|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-pre-processing-for-machine-learning-in-python/
课程评论：没有评论
**Coursera课程：Python机器学习数据预处理** 本课程专注于机器学习的数据预处理技术。数据预处理是指将原始数据集转换为机器学习模型可用形式的一系列操作。这对于使数据适用于某些机器学习模型、降低数据维度、更好地识别相关数据以及提升模型性能至关重要。它是机器学习流程中最重要的部分，并对项目成功产生决定性影响。如果输入给机器学习模型的数据形状不正确，模型将无法正常工作。 许多初学者在学习神经网络等复杂模型时，往往忽视了如何处理数据集以使其适应算法，从而导致模型效果不佳。最终他们会意识到，良好的预处理能够节省大量时间和提升算法性能。因此，掌握数据预处理技术是一项极其重要的技能。 **通过本课程，您将学到：** * 数据清洗 * 类别变量的编码 * 数值特征的转换 * Scikit-learn Pipeline 和 ColumnTransformer 对象 * 数值特征的缩放 * 主成分分析 (PCA) * 基于过滤器的特征选择 * 使用 SMOTE 进行过采样 所有示例都将使用 Python 编程语言及其功能强大的 Scikit-learn 库进行演示。课程将使用数据科学行业标准的 Jupyter 环境。课程的所有章节都包含实践练习，并且 Jupyter 笔记本均可下载。
In this course, we are going to focus on pre-processing techniques for machine learning. Pre-processing is the set of manipulations that transform a raw dataset to make it used by a machine learning model. It is necessary for making our data suitable for some machine learning models, to reduce the dimensionality, to better identify the relevant data, and to increase model performance. It's the most important part of a machine learning pipeline and it's strongly able to affect the success of a project. In fact, if we don't feed a machine learning model with the correctly shaped data, it won't work at all.Sometimes, aspiring Data Scientists start studying neural networks and other complex models and forget to study how to manipulate a dataset in order to make it used by their algorithms. So, they fail in creating good models and only at the end they realize that good pre-processing would make them save a lot of time and increase the performance of their algorithms. So, handling pre-processing techniques is a very important skill. That's why I have created an entire course that focuses only on data pre-processing.With this course, you are going to learn:Data cleaningEncoding of the categorical variablesTransformation of the numerical featuresScikit-learn Pipeline and ColumnTransformer objectsScaling of the numerical featuresPrincipal Component AnalysisFilter-based feature selectionOversampling using SMOTEAll the examples will be given using Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the sections of this course end with some practical exercises and the Jupyter notebooks are all downloadable.
