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所在平台: Udemy |
课程主页: https://www.udemy.com/course/applied-ml-data-analytics-intro/
课程评论：没有评论
课程名称：应用机器学习：使用Pandas和PySpark进行分析入门 课程概述：在机器学习和数据科学生命周期中，数据的探索和准备是一个重要的步骤。正如在我之前的课程“应用机器学习：大局观”中提到的，这一基础步骤至关重要，学习所有可用工具并实践理解何时选择哪种工具显得尤为重要。本课程将教授实际操作技巧，以进行数据处理、探索、转换以及可视化的各个阶段。课程将让学习者接触到各种场景，帮助他们在实际项目中区分和选择合适的工具。 在每种工具的学习中，我们将涵盖多种技术及其在真实数据集上的特定用途，便于数据分析和操作。想要通过实践学习的学员需要具备Python开发环境，以便进行动手训练。对于已有实践经验的学员，本课程可作为各种工具和技术的复习，确保能够根据手头的问题使用正确的工具和技术组合。同时，课程内容也可以扩展至面试准备，帮助学员回顾Python中最佳的数据实践，为机器学习和数据科学做好准备。
Exploring and preparing data is a huge step in the Machine Learning and Data Science lifecycle as I've already mentioned in my other course "Applied ML: The Big Picture". Being such a crucial foundational step in the lifecycle, it's important to learn all the tools at your disposal and get a practical understanding on when to choose which tool.This course will teach the hands-on techniques to perform several stages in data processing, exploration and transformation, alongside visualization. It will also expose the learner to various scenarios, helping them differentiate and choose between the tools in the real world projects.Within each tool, we will cover a variety of techniques and their specific purpose in data analysis and manipulation on real datasets. Those who wish to learn by practice will require a system with Python development environment to get hands-on training. For someone who has already had the practice, this course can serve as a refresher on the various tools and techniques, to make sure you are using the right combination of tools and techniques for the given problem at hand. And likewise, be extended to interview preparations to refresh memory on best data practices for ML and Data Science in Python.
