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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-regression-analysis-statistics-machine-learning/
课程评论：没有评论
课程名称：Python回归分析：统计与机器学习 课程概述： 本课程专注于回归分析，这是统计分析和机器学习分析的核心内容。通过实践操作，您将学习Python中用于统计数据分析和机器学习的回归分析。课程从基础到高级深度探讨相关概念，帮助您在学术和职业生涯中运用所学知识进行商业预测和数据分析。 课程讲师： Minerva Singh，牛津大学MPhil（地理与环境）毕业生，剑桥大学博士（热带生态学与保护）毕业生，拥有超过5年的现实数据分析经验，曾在国际同行评审期刊上发表多篇论文。 课程内容： 1. 安装Python和Anaconda，学习加载包和读取不同类型数据。 2. 进行数据清理和准备。 3. 实施普通最小二乘法（OLS）回归，并学习如何解释结果。 4. 评估回归模型的准确性。 5. 使用广义线性模型（GLMs），如逻辑回归。 6. 应用基于机器学习的回归技术进行预测建模。 7. 使用基于树的机器学习模型。 8. 实施随机森林回归和梯度提升回归等机器学习方法，以提高回归预测的准确性。 9. 进行模型选择。 课程目标： - 从基础的统计知识提升到高级回归分析技术的应用。 - 学会使用Python执行各种统计与机器学习数据分析任务。 - 理解重要的统计与机器学习概念，并能够将其应用于实际数据分析中。 - 能够选择适合其研究问题的回归分析技术，并解释结果。 本课程为您提供了一个实践导向的学习机会，课程构建在严谨的学术训练和大量实践经验基础上，让您在学习理论的同时，积极运用不同技术于实际数据分析中。立即加入课程，与我们一起提升您的回归分析技能！
HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE: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 Python 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 & make business forecasting related decisions...All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.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 is Different; It will help you 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. LEARN FROM AN EXPERT DATA SCIENTIST:My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I also just recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have +5 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. THIS COURSE WILL HELP YOU BECOME A REGRESSION ANALYSIS EXPERT:Here is what we'll be covering inside the course:Get started with Python and Anaconda. Install these on your system, learn to load packages and read in different types of data in PythonCarry out data cleaning PythonImplement ordinary least square (OLS) regression in Python and learn how to interpret the results.Evaluate regression model accuracyImplement generalized linear models (GLMs) such as logistic regression using PythonUse machine learning based regression techniques for predictive modelling 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 selectionTHIS IS A PRACTICAL GUIDE TO REGRESSION ANALYSIS WITH REAL LIFE DATA:This 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, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One. Specifically the course will: (a) Take you from a basic level of statistical knowledge to performing some of the most common advanced regression analysis based techniques. (b) Equip you to use Python for performing the different statistical and machine learning data analysis tasks. (c) Introduce some of the most important statistical and machine learning concepts to you in a practical manner so you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong background in some of the most important statistical and machine learning concepts for regression analysis. (e) You will be able to decide which regression analysis techniques are best suited to answer your research questions and applicable to your data and interpret the results.It 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, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. JOIN THE COURSE NOW!
