Linear Regression with NumPy and Python

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在此指導項目中,您將:

Implement the gradient descent algorithm from scratch

Perform univariate linear regression with Numpy and Python

Create data visualizations and plots using matplotlib

Clock1.5 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

Data ScienceMachine LearningPython ProgrammingregressionNumpy

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Introduction and Overview

  2. Load the Data and Libraries

  3. Visualize the Data

  4. Compute the Cost Function 𝐽(𝜃)

  5. Gradient Descent

  6. Visualize the Cost Function 𝐽(𝜃)

  7. Plot the Convergence

  8. Training Data with Univariate Linear Regression Fit

  9. Inference using the optimized 𝜃 values

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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