Machine Learning and Artificial Intelligence have been a part of the API conversation for a number of years now. With platforms from a number of big names such as Google Cloud Machine Learning (ML) Engine, Machine Learning at AWS, Azure Machine Learning and many others, businesses have no shortage of tools that can be used to leverage their ever increasing amounts of data. For those working in data science, Python tends to be one of the most popular languages. Eirini-Eleni Papadopoulou explains that this is because “Python’s syntax is the closest to the mathematical syntax and...is most easily understood and learned by professions like mathematicians or economists.” Her article presents the top five Python tools for machine learning with an additional five for data science..
The first tool is Shogun, an open-source machine learning toolbox that is one of the oldest machine learning tools having been released in 1999. Originally used for scientific research, it has made its way into a number of industry applications. It also offers an API to implementation of machine learning algorithms.
Keras is a high level neural networks library written in Python, that is built on TensorFlow. It is considered a tool that allows beginners to quickly build and test neural networks. While it is build on TensorFlow it can run on top of other frameworks such as CNTK or Theano. The third tool is Scikit-learn, a Python based, open source tool for data mining and data analysis. Its features include various classification, regression, clustering, dimensionality reduction, model selection and preprocessing algorithms.
Pattern is a web mining module for Python. It offers tools for data mining, natural language processing, machine learning, network analysis and more. This free tool includes more than 50 examples and 350 unit tests. Theano is an open source, numerical computation library for Python that lets users define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays. It has been around for over a decade and features tight integration with NumPy as well as transparent use of a GPU.
No matter your comfort level with machine learning, the tools listed by Papadopoulou offer a number of features to help you leverage your Python skills so that you can incorporate machine learning in your applications.