynamic deep learning and research applications.
PyTorch was developed by Meta to build neural networks for research. It’s known for its dynamic computation graph, which builds graphs on-the-fly, in contrast to TensorFlow’s static graphs.
This makes it ideal for tasks that require dynamic inputs and structures such as text processing with Recurrent Neural Networks (RNNs).
What’s it good for: Training large-scale deep learning models and supporting end-to-end machine learning workflows.
TensorFlow was developed by Google Brain to help build and deploy complex machine learning models, including deep learning networks. As suggested by its name, TensorFlow runs on tensors or multi-dimensional data arrays. Computations are represented as graphs, where nodes are operations and edges represent the tensors.
While TensorFlow can be used for a range of ML use cases, it’s best used for neural networks.
What’s it good for: Has a user-friendly interface and comes with pre-trained models, which makes it easy to prototype deep learning models quickly.
Keras is a high-level neural network API that makes it easier for data scientists to build neural network models. It has been integrated into TensorFlow. Keras models are built using building blocks, like layers, optimizers, and activation functions.
This modular approach makes building machine learning models very accessible. Models can be built, trained, and evaluated with very little code required.
What’s it good for: Simplifies the process of building and training deep learning models with minimal code.
FastAI was created to make state-of-the-art machine learning techniques more accessible to use. Because minimal code is required, advanced techniques are more accessible to Python beginners.
It’s a deep learning library built on top of PyTorch with a focus on practical application and rapid prototyping.
Time-series forecasting Python machine learning libraries
What’s it good for: Analyzing very long time-series data more efficiently.
STUMPY is one of the newer Python machine learning libraries out there. It computes matrix profiles, a novel data structure that can be used to identify patterns like anomalies in time-series data.
Because it’s designed for scalability, STUMPY can handle very long time-series data. The library has a simple API design for easy application to a broad range of projects.
What’s it good for: If your time-series dataset has strong seasonal patterns, use this library for forecasting.
Prophet was released by Facebook (now Meta) back in 2017 as a better forecasting library for irregular time-series patterns. It works by taking into account trends, seasonality, and holiday effects.
As Prophet adopts sklearn’s API, most data analysts will be able to breeze through their quick start tutorial and start using it in their own projects.
Natural language processing (NLP) libraries
What’s it good for: Draw from more than 150,000 pre-trained models for natural language processing, computer vision, and more.
The transformers library, which includes popular models like BERT, RoBERTa, and GPT-2, also contains NLP datasets. This makes it easy for users to run experiments quickly with ready-made datasets and state-of-the-art models.
What’s it good for: If your NLP project optimizes for speed, spaCy offers fast performance for production-ready use cases.
spaCy bills itself as an “industrial strength” NLP library and its competitive edge arises from being built in Cython. spaCy also offers pre-trained models and word vectors for different languages. The API’s design makes it easy to create custom processing pipelines for machine learning workflows.
What’s it good for: A scalable Python machine learning library for vector space and topic modeling, best used for datasets too large to fit into memory.
Gensim specializes in processing large datasets. It uses Latent Dirichlet Allocation (LDA) for topic modeling and document clustering. Gensim works well with sparse matrices, which reduces storage requirements and enables more efficient computation. This comes in handy for text processing tasks which usually involve very large datasets.
Statistics & technical computing Python machine learning libraries
What’s it good for: A statistics library that allows for data exploration, model development, regression analysis, and hypothesis testing.
statsmodels is a suite of tools that enables hypothesis testing and statistical model building, and which integrates well with other Python data packages. Its users include researchers, economists, and social scientists, as statsmodels can be used for more advanced analysis (checking heteroscedasticity, autocorrelation, and multicollinearity).
What’s it good for: As its name suggests, SciPy is best leveraged for scientific computing and is the go-to library for advanced use cases.
SciPy was built on the NumPy library to offer more advanced functions for scientists and engineers. It can be used for eigenvalue problems, algebra, optimization, signal and image processing, statistics, ordinary differential equation solvers, and more.
SciPy also supports sparse data and its efficient computation. It can be used with other Python libraries for data visualization and transformation.
What’s it good for: For advanced users who understand probabilities programming, or for individuals looking to understand uncertainty in data analysis.
PyMC3 is a popular library that implements Bayesisan statistical modeling in Python. It supports a wide range of numerical methods for approximating posterior distributions, including Markov Chain Monte Carlo (MCMC) methods.
Like many of the Python machine learning libraries in this article, it integrates well with other Python data libraries to create end-to-end machine learning pipelines.
Whether you are looking to start with building and training models, data visualization, or more advanced ways to optimize your text data for big data processing, these Python machine learning libraries will serve as helpful tools to draw on in your journey into machine learning.
If this area of data interests you but you don’t know where to start, why not try CareerFoundry’s Machine Learning with Python Course? It covers the basics of machine learning as a field through hands-on projects with an expert mentor, and will give you a good idea of whether or not it’s a career path you’re interested in pursuing further.
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