Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.

Core functionality only (basic viewsets & schema classes): Database tools: Let's create a viewset for managing content posts. It will provide full kit of actions with simple mock data: Pseudocode…

ticketutil is a Python module that allows you to easily interact with various ticketing tools using their REST APIs. Currently, the supported tools are JIRA, RT, Redmine, Bugzilla, and ServiceNow. Al…

The hub-rest-api-python provides Python bindings for Hub REST API. Introducing the new Client class. In order to provide a more robust long-term connection, faster performance, and an overall better …

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The examples above use the wallet private key when creating the Client to specify which wallet to interact with. IDEX v3 supports the Mumbai testnet as a sandbox to test functionality. For other toke…

Pygerrit2 is tested on the following platforms and Python versions: Support for Python 2.x is no longer guaranteed. To install pygerrit2, simply: This simple example shows how to get the user's open …

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02. The code will look like the following: In order to work with the …

A very simple wrapper for Firebase's REST API. Install python-firebase using pip: or with easy_install (not recommended): Then simply import firebase at the top of your python script: and then instan…

CLAM allows you to quickly and transparently transform your Natural Language Processing application into a RESTful webservice, with which both human end-users as well as automated clients can interac…