Linear Regression with NumPy and Python
Implement the gradient descent algorithm from scratch
Perform univariate linear regression with Numpy and Python
Create data visualizations and plots using matplotlib
Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. 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, you’ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed.
您要培养的技能
分步进行学习
在与您的工作区一起在分屏中播放的视频中，您的授课教师将指导您完成每个步骤：
Introduction and Overview
Load the Data and Libraries
Visualize the Data
Compute the Cost Function 𝐽(𝜃)
Gradient Descent
Visualize the Cost Function 𝐽(𝜃)
Plot the Convergence
Training Data with Univariate Linear Regression Fit
Inference using the optimized 𝜃 values
指导项目工作原理
您的工作空间就是浏览器中的云桌面，无需下载
在分屏视频中，您的授课教师会为您提供分步指导
审阅
来自LINEAR REGRESSION WITH NUMPY AND PYTHON的热门评论
Great course , concepts were also explained nicely but somewhere I felt lost and was like what's going on . But rather than that everything was great and great experiencing ML while viewing it.
Good for those with an understanding of regression and the maths behind. Ideal for those who just need help implementing the concept in code as opposed to learn it for the first time.
It was nice to know how to implement the knowledge I have already gathered. Some prior experience of basic level surely required to understand effectively. Overall worth mine time.
Cloud Server was lagging very much. Content was great. Despite of Linear Regression being one of the most basic algorithms, I got to learn quite a few things. Thank You!
常见问题
常见问题
如果我购买指导项目，会得到什么？
购买指导项目后，您将获得完成指导项目所需的一切，包括通过 Web 浏览器访问云桌面工作空间，工作空间中包含您需要了解的文件和软件，以及特定领域的专家提供的分步视频说明。
指导项目可在台式设备和移动设备上学习吗？
由于您的工作空间包含适合笔记本电脑或台式计算机使用的云桌面，因此指导项目不在移动设备上提供。
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。
我能够退款吗？退款政策是如何规定的？
指导项目不符合退款条件。请查看我们完整的退款政策。
有助学金吗？
指导项目不提供助学金。
我能旁听指导项目并免费观看视频部分吗？
指导项目不支持旁听。
我需要具备多少经验才能做这个指导项目？
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？
您可以直接在浏览器中于分屏环境下完成任务，以此从做中学。在屏幕的左侧，您将在工作空间中完成任务。在屏幕的右侧，您将看到有讲师逐步指导您完成项目。
还有其他问题吗？请访问 学生帮助中心。
