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所在平台: Udemy |
课程主页: https://www.udemy.com/course/exploratory-data-analysis-in-python/
课程评论：没有评论
课程名称：Python中的探索性数据分析 课程概述：在首次接触数据集时，我们常常迫不及待地想要测试各种模型和算法。然而，如果我们在了解数据之前就直接输入模型，结果将会不可靠，模型也可能失败。此外，如果不提前选择最佳特征，训练过程将变得缓慢，模型也无法学习到有用的信息。因此，我们要采取的第一步是审视我们的数据集并可视化其包含的信息，也就是进行探索性数据分析（EDA）。 EDA是数据科学和机器学习中重要的一步，它帮助我们在应用任何模型或算法之前探索数据集内部隐藏的信息。它大量使用数据可视化，并且不带偏见。此外，EDA还可以帮助我们判断特征是否具有预测能力，确定我们所参与的机器学习项目是否有成功的可能。如果没有进行探索性数据分析，我们可能会向模型提供错误的数据而导致失败。 通过本课程，学生将学习到： - 如何可视化数据集中隐藏的信息 - 如何可视化数据集中列之间的相关性和重要性 - 一些有用的Python库 所有课程内容都是实用的，使用Python编程语言和Jupyter笔记本进行讲解，并且所有笔记本均可下载。
When we put our hands on a dataset for the first time, we can't wait to test several models and algorithms. This is wrong because if we don't know the information before feeding our model, the results will be unreliable and the model itself will surely fail. Moreover, if we don't select the best features in advance, the training phase becomes slow and the model won't learn anything useful.So, the first approach we must have is to take a look at our dataset and visualize the information it contains. In other words, we have to explore it.That's the purpose of the Exploratory Data Analysis.EDA is an important step of data science and machine learning. It helps us explore the information hidden inside a dataset before applying any model or algorithm. It makes heavy use of data visualization, it's bias-free.Moreover, it lets us figure out whether our features have predictive power or not, determining if the machine learning project we are working on has chances to be successful. Without EDA, we may give the wrong data to a model without reaching any success.With this course, the student will learn:How to visualize information that is hidden inside the datasetHow to visualize the correlation and the importance of the columns of a datasetSome useful Python librariesAll the lessons are practical and made using Python programming language and Jupyter notebooks. All the notebooks are downloadable.
