Linear Regression with NumPy and Python

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In this Guided Project, you will:

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
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

Data ScienceMachine LearningPython ProgrammingregressionNumpy

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  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

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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