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所在平台: Udemy |
课程主页: https://www.udemy.com/course/geospatial-data-science-statistics-and-machine-learning-i/
课程评论：没有评论
课程名称：地理空间数据科学：统计与机器学习 I 课程概述：本课程旨在展示开源Python包用于矢量地理空间数据的分析。我使用Jupyter Notebooks作为互动Python环境，主要利用GeoPandas读取和存储地理空间数据，进行探索性数据分析，以及准备数据以便于统计模型的使用（包括特征工程、处理离群值和缺失数据等），并进行简单绘图。Statsmodels用于统计推断，提供了关于个别解释变量解释力的详细信息及模型选择框架。Scikit-learn则用于机器学习应用，包含许多先进的机器学习算法，以及交叉验证、正则化、模型性能评估等工具。 本课程为项目导向，使用与墨西哥生物多样性相关的真实数据，从统计推断和机器学习的角度全面讲解整个过程。课程以线性回归为基础，开发对方法论的概念理解，同时讨论泊松回归、逻辑回归、决策树、随机森林、K-NN分类，以及主成分分析（PCA）和K均值聚类等无监督分类方法。 在整个课程中，重点放在地理空间数据及其特殊考虑上，如空间连接、地图绘图和处理空间自相关等。重要概念包括模型选择、最大似然估计、统计推断与机器学习之间的差异等，将以面向地理空间专业人士而非统计学家的方式进行概念性解释。
In this course I demonstrate open source python packages for the analysis of vector-based geospatial data. I use Jupyter Notebooks as an interactive Python environment. GeoPandas is used for reading and storing geospatial data, exploratory data analysis, preparing data for use in statistical models (feature engineering, dealing with outlier and missing data, etc.), and simple plotting. Statsmodels is used for statistical inference as it provides more detail on the explanatory power of individual explanatory variables and a framework for model selection. Scikit-learn is used for machine learning applications as it includes many advanced machine learning algorithms, as well as tools for cross-validation, regularization, assessing model performance, and more.This is a project-based course. I use real data related to biodiversity in Mexico and walk through the entire process, from both a statistical inference and machine learning perspective. I use linear regression as the basis for developing conceptual understanding of the methodology and then also discuss Poisson Regression, Logistic Regression, Decision trees, Random Forests, K-NN classification, and unsupervised classification methods such as PCA and K-means clustering.Throughout the course, the focus is on geospatial data and special considerations for spatial data such as spatial joins, map plotting, and dealing with spatial autocorrelation. Important concepts including model selection, maximum likelihood estimation, differences between statistical inference and machine learning and more are explained conceptually in a manner intended for geospatial professionals rather than statisticians.
