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所在平台: Udemy |
课程主页: https://www.udemy.com/course/excel-for-data-science-and-machine-learning/
课程评论：没有评论
课程名称：Excel数据科学与机器学习 课程概述：在数据科学和机器学习的领域，Excel的作用不可小觑。尽管许多人认为只有通过编程才能进行数据分析，但其实，数据科学家和数据分析师们也常常使用Excel。由于Excel的广泛使用和易于理解的界面，使得它成为了一个理想的平台，可以帮助初学者和专家理解机器学习的基本原理，进行数据准备和分析。 本课程旨在帮助那些没有计算机科学背景或编程技能的人，通过Excel进行机器学习和统计分析。课程将展示如何使用简单的工具来实现复杂的概念，从而有效降低学习的数据分析门槛。在这里，你将学习到多种机器学习方法，包括：线性回归、多重线性回归、逻辑回归、聚类分析、K均值聚类以及决策树。此外，还会讲解基本的统计和机器学习概念，如回归系数、变异性、ROC曲线、欠拟合与过拟合等。 课程的亮点在于，即使你没有编程经验，也能轻松理解机器学习算法的逻辑。完成这门课程后，你将掌握数据科学和机器学习的基础知识，为你未来深入学习Python等更高级框架打下良好基础。 如果你对机器学习充满热情但缺乏编程技能，这门课程将是你迈入数据科学的绝佳机会。快来加入我们，一起开启你的机器学习之旅吧！
Why machine learning and data science in Excel?Do data scientists and data analysts use Excel at all?The answer is a resounding "Yes, they do!"Few people in an organization can read a Jupyter Notebook, but literally everyone is familiar with Excel. It provides the direct, visual insight that both experts and beginners need to apply the most common machine learning methods. Plus, it is naturally suited to data preparation.In fact, the simplicity of Excel lowers barriers to entry and allows you to undertake your own data analysis right away. Even if you are not a computer science graduate with Python coding skills, this course will teach you how to perform machine learning and advanced statistical analysis on your own.Excel is the perfect environment to grasp the logic of different machine learning techniques in an easy-to-understand way. All you need to do is get started, and in no time, you will be able to fully understand the intuition behind ML algorithms without having to code at all.So, if you are not into programming but you want to break into data science, statistical analysis, and machine learning, and you aspire to become a data analyst or data scientist, you've come to the right place.Machine learning methods we will cover in the course:Linear regressionMultiple Linear RegressionLogistic RegressionCluster AnalysisK-Means ClusteringDecision TreesYou will learn fundamental statistical and machine learning concepts, such as:Regression coefficientsVariabilityOLS assumptionsROC curveUnderfittingOverfittingDifference between classification and clusteringHow to choose the number of clustersHow to cluster categorical dataWhen to standardize dataPros and Cons of clusteringEntropy (Loss function)Information gainAs you can see, we aim to teach you the foundations of machine learning and advanced statistical analysis in a software that is truly easy to understand. And the best part is, once you finish this course, you will have the transferable theoretical knowledge you'll need if you decide to dive into the advanced frameworks available in Python.So, if you are passionate about machine learning but you don't know how to code, then this course is the perfect opportunity for you. Click ‘Buy Now', get excited, and begin your ML journey today!!
