|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-prep-for-h2o-driverless-ai/
课程评论：没有评论
课程名称：H2O Driverless AI 数据准备 概述：本课程是H2O大学认证项目的一部分，旨在为参与者提供有效使用H2O Driverless AI工具所需的技能。H2O的解决方案工程师Jonathan Farinela将强调数据质量在实现成功结果中的重要性，同时阐述数据准备的原则和流程。课程分为两个主要部分： 在第一部分，参与者将深入了解表格格式在经典机器学习中的重要性，并掌握监督学习和无监督学习之间的区别，以及分类和回归等常见方法。课程还将强调在数据集构建中定义分析单位的重要性。此外，参与者将看到Driverless AI中数据准备的演示，展示其自动预处理任务的能力，以及如何使用Python代码进行定制。 第二部分将专注于时间序列数据的准备。课程将探索时间序列问题的基本方面，包括日期列的必要性和数据的自回归特性等。课程还将讨论处理数据集中多个系列时所面临的挑战，并提供提高模型性能的最佳实践。Jonathan将展示经过为时间序列分析量身定制的数据集准备和分割技术，利用Driverless AI的强大功能来实现。 享受学习的旅程！
This course, a component of H2O's University's certification program, aims to equip participants with the requisite skills to effectively utilize our H2O's Driverless AI tool. Jonathan Farinela, Solutions Engineer at H2O, will emphasize the crucial role of data quality in achieving successful outcomes, while also elucidating the principles and procedures of data preparation. The course is divided into two main sections: In the initial section, participants will delve into the importance of the tabular format in classical machine learning. They will also grasp the distinction between supervised and unsupervised learning, along with common methodologies like classification and regression. The significance of defining the unit of analysis in dataset construction will be highlighted. Moreover, participants will witness demonstrations of data preparation within Driverless AI, showcasing its ability to automate preprocessing tasks and allow customization using Python code.Transitioning to the second section, the course will concentrate on time series data preparation. Fundamental aspects of time series problems will be explored, including the necessity of a date column and understanding the autoregressive nature of such data. The course will also address challenges associated with handling multiple series within a dataset and provide best practices for improving model performance. Jonathan will exemplify dataset preparation and splitting techniques tailored for time series analysis using the capabilities of Driverless AI. Enjoy the learning journey!
