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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-in-python-regression/
课程评论：没有评论
课程名称：Python 数据科学：回归与预测 课程概述： 本课程是一个以实践为基础的项目导向课程，旨在帮助您掌握使用 Python 进行回归分析和预测的基础知识。我们将从回顾 Python 数据科学工作流开始，讨论回归分析的主要目标和类型，并深入研究整个课程中使用的回归建模步骤。您将学习如何执行探索性数据分析（EDA），拟合简单和多元线性回归模型，并培养对模型解释和评估其性能的直觉，使用假设检验、残差图和误差度量等工具。我们还将审查线性回归的假设，并学习如何诊断和修复每个假设。 接下来，我们将讨论模型测试和验证步骤，以确保我们的模型在新的、未知的数据上表现良好，包括数据拆分、调优和模型选择的概念。您还将学习如何利用特征工程技术和正则化回归算法来提高模型性能。 在整个课程中，您将作为 Maven Consulting Group 的助理数据科学家，专注于为客户制定定价策略。使用您在课程中学到的技能，您将使用 Python 探索数据并建立回归模型，帮助企业准确预测价格并理解影响价格的变量。最后，您将接触时间序列分析和预测技术，学习如何分析趋势和季节性，进行分解，并预测未来的值。 课程大纲： 1. Python 数据科学简介：介绍数据科学和机器学习领域，回顾必要技巧，并介绍数据科学工作流的每个阶段。 2. 回归基础：回顾回归的基本知识，包括关键术语、回归分析的类型和目标，以及回归建模工作流。 3. 数据准备与 EDA：回顾建模所需的数据准备和探索性数据分析步骤，以及探索目标、特征及其关系的主要技术。 4. 简单线性回归：在 Python 中构建简单线性回归模型，学习评估其质量和输出的度量和统计检验。 5. 多元线性回归：在 Python 中构建多元线性回归模型，评估模型拟合，进行变量选择，并使用误差度量比较模型。 6. 模型假设：回顾线性回归模型需要满足的假设，以确保模型的预测和解释有效。 7. 模型测试与验证：通过拆分数据、使用训练和验证数据调优模型、选择最佳模型以及在测试数据上评分来测试模型性能。 8. 特征工程：为回归模型应用特征工程技术，包括虚拟变量、交互项、分箱等。 9. 正则化回归：介绍正则化回归技术，作为线性回归的替代方案，包括岭回归、Lasso 回归和弹性网回归。 10. 时间序列分析：学习探索时间序列数据的方法，以及如何使用线性回归和 Facebook Prophet 执行时间序列预测。 准备好开始学习了吗？立刻加入以获得终身访问以下内容： - 8.5 小时的高质量视频 - 14 个作业 - 10 次测验 - 3 个项目 - 数据科学 Python：回归与预测电子书（超过 230 页） - 可下载的项目文件和解决方案 - 专家支持及问答论坛 - 30 天的 Udemy 满意保证 如果您是一位商业智能专业人士或有志成为数据科学家的学习者，正在寻找 Python 中回归建模和预测的入门课程，那么这是适合您的课程。祝您学习愉快！ - Chris Bruehl（数据科学专家及 Maven Analytics 首席 Python 教员） 如需查看我们的完整商业智能课程库，请搜索“Maven Analytics”，了解包括 Excel、Power BI、MySQL、Tableau 和机器学习课程在内的课程！
This is a hands-on, project-based course designed to help you master the foundations for regression analysis and forecasting with Python.We'll start by reviewing the Python data science workflow, discussing the primary goals & types of regression analysis, and do a deep dive into the regression modeling steps we'll be using throughout the course.You'll learn to perform exploratory data analysis (EDA), fit simple & multiple linear regression models, and build an intuition for interpreting models and evaluating their performance using tools like hypothesis tests, residual plots, and error metrics. We'll also review the assumptions of linear regression, and learn how to diagnose and fix each one.From there, we'll cover the model testing & validation steps that help ensure our models perform well on new, unseen data, including the concepts of data splitting, tuning, and model selection. You'll also learn how to improve model performance by leveraging feature engineering techniques and regularized regression algorithms.Throughout the course, you'll play the role of Associate Data Scientist for Maven Consulting Group on a team that focuses on pricing strategy for their clients. Using the skills you learn throughout the course, you'll use Python to explore their data and build regression models to help firms accurately predict prices and understand the variables that impact them.Last but not least, you'll get an introduction to time series analysis & forecasting techniques. You'll learn to analyze trends & seasonality, perform decomposition, and forecast future values.COURSE OUTLINE:Intro to Data Science with PythonIntroduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflowRegression 101Review the basics of regression, including key terms, the types and goals of regression analysis, and the regression modeling workflowPre-Modeling Data Prep & EDARecap the data prep & EDA steps required to perform modeling, including key techniques to explore the target, features, and their relationshipsSimple Linear RegressionBuild simple linear regression models in Python and learn about the metrics and statistical tests that help evaluate their quality and outputMultiple Linear RegressionBuild multiple linear regression models in Python and evaluate the model fit, perform variable selection, and compare models using error metricsModel AssumptionsReview the assumptions of linear regression models that need to be met to ensure that the model's predictions and interpretation are validModel Testing & ValidationTest model performance by splitting data, tuning the model with the train & validation data, selecting the best model, and scoring it on the test dataFeature EngineeringApply feature engineering techniques for regression models, including dummy variables, interaction terms, binning, and moreRegularized RegressionIntroduce regularized regression techniques, which are alternatives to linear regression, including Ridge, Lasso, and Elastic Net regressionTime Series AnalysisLearn methods for exploring time series data and how to perform time series forecasting using linear regression and Facebook Prophet__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:8.5 hours of high-quality video14 homework assignments10 quizzes3 projectsData Science in Python: Regression & forecasting ebook (230+ pages)Downloadable project files & solutionsExpert support and Q & A forum30-day Udemy satisfaction guaranteeIf you're a business intelligence professional or aspiring data scientist looking for an introduction to the world of regression modeling and forecasting with Python, this is the course for you.Happy learning!-Chris Bruehl (Data Science Expert & Lead Python 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, Tableau and Machine Learning 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.
