Excel Analytics: Linear Regression Analysis in MS Excel

所在平台: Udemy

课程主页: https://www.udemy.com/course/predictive-regression-modelling-in-microsoft-excel/

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课程名称:Excel分析:MS Excel中的线性回归分析 课程概述: 这是一门完整的线性回归课程,旨在教授如何在Excel中创建线性回归模型。在完成此课程后,您将能够识别商业问题并应用线性回归技术进行解决;在Excel中创建线性回归模型并分析结果;自信地实践、讨论和理解机器学习概念。 课程的帮助: 本课程为所有参加者提供一个可验证的结业证书。对于商业经理、高管或希望在实际商业问题中应用机器学习的学生来说,这门课程将奠定稳固的基础,教授最流行的机器学习技术:线性回归。 为什么选择这门课程? 本课程涵盖了解决商业问题时所需采取的所有步骤。许多课程仅关注如何进行分析,而我们相信,分析前后的准备工作同样重要,包括数据的正确性和预处理,以及在分析后评估模型质量并解读结果,以真正为商业提供帮助。 授课资格: 本课程由Abhishek和Pukhraj授课,他们在全球分析咨询公司担任经理,运用机器学习技术帮助企业解决问题,并将实践经验融入教学中。他们也是一些最受欢迎在线课程的创造者,拥有超过150,000个注册和数千个五星好评。 课程内容: - **部分一:统计基础** 包含数据类型、统计类型、数据图形表示、集中趋势的度量(如均值、中位数、众数)以及离散度量(如范围和标准差)。 - **部分二:数据预处理** 将教授如何逐步获取并准备数据,以便进行分析,包括业务知识重要性、单变量分析、双变量分析、异常值处理、缺失值填补、变量变换和相关性分析。 - **部分三:回归模型** 讨论简单线性回归和多重线性回归,解释基础理论,量化模型精度、F统计量意义、分类变量的解释及普通最小二乘法的变种,帮助学员解读结果并解决商业问题。 其他信息: 课程还将提供练习文件、测验与作业,帮助学员在每节课后跟进所学内容。此外,学员可以在课程中提问或直接联系讲师,以获得进一步的帮助与支持。 这门课程为希望入门机器学习的学生提供了一个全面的学习路径,深入浅出地引导他们掌握线性回归建模,并提升在R语言中创建模型的信心。欢迎点击注册,我将在第一课与你相见!

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You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in Excel, right?You've found the right Linear Regression course!After completing this course you will be able to:· Identify the business problem which can be solved using linear regression technique of Machine Learning.· Create a linear regression model in Excel and analyze its result.· Confidently practice, discuss and understand Machine Learning conceptsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear RegressionWhy should you choose this course?This course covers all the steps that one should take while solving a business problem through linear regression.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this courseWe are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.What is covered in this course?This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.Below are the course contents of this course on Linear Regression:· Section 1 - Basics of StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviation· Section 2 - Data PreprocessingIn this section you will learn what actions you need to take a step by step to get the data and thenprepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.· Section 3 - Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.By the end of this course, your confidence in creating a regression model in R will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.What is the Linear regression technique of Machine learning?Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).When there is a single input variable (x), the method is referred to as simple linear regression.When there are multiple input variables, the method is known as multiple linear regression.Why learn Linear regression technique of Machine learning?There are four reasons to learn Linear regression technique of Machine learning:1. Linear Regression is the most popular machine learning technique2. Linear Regression has fairly good prediction accuracy3. Linear Regression is simple to implement and easy to interpret4. It gives you a firm base to start learning other advanced techniques of Machine LearningHow much time does it take to learn Linear regression technique of machine learning?Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 4 parts:Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the R environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in RUnderstanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture in R where we actually run each query with you.

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