Python packaging tutorial
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The class has a property named members — which is a list of some mammals we might be interested in. For example, if you have two draw modules with slighty different names - you may do the following: game. In order to deal with the tasks of distribution, Python distribution utilities toolset distutils was created.
It also must not already taken on pypi. Imagine you have an application that needs version 1 of LibFoo, but another application requires version 2.
After completing this tutorial, you will gain a broad picture of the machine learning environment and the best practices for machine learning techniques. The X to the right of the package uninstalls it. For example, when building a ping pong game, one module would be responsible for the game logic, and another module would be responsible for drawing the game on the screen. They are also run if the file is executed as a script. Structure of Python Packages As we discussed, a package may hold other Python packages and modules. You can also change the font style.
Python GUI examples (Tkinter Tutorial) - Second, it does not check the cache if there is no source module.
Python is a general-purpose high level programming language that is being increasingly used in data science and in designing machine learning algorithms. This tutorial provides a quick introduction to Python and its libraries like numpy, scipy, pandas, matplotlib and explains how it can be applied to develop machine learning algorithms that solve real world problems. This tutorial starts with an introduction to machine learning and the Python language and shows you how to setup Python and its packages. It further covers all important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, python packaging tutorial and model performance evaluation. This tutorial also provides various projects that teaches you the techniques and functionalities such as news topic classification, spam email detection, online ad click-through prediction, stock prices forecast and other several important machine learning algorithms. Audience This tutorial has been prepared for professionals aspiring to learn the basics of Python and develop applications involving machine learning techniques such as recommendation, classification, and clustering. Through this tutorial, you will learn to solve data-driven problems and implement your solutions using the powerful yet simple programming language, Python and its packages. After completing this tutorial, you will gain a broad picture of the machine learning environment and the best practices for machine learning techniques. Prerequisites Before you start proceeding with this tutorial, we assume that you have a prior exposure to Python, Numpy, pandas, scipy, matplotlib, Windows and any of the Linux operating system flavors. If you are new to any of these concepts, we python packaging tutorial you to take up tutorials concerning these topics, before you dig further into this tutorial.
