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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-for-bi-part-2/
课程评论：没有评论
课程名称：机器学习数据分析：分类建模 概述：请注意！本课程现已成为《机器学习与数据科学的完整视觉指南》的一部分，合并了Maven Analytics的所有四个机器学习课程。随着系列课程的退休，本课程与其他个别课程将不久后停止提供。如果你对探索数据科学和机器学习感兴趣，但对学习复杂的编程语言感到担忧，或对“朴素贝叶斯”、“逻辑回归”、“KNN”和“决策树”等术语感到畏惧，那么你来对地方了。 这门课程是四部分系列中的第二部分，旨在帮助你建立机器学习的基础性理解： 第一部分：质量保证与数据分析 第二部分：分类建模 第三部分：回归与预测 第四部分：无监督学习 本课程力求让普通人也能轻松接触到数据科学，并旨在以通俗易懂的方式解释强大的机器学习工具和技术，而不试图同时教授编码语言。我们将使用如Microsoft Excel等熟悉的用户友好工具来解析复杂主题，并帮助你理解机器学习如何以及为何运作，然后再开始使用像Python或R这样的编程语言。与大多数数据科学和机器学习课程不同，你不需要编写任何代码。 课程大纲： 在这门第二部分课程中，我们将介绍监督学习的概念，回顾分类工作流程，并讨论关键主题，如因变量与自变量、特征工程、数据拆分和过拟合。接下来，我们将回顾常见的分类模型，包括K近邻（KNN）、朴素贝叶斯、决策树、随机森林、逻辑回归和情感分析，并分享模型评分、选择和优化的技巧。 第一部分：分类简介 - 监督学习概念 - 分类工作流程 - 特征工程 - 数据拆分 - 过拟合与欠拟合 第二部分：分类模型 - K近邻 - 朴素贝叶斯 - 决策树 - 随机森林 - 逻辑回归 - 情感分析 第三部分：模型选择与调优 - 超参数调优 - 不平衡类别 - 混淆矩阵 - 准确率、精确率与召回率 - 模型选择与漂移 在整个课程中，我们将引入案例研究，以巩固关键概念并将其与现实世界场景联系起来。你将帮助构建Spotify的推荐引擎，分析零售商店的客户购买行为，预测旅行公司的订阅情况，提取客户评论中的情感等。 如果你准备好为成功的数据科学职业打下基础，这门课程就是为你量身定制的！ 立即加入，获得以下内容的终身访问权限： - 高质量的按需视频 - 机器学习：分类电子书 - 可下载的Excel项目文件 - 专家问答论坛 - 30天退款保证 祝学习愉快！- Josh M.（首席机器学习讲师，Maven Analytics）
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.If you're excited to explore Data Science & Machine Learning but anxious about learning complex programming languages or intimidated by terms like "naive bayes", "logistic regression", "KNN" and "decision trees", you're in the right place.This course is PART 2 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 2 course, we'll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting.From there we'll review common classification models including K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization.Section 1: Intro to ClassificationSupervised Learning landscapeClassification workflowFeature engineeringData splittingOverfitting & UnderfittingSection 2: Classification ModelsK-Nearest NeighborsNaïve BayesDecision TreesRandom ForestsLogistic RegressionSentiment AnalysisSection 3: Model Selection & TuningHyperparameter tuningImbalanced classesConfusion matricesAccuracy, Precision & recallModel selection & driftThroughout the course we'll introduce case studies to solidify key concepts and tie them back to real world scenarios. You'll help build a recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for a travel company, extract sentiment from customer reviews, and much more.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: Classification 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.
