Build univariate and multivariate linear regression models in Python using scikit-learn
Perform Exploratory Data Analysis (EDA) and data visualization with seaborn
Evaluate model fit and accuracy using numerical measures such as R² and RMSE
Model interaction effects in regression using basic feature engineering techniques
In this 2-hour long project-based course, you will build and evaluate multiple linear regression models using Python. You will use scikit-learn to calculate the regression, while using pandas for data management and seaborn for data visualization. The data for this project consists of the very popular Advertising dataset to predict sales revenue based on advertising spending through media such as TV, radio, and newspaper. By the end of this project, you will be able to: - Build univariate and multivariate linear regression models using scikit-learn - Perform Exploratory Data Analysis (EDA) and data visualization with seaborn - Evaluate model fit and accuracy using numerical measures such as R² and RMSE - Model interaction effects in regression using basic feature engineering techniques This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, this means instant access to a cloud desktop with Jupyter Notebooks and Python 3.7 with all the necessary libraries pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Basic familiarity with programming in Python. An understanding of linear regression.
Introduction and Overview
Load the Data
Relationships between Features and Target
Multiple Linear Regression Model
Model Evaluation Using Train/Test Split and Model Metrics
Interaction Effect (Synergy) in Regression Analysis
Great course. Thanks to the instructor, The rhyme platform is sometimes very slow, content: (7/10),Audio clarity: (5/10), video clarity: (8/10), Rhyme platform performance: (4/10).
Very good for freshers. Discussed the basic concepts and implemented them. They have a virtual computer so you need not install or download anything.
Nice project for beginners. In the last video, there was a very useful concept of synergy which could be helpful for intermediate learners.
This project is great. Clearly explained and well delivered. I will highly recommend to take this project. The instructor is great!
指导 项目 可在台式设备和移动设备上学习吗？
指导 项目 的讲师是谁？
指导 项目 讲师是特定领域的专家，他们在项目的技能、工具或领域方面经验丰富，并且热衷于分享自己的知识以影响全球数百万的学生。
我能在完成指导 项目 后从中下载作品吗？
您可以从指导 项目 中下载并保留您创建的任何文件。为此，您可以在访问云桌面时使用'文件浏览器'功能。
您可在页面顶部点按此指导 项目 的经验级别，查看任何知识先决条件。对于指导 项目 的每个级别，您的讲师会逐步为您提供指导。
我能直接通过 Web 浏览器来完成此指导 项目，而不必安装特殊软件吗？
是，您可以在浏览器的云桌面中获得完成指导 项目 所需的一切。
指导 项目 的学习体验如何？