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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-ss/
课程评论：没有评论
课程名称：初学者的应用数据科学和机器学习R语言实战 课程概述：本课程旨在为初学者提供数据科学方法和机器学习技术的基础理解，从R编程的基本知识开始。通过本课程的学习，学员将能够独立探索数据科学和机器学习的领域，并在此基础上构建更高级的知识体系。课程不仅涵盖R语言中的预定义函数和各种软件包的使用，还将教授如何从零开始构建机器学习算法。 在课程中，我们将涉及R编程、数据科学与机器学习的介绍、数据理解、数据预处理、数据清洗以及如何应用数据科学工具对结构化和非结构化数据进行预处理和分析，构建预测模型，进行聚类分析、主成分分析（PCA）等。机器学习部分将包括监督学习和非监督学习。在监督学习中，我们将学习线性回归、逻辑回归、时间序列预测、文本分析（自然语言处理的一部分）、神经网络、支持向量机、市场篮分析（关联规则）等内容。而在非监督学习中，我们将探讨聚类技术，包括层次聚类和非层次聚类，具体内容包括K-means、软聚类和硬聚类、相似性函数、聚合方法及分裂方法等。 我们还将使用现实生活中的不同实例来阐述各类主题。在课程结束时，将进行每个主题的实验室环节，利用不同的数据集进行实践，涵盖所有的代码和操作。
In this course, we have provided the basic understanding of data science methods and techniques and machine learning, beginning with the basics of R programming. Having gone through this course, a new beginner, who has interest in data science and machine learning, will be able to sail through on their own. What it means is that students will be able to build the advanced knowledge based on what they learn here. We will not only use the predefined functions in R and different packages, but we will also learn how to build machine learning algorithms from scratch defining our own functions.In this course we will cover topics such as, R programming, introduction to data science and machine learning, understanding of data, preprocessing of data, cleansing of data and how to apply data science tools to preprocess and analyze data (structured as well as unstructured) and build predictive models, perform clustering, PCA, etc.The machine learning topics would include, supervised learning and unsupervised learning. In supervised learning we will cover topics such as linear regression, logistics regression, time series and forecasting, text analytics (part of natural language processing), neural network, support vector machine, market basket analysis (association rules). As part of unsupervised learning, we will go through clustering, such as hierarchical clustering and non-hierarchical clustering. We will focus on topics such as K-means, soft and hard clustering, similarity functions, agglomerative methods (bottom up), divisive methods (top-down). We will also cover topics such as principal component analysis (PCA), and how to use both in real life.We will look at different examples of various topics. At the end, we will go through various lab sessions on each topic, using different datasets, with all the codes and operations.
