你将学到什么
Implement the gradient descent algorithm from scratch
Perform univariate linear regression with Numpy and Python
Create data visualizations and plots using matplotlib
课程概况
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.
课程大纲
Project: Linear Regression with NumPy and Python
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 of the various algorithms yourself, so you have a deeper understanding of the fundamentals.
课程项目
Introduction and Overview
Load the Data and Libraries
Visualize the Data
Compute the Cost Function ( )
Gradient Descent
Visualize the Cost Function ( )
Plot the Convergence
Training Data with Univariate Linear Regression Fit
Inference using the optimized values
