Interpretable Machine Learning Applications: Part 4

提供方
在此指导项目中,您将:

Set up a machine learning application in a "zero configuration" environment such as Google's Colab(oratory) Research platform.

Set up and configure the What-If Tool to analyze the behavior of exemplary machine learning prediction models.

1.5 hours
中级
无需下载
分屏视频
英语(English)
仅限桌面

In this 1-hour long guided project, you will learn how to use the "What-If" Tool (WIT) in the context of training and testing machine learning prediction models. In particular, you will learn a) how to set up a machine learning application in Python by using interactive Python notebook(s) on Google's Colab(oratory) environment, a.k.a. "zero configuration" environment, b) import and prepare the data, c) train and test classifiers as prediction models, d) analyze the behavior of the trained prediction models by using WIT for specific data points (individual basis), e) moving on to the analysis of the behavior of the trained prediction models by using WIT global basis, i.e., all test data considered. Note: 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

  • Data Analysis

  • Data scientist

  • Machine learning project management

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:

  1. Set up the environment for the "What-If" tool (WIT) as an extension in Jupyter and as a Google's Colaboratory notebook, including importing of the dataset (e.g., white wine quality data)

  2. Train classifiers, e.g., Decision Tree and Random Forest, as exemplary machine learning  prediction models to make predictions about the quality of white wines.

  3. Launch the What-If Tool (WIT) widget. This task will allow us to get a first understanding on how our prediction model(s) behave at both individual and global levels.

  4. Use the What-If Tool (WIT) features to explain the behavior of a prediction model on an individual basis.

  5. Use the What-If Tool (WIT) advanced features to explain the behavior of a prediction model on an individual basis.

  6. Use the What-If Tool (WIT) features to explain the behavior of a prediction model on a global basis.

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

常见问题

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。

由于您的工作空间包含适合笔记本电脑或台式计算机使用的云桌面,因此指导项目不在移动设备上提供。

指导项目授课教师是特定领域的专家,他们在项目的技能、工具或领域方面经验丰富,并且热衷于分享自己的知识以影响全球数百万的学生。

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。

指导项目不符合退款条件。请查看我们完整的退款政策

指导项目不提供助学金。

指导项目不支持旁听。

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,您的授课教师会逐步为您提供指导。

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。

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,您将看到有授课教师逐步指导您完成项目。