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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-preprocessing-for-machine-learning-and-data-analysis/
课程评论：没有评论
课程名称：机器学习与数据分析的数据预处理 课程概述：本课程包含29个可下载文件，包括一个包含整个课程总结的PDF文件（91页）和28个与相应讲座相关的Python代码文件。我们认为，理论的扎实理解是有效应用的基础。因此，本课程采用经典的“课堂式”教学方法。首先，我们将充分时间用于解释每个主题的理论基础，包括为何使用特定技术、其适用场景及优势。在建立了坚实的理论基础后，我们将进入编码环节，逐行解释示例代码。课程中包含大量基于Python的编程示例，对于某些主题，我们提供多个示例以强化理解。这些示例可以进行适当的修改，以适应您的具体项目需求。 数据预处理是人工智能与机器学习中至关重要的一步，直接影响模型的性能、准确性和效率。由于原始数据通常是杂乱和非结构化的，数据预处理确保了数据集的清洁和优化，从而提高预测能力。本课程涵盖了一系列核心技术，包括处理缺失值、数据缩放、编码分类数据、特征工程以及降维（PCA）。我们还将探讨与地理信息相关的数据可视化、加权散点图和形状文件，这些在地理空间AI应用中尤为实用。 除了传统的结构化数据集，本课程还包含图像和地理数据集，使学习者对现实世界的AI项目有更广泛的视角。到课程结束时，您将能够构建自动化的数据预处理管道，并高效地为机器学习和深度学习应用准备数据集。 本课程特别适合机器学习工程师、数据科学家、AI开发者和研究人员，提供实用技能和最佳实践，以确保高质量、经过良好处理的数据集，从而提升模型性能。您可以从最后一节课（第28节课）下载整个课程总结PDF。
This course includes 29 downloadable files, including one PDF file containing the entire course summary (91 pages) and 28 Python code files attached to their corresponding lectures.If we understand a concept well theoretically, only then can we apply it effectively for our purposes. Therefore, this course is structured in a classic "classroom-style" approach. First, we dedicate sufficient time to explaining the theoretical foundations of each topic, including why we use a particular technique, where it is applicable, and its advantages.After establishing a solid theoretical understanding, we move on to the coding session, where we explain the example code line by line. This course includes numerous Python-based coding examples, and for some topics, we provide multiple examples to reinforce understanding. These examples are adaptable, meaning you can modify them slightly to fit your specific projects.Data preprocessing is a crucial step in AI and machine learning, directly affecting model performance, accuracy, and efficiency. Since raw data is often messy and unstructured, preprocessing ensures clean, optimized datasets for better predictions.This hands-on course covers essential techniques, including handling missing values, scaling, encoding categorical data, feature engineering, and dimensionality reduction (PCA). We will also explore data visualization with geographic information, weighted scatter plots, and shapefiles, particularly useful for geospatial AI applications.Beyond traditional structured datasets, this course includes image and geographic datasets, giving learners a broader perspective on real-world AI projects.By the end, you'll be able to build automated data preprocessing pipelines and prepare datasets efficiently for machine learning and deep learning applications.Ideal for ML engineers, data scientists, AI developers, and researchers, this course equips you with practical skills and best practices for high-quality, well-processed datasets that enhance model performance. You can download the entire course summary PDF from the final lecture (Lecture 28)
