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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.
Data Science
Machine Learning
Python Programming
regression
Numpy
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
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
由 MK 提供
Jun 12, 2020Good learning experience, have a basic background of coding and you'll follow the tutorials easily.
由 AV 提供
Jun 12, 2020It 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.
由 VB 提供
Jul 9, 2020Best Project ever we have seen, all plotting and code are explain in very well manner and its definitely increase my knowledge in machine learning
由 AA 提供
Jun 28, 2020Good course but make sure to have some previous knowledge of linear algebra especially matrix multiplication.
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指导项目授课教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。
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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。
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，您将看到有授课教师逐步指导您完成项目。
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