Linear Regression with Python

4.6
385 個評分
提供方
Coursera Project Network
9,049 人已註冊
在此指導項目中,您將:

Create a linear model, and implement gradient descent.

Train the linear model to fit given data using gradient descent.

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process. Since this is a practical, project-based course, you will need to have a theoretical understanding of linear regression, and gradient descent. We will focus on the practical aspect of implementing linear regression with gradient descent, but not on the theoretical aspect. Note: 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.

您要培養的技能

Data ScienceDeep LearningMachine LearningPython ProgrammingLinear Regression

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

  2. Dataset

  3. Initialize Parameters

  4. Forward Pass

  5. Compute Loss

  6. Backward Pass

  7. Update Parameters

  8. Training Loop

  9. Predictions

  10. Additional Example

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

審閱

來自LINEAR REGRESSION WITH PYTHON的熱門評論

查看所有評論

常見問題

常見問題

還有其他問題嗎?請訪問 學生幫助中心