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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-for-bi-part-3/
课程评论：没有评论
课程名称：机器学习与数据分析：回归与预测 概述： 本课程是“Maven Analytics”四部分系列课程的第三部分，现在已被纳入《机器学习与数据科学的完整视觉指南》。该系列课程旨在帮助学习者建立机器学习的基础知识，课程详细划分为四个部分： - 第1部分：QA与数据分析 - 第2部分：分类建模 - 第3部分：回归与预测 - 第4部分：无监督学习 本课程旨在使数据科学易于理解，采用微软Excel等用户友好的工具，帮助学习者深入理解机器学习的工作原理，而无需编写任何代码。课程将通过案例研究来巩固关键概念，并与实际场景相结合，例如如何使用回归分析估算房价、预测季节性趋势等。 课程大纲： - 第一部分：回归介绍 - 监督学习的概述 - 回归与分类的区别 - 特征工程 - 过拟合与欠拟合 - 预测与根本原因分析 - 第二部分：回归建模基础 - 线性关系 - 最小二乘误差（SSE） - 单变量回归 - 多变量回归 - 非线性转化 - 第三部分：模型诊断 - R平方 - 平均误差度量（MSE, MAE, MAPE） - 零假设 - F显著性检验 - T值与P值 - 同方差性 - 多重共线性 - 第四部分：时间序列预测 - 季节性 - 自相关函数（ACF） - 线性趋势 - 非线性模型（Gompertz模型） - 干预分析 课程适合希望在数据科学领域建立成功职业基础的学习者。参与本课程，您将获得高质量的视频教程、机器学习回归与预测电子书、可下载的Excel项目文件、专家问答论坛以及30天的退款保证。 加入我们，开启您的学习之旅吧！
HEADS UP! This course is now part of The Complete Visual Guide to Machine Learning & Data Science, which combines all 4 Machine Learning courses from Maven Analytics. This course, along with the other individual courses in the series, will be retired soon.This course is PART 3 of a 4-PART SERIES designed to help you build a strong, foundational understanding of Machine Learning:PART 1: QA & Data ProfilingPART 2: Classification ModelingPART 3: Regression & ForecastingPART 4: Unsupervised LearningThis course makes data science approachable to everyday people, and is designed to demystify powerful Machine Learning tools & techniques without trying to teach you a coding language at the same time.Instead, we'll use familiar, user-friendly tools like Microsoft Excel to break down complex topics and help you understand exactly HOW and WHY machine learning works before you dive into programming languages like Python or R. Unlike most Data Science and Machine Learning courses, you won't write a SINGLE LINE of code.COURSE OUTLINE:In this Part 3 course, we'll start by introducing core building blocks like linear relationships and least squared error, then show you how these concepts can be applied to univariate, multivariate, and non-linear regression models.From there we'll review common diagnostic metrics like R-squared, mean error, F-significance, and P-Values, along with important concepts like homoscedasticity and multicollinearity.Last but not least we'll dive into time-series forecasting, and explore powerful techniques for identifying seasonality, predicting nonlinear trends, and measuring the impact of key business decisions using intervention analysis:Section 1: Intro to RegressionSupervised Learning landscapeRegression vs. ClassificationFeature engineeringOverfitting & UnderfittingPrediction vs. Root-Cause AnalysisSection 2: Regression Modeling 101Linear RelationshipsLeast Squared Error (SSE)Univariate RegressionMultivariate RegressionNonlinear TransformationSection 3: Model DiagnosticsR-SquaredMean Error Metrics (MSE, MAE, MAPE)Null HypothesisF-SignificanceT-Values & P-ValuesHomoskedasticityMulticollinearitySection 4: Time-Series ForecastingSeasonalityAuto Correlation Function (ACF)Linear TrendingNon-Linear Models (Gompertz)Intervention AnalysisThroughout the course we'll introduce hands-on case studies to solidify key concepts and tie them back to real world scenarios. You'll see how regression analysis can be used to estimate property prices, forecast seasonal trends, predict sales for a new product launch, and even measure the business impact of a new website design.If you're ready to build the foundation for a successful career in Data Science, this is the course for you!__________Join today and get immediate, lifetime access to the following:High-quality, on-demand videoMachine Learning: Regression & Forecasting ebookDownloadable Excel project fileExpert Q & A forum30-day money-back guaranteeHappy learning!-Josh M. (Lead Machine Learning Instructor, Maven Analytics)__________Looking for our full business intelligence stack? Search for "Maven Analytics" to browse our full course library, including Excel, Power BI, MySQL, and Tableau courses!See why our courses are among the TOP-RATED on Udemy:"Some of the BEST courses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C."This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M."Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.
