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所在平台: Udemy |
课程主页: https://www.udemy.com/course/regression-analysis-for-statistics-machine-learning-in-r/
课程评论：没有评论
课程名称：R语言回归分析与统计和机器学习 课程概述：在众多R语言统计和机器学习课程中，为什么要选择这门课？回归分析是统计和机器学习分析的核心内容之一。本课程将以实用的方式教你如何在R中进行统计数据分析和机器学习的回归分析，内容涵盖从基础到专家级的相关概念。课程旨在帮助你提升学术成绩，提供新的分析工具，在工作环境中运用知识，或做出商业预测方面的决策。同时，课程由一位拥有牛津和剑桥背景的研究者授课。 授课教师：MINERVA SINGH，牛津大学地理与环境硕士，剑桥大学热带生态与保护博士。教师具备多年使用数据科学技术分析现实数据的经验，并在国际同行评审期刊上发表过多篇论文。 课程亮点： - 从简单的普通最小二乘回归（OLS）模型到多重共线性处理，再到基于机器学习的回归模型，逐步深入回归分析技巧。 - 学习如何在R和RStudio中进行数据加载、数据清理和可视化。 - 实施OLS回归，并学习如何解释结果。 - 处理多重共线性，可使用变量选择及正则化技术如岭回归。 - 进行变量和模型选择，使用交叉验证方法提高模型准确性。 - 实施广义线性模型（GLM）和非参数技术，如广义加法模型（GAM）及树基机器学习模型。 - 使用机器学习方法（如随机森林回归和梯度提升回归）来提高回归预测精度。 课程目标： - 带领具有基本统计知识的学员掌握常见的高级回归分析技术。 - 使学员熟练使用R进行统计和机器学习的数据分析及可视化任务。 - 以实用的方式介绍重要的统计和机器学习概念，使学员能够应用于实际数据分析与解读。 - 帮助学员决定适合其研究问题的回归分析技术，并解释结果。 课程为实践为主，尽管涵盖一些理论概念，重点是实施各种技术于真实数据上并进行结果解释。每节视频后，学员将学习新的概念或技术，可应用于自身项目。 立即行动！我将亲自支持你，确保你在这门课程中的学习体验成功。
With so many R Statistics & Machine Learning courses around, why enrol for this?Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions. All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. This course is based on my years of regression modelling experience and implementing different regression models on real life data. Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling. My course will change this. You will go all the way from implementing and inferring simple OLS (ordinary least square) regression models to dealing with issues of multicollinearity in regression to machine learning based regression models. Become a Regression Analysis Expert and Harness the Power of R for Your AnalysisGet started with R and RStudio. Install these on your system, learn to load packages and read in different types of data in RCarry out data cleaning and data visualization using RImplement ordinary least square (OLS) regression in R and learn how to interpret the results.Learn how to deal with multicollinearity both through variable selection and regularization techniques such as ridge regressionCarry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods.Evaluate regression model accuracyImplement generalized linear models (GLMs) such as logistic regression and Poisson regression. Use logistic regression as a binary classifier to distinguish between male and female voices.Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data. Work with tree-based machine learning modelsImplement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy.Carry out model selectionBecome a Regression Analysis Pro and Apply Your Knowledge on Real-Life DataThis course is your one shot way of acquiring the knowledge of statistical and machine learning analysis that I acquired from the rigorous training received at two of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in a renowned international journal like PLOS One. Specifically, the course will: (a) Take the students with a basic level of statistical knowledge to perform some of the most common advanced regression analysis based techniques (b) Equip students to use R for performing the different statistical and machine learning data analysis and visualization tasks (c) Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that the students can apply these concepts for practical data analysis and interpretation (d) Students will get a strong background in some of the most important statistical and machine learning concepts for regression analysis. (e) Students will be able to decide which regression analysis techniques are best suited to answer their research questions and applicable to their data and interpret the resultsIt is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results. After each video, you will learn a new concept or technique which you may apply to your own projects. TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.
