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python-timbl is a Python extension module wrapping the full TiMBL C++ programming interface, exposing all functionality of the C++ interface to Python scripts for prototyping TiMBL-based applications. It was originally developed by Sander Canisius, with a 2013 release by Maarten van Gompel, and since 2020 supports only Python 3. It is distributed under the GNU General Public License.
A tutorial project for applying machine learning in Python with scikit-learn, a Python module built on numpy and scipy. It aims to teach practical application of scikit-learn algorithms; the stated audience is experienced Python developers familiar with numpy and scipy. Prebuilt PDF and HTML versions, tutorial source and exercise solutions are available, and the material is under a Creative Commons Attribution 3.0 license.
Python is a programming language whose official site provides downloads, documentation, a beginner's guide and news. The site lists application areas including web development, GUI development, AI and machine learning, scientific and numeric computing, software development and system administration. The Python Software Foundation supports the language and its community.
python-frog is an open-source Python library hosted on GitHub providing bindings to the Dutch NLP tool Frog, which supports part-of-speech tagging, lemmatisation, named entity recognition, morphological analysis, shallow parsing and dependency parsing.
Talos is a Python package for running parameter sweeps for Keras, TensorFlow and PyTorch models. Experiments can use the Python Scan interface or a single-file definition run through Python or the CLI. The Talos 2 generation is in active maintenance, and the Talos 1.x series will remain supported at least until 2028.
automl-gs is a Python 3 AutoML module that takes an input CSV and a target field and generates a trained machine learning or deep learning model plus native Python code pipelines. It provides a zero code/model definition interface and performs random hyperparameter searches, reporting metrics for each trial in a CSV.
Open-source Python library for data-centric AI that helps clean data and labels by automatically detecting issues in machine learning datasets. It uses existing models to estimate dataset problems that can be fixed to train better models. It runs on Python 3.10+ and works with any model across classification, regression, segmentation and object detection tasks.
NALP is a Python library covering Natural Adversarial Language Processing. It combines natural language processing and adversarial learning, from embeddings to neural networks. The package offers corpus loading for raw text and audio, encoders, and pre-loaded neural network models. Version 3.0.2 requires Python 3.11+.
nbshare.io is a platform for learning data analytics using Python and R. It hosts a collection of notebooks covering topics including artificial intelligence, machine learning, basic Python coding, data analytics, web scraping and other related technical subjects.
PyBrain is a Python machine learning library distributed under a BSD-3-Clause license. The repository includes documentation, examples, and a setup script; installation requires SciPy and is performed with python setup.py install or by cloning the repository. The project provides a quickstart, tutorials, and an API reference in its documentation.
PyBroker is a Python framework for developing algorithmic trading strategies with a focus on machine learning. It provides a backtesting engine built in NumPy and accelerated with Numba, model training and backtesting using Walkforward Analysis, parameter optimization with Optuna, and access to historical data from Alpaca, Yahoo Finance, and AKShare. It supports Python 3.11+ on Windows, Mac, and Linux.
A hands-on, project-based introduction to programming with Python by Eric Matthes, published in its third edition in December 2022. The book covers basic concepts such as variables, lists, classes, and loops, and guides readers through projects including a Space Invaders-inspired arcade game, data visualizations, and a simple web application.
An interactive tutorial and introduction to programming with Python for the humanities by Folgert Karsdorp. The book has 10 chapters covering text processing, preprocessing, programming principles, readability computation, authorship attribution, object-oriented programming, information retrieval, a PDF search web application, classification and hierarchical clustering. It expects no prior programming experience. Chapters 5 and 6 are in draft status.
A Python wrapper around the ZPar statistical natural language parser, providing a ctypes-based interface and an XML-RPC ZPar server for batch processing. It works only with English ZPar models and supports 64-bit Linux and OS X. The repository was archived on Sep 13, 2022 and is read-only; the project is no longer under active development.
BayesO is a Python framework for Bayesian optimization, described as a simple but essential Bayesian optimization package. It is installed via pip and is tested on Python 3.8 through 3.12. The repository includes documentation, examples and tests, and is released under the MIT License.
DEAP is an evolutionary computation framework for Python aimed at rapid prototyping and testing of ideas. It provides genetic algorithms over representations such as lists, arrays, sets, dictionaries, trees and Numpy arrays, genetic programming with prefix trees, evolution strategies including CMA-ES, multi-objective optimisation, and co-evolution of multiple populations. It supports parallelisation via multiprocessing and SCOOP and works out of the box with Python 3 since version 0.8.
Archived, read-only GitHub repository of free resources for learning machine learning with Python and Jupyter Notebook, including notebooks, courses and other links. It was first posted in 2016 and was archived by the owner on Jun 27, 2022.
Fiber is a Python-based distributed computing library for modern computer clusters. It provides the same API as Python's standard multiprocessing library, uses Nanomsg for communication, and allows dynamic allocation of CPU, memory, and GPU resources. Installation uses pip install fiber.
FLAML is a lightweight Python library from Microsoft for automating machine learning and AI operations. It performs model selection and hyperparameter optimization under resource constraints, including tuning foundation model inference hyperparameters. It requires Python 3.10 or later, is installable via pip, and can be used as a scikit-learn style estimator.
igraph is a collection of network analysis tools emphasizing efficiency, portability and ease of use. It is open source and free, and can be programmed in R, Python, Mathematica and C/C++. Implementations include the igraph R package, python-igraph, IGraph/M and the igraph C library.
A GitHub repository holding the notebooks and code for the book "Introduction to Machine Learning with Python" by Andreas Mueller and Sarah Guido, together with the mglearn library of helper functions for figures and datasets. The book requires scikit-learn 0.20.0, and the code also needs numpy, scipy, matplotlib, pandas and pillow.
Notebooks is a Docker-based starter kit for machine learning through Jupyter notebooks, intended for users who want a ready runtime environment. Its images cover combinations of Keras, TensorFlow and PyTorch with CUDA v8 through v11, and include JupyterLab, scikit-learn, pandas and visualization, vision and NLP libraries. Python 3 only, as Python 2 is end-of-life.
pkuseg is a Python toolkit for multi-domain Chinese word segmentation, based on a 2019 paper. It provides pretrained models for news, web, medicine, tourism, and mixed domains, and supports user-trained models and part-of-speech tagging. It is distributed under the MIT license.
A Python library for implementing a Recommender System.
