Project: Logistic Regression with NumPy and Python

提供方
Rhyme
在此指导项目中,您将:

Implement the gradient descent algorithm from scratch

Perform logistic regression with NumPy and Python

Create data visualizations with Matplotlib and Seaborn

Clock1.5 hours
Beginner初级
Cloud无需下载
Video分屏视频
Comment Dots英语(English) + subtitles
Laptop不适用于移动设备

Welcome to this project-based course on Logistic 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, cost function, and logistic regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to build a logistic regression model using Python and NumPy, conduct basic exploratory data analysis, and implement gradient descent from scratch. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory. 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

Data ScienceMachine LearningPython ProgrammingclassificationNumpy

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,您的讲师将指导您完成每个步骤:

  1. Introduction and Project Overview

  2. Load the Data and Import Libraries

  3. Visualize the Data

  4. Define the Logistic Sigmoid Function 𝜎(𝑧)

  5. Compute the Cost Function 𝐽(𝜃) and Gradient

  6. Cost and Gradient at Initialization

  7. Implement Gradient Descent

  8. Plotting the Convergence of 𝐽(𝜃)

  9. Plotting the Decision Boundary

  10. Predictions 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虚拟桌面,无需下载。

在分屏视频中,您的讲师会为您提供分步指导

常见问题

常见问题

  • 购买项目后,您将获得完成项目所需的一切内容,包括通过 Web 浏览器访问云桌面工作空间,其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。

  • 因为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,所以项目不在移动设备上使用。

  • 项目讲师是特定领域的专家,他们在项目的技能、工具或领域上都很有经验,并且热衷于分享自己的知识以影响全球数百万的学生。

  • 您可以从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。

  • 项目没有助学金。

  • 您不需要任何前期经验即可开始项目。讲师将逐步指导您完成项目。

  • 是,您可以在浏览器的云桌面中获得完成项目所需的一切。

  • 您可以通过直接在浏览器中的分屏环境中完成项目来进行学习。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。