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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/feature-engineering
课程评论：没有评论
课程名称：特征工程 课程概述： 想了解 Vertex AI 特征库吗？想知道如何提高机器学习模型的准确性？想发现哪些数据列能够形成最有用的特征？欢迎来到特征工程课程，我们将讨论好特征与坏特征的区别，以及如何对它们进行预处理和转换，以达到最佳模型应用效果。本课程包含有关使用 BigQuery ML、Keras 和 TensorFlow 进行特征工程的内容和实验。 课程大纲： - **模块 0：介绍** - 描述：本模块提供课程的概述和目标。 - **模块 1：Vertex AI 特征库介绍** - 描述：本模块介绍 Vertex AI 特征库。 - **模块 2：原始数据到特征** - 描述：特征工程通常是构建机器学习项目中最长且最困难的阶段。在特征工程过程中，您将从原始数据出发，运用自己的领域知识创建特征，以使机器学习算法发挥作用。本模块探讨何谓良好特征以及如何在机器学习模型中表示它们。 - **模块 3：特征工程** - 描述：本模块回顾了机器学习与统计学之间的区别，以及如何在 BigQuery ML 和 Keras 中执行特征工程。我们还将涵盖一些高级特征工程实践。 - **模块 4：预处理和特征创建** - 描述：在本模块中，您将深入了解 Dataflow，这是一种与 Apache Beam 互补的技术，可帮助您构建和运行预处理和特征工程。 - **模块 5：特征交叉 - TensorFlow Playground** - 描述：在传统机器学习中，特征交叉并没有起到很大的作用，但在现代机器学习方法中，特征交叉是您工具箱中不可或缺的一部分。在本模块中，您将学习如何识别特征交叉帮助机器学习的强大应用场景。 - **模块 6：TensorFlow Transform 介绍** - 描述：TensorFlow Transform（tf.Transform）是用于使用 TensorFlow 预处理数据的库。tf.Transform 适用于需要对数据进行全面访问的预处理，例如：通过均值和标准差对输入值进行标准化；根据所有输入示例查看值进行词汇整数化；根据观察到的数据分布进行输入桶化。在本模块中，我们将探讨 tf.Transform 的用例。 - **模块 7：总结** - 描述：本模块是特征工程课程的总结。 这个课程帮助您掌握特征工程的核心理念和实践，提升您在机器学习项目中的能力。
Name:Module 0: Introduction
Description:This module provides an overview of the course and its objectives.
Name:Module 1: Introduction to Vertex AI Feature Store
Description:This module introduces Vertex AI Feature Store.
Name:Module 2: Raw Data to Features
Description:Feature engineering is often the longest and most difficult phase of building your ML project. In the feature engineering process, you start with your raw data and use your own domain knowledge to create features that will make your machine learning algorithms work. In this module we explore what makes a good feature and how to represent them in your ML model.
Name:Module 3: Feature Engineering
Description:This module reviews the differences between machine learning and statistics, and how to perform feature engineering in both BigQuery ML and Keras. We'll also cover some advanced feature engineering practices.
Name:Module 4: Preprocessing and Feature Creation
Description:In this module you will learn more about Dataflow, which is a complementary technology to Apache Beam and both of them can help you build and run preprocessing and feature engineering.
Name:Module 5: Feature Crosses - TensorFlow Playground
Description:In traditional machine learning, feature crosses don’t play much of a role, but in modern day ML methods, feature crosses are an invaluable part of your toolkit. In this module, you will learn how to recognize the kinds of problems where feature crosses are a powerful way to help machines learn.
Name:Module 6: Introduction to TensorFlow Transform
Description:TensorFlow Transform (tf.Transform) is a library for preprocessing data with TensorFlow. tf.Transform is useful for preprocessing that requires a full pass the data, such as: - normalizing an input value by mean and stdev - integerizing a vocabulary by looking at all input examples for values - bucketizing inputs based on the observed data distribution In this module we will explore use cases for tf.Transform.
Name:Module 7: Summary
Description:This module is a summary of the Feature Engineering course.
Want to know about Vertex AI Feature Store? Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering, where we discuss good versus bad features and how you can preprocess and transform them for optimal use in your models. This course includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
