Classification Trees in Python, From Start To Finish
Create Classification Trees in Python
Apply Cost Complexity Pruning in Python
Apply Cross Validation in Python
Create Confusion Matrices in Python
In this 1-hour long project-based course, you will learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with Python and the theory behind Decision Trees, Cost Complexity Pruning, Cross Validation and Confusion Matrices. Notes: - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
您要培养的技能
分步进行学习
在与您的工作区一起在分屏中播放的视频中，您的授课教师将指导您完成每个步骤：
Task 1: Import the modules that will do all the work
Task 2: Import the data
Task 3: Missing Data Part 1: Identifying Missing Data
Task 4: Missing Data Part 2: Dealing With Missing Data
Task 5: Format Data Part 1: Split the Data into Dependent and Independent Variables
Task 6: Format the Data Part 2: One-Hot Encoding
Task 7: Build A Preliminary Classification Tree
Task 8: Cost Complexity Pruning Part 1: Visualize alpha
Task 9: Cost Complexity Pruning Part 2: Cross Validation For Finding the Best Alpha
Task 10: Building, Evaluating, Drawing, and Interpreting the Final Classification Tree
指导项目工作原理
您的工作空间就是浏览器中的云桌面，无需下载
在分屏视频中，您的授课教师会为您提供分步指导
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来自CLASSIFICATION TREES IN PYTHON, FROM START TO FINISH的热门评论
A very informative and well guided short session to understand overview of Classification Trees. Covers lot of important concepts in 1 hour. Highly recommend
Machine learning algorithms used for data-set classification and many more works really impressed.
Liked, easy to understand and utilize the knowledge in a similar dataset.
Good Course. Cost Complexity Pruning explained nicely. Bammmm!!!!!!!!
常见问题
常见问题
如果我购买指导项目，会得到什么？
购买指导项目后，您将获得完成指导项目所需的一切，包括通过 Web 浏览器访问云桌面工作空间，工作空间中包含您需要了解的文件和软件，以及特定领域的专家提供的分步视频说明。
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您可以从指导项目中下载并保留您创建的任何文件。为此，您可以在访问云桌面时使用‘文件浏览器’功能。
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指导项目不提供助学金。
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指导项目不支持旁听。
我需要具备多少经验才能做这个指导项目？
您可在页面顶部点按此指导项目的经验级别，查看任何知识先决条件。对于指导项目的每个级别，您的讲师会逐步为您提供指导。
我能直接通过 Web 浏览器来完成此指导项目，而不必安装特殊软件吗？
是，您可以在浏览器的云桌面中获得完成指导项目所需的一切。
指导项目的学习体验如何？
您可以直接在浏览器中于分屏环境下完成任务，以此从做中学。在屏幕的左侧，您将在工作空间中完成任务。在屏幕的右侧，您将看到有讲师逐步指导您完成项目。
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