950 ratings
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Implement the gradient descent algorithm from scratch
Perform univariate linear regression with Numpy and Python
Create data visualizations and plots using matplotlib
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.
Data Science
Machine Learning
Python Programming
regression
Numpy
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction and Overview
Load the Data and Libraries
Visualize the Data
Compute the Cost Function 𝐽(𝜃)
Gradient Descent
Visualize the Cost Function 𝐽(𝜃)
Plot the Convergence
Training Data with Univariate Linear Regression Fit
Inference using the optimized 𝜃 values
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
by ZK
Oct 11, 2020Very well explained.... I learned a lot about what is happening behind the scene in Regression!!! Thanks Snehan!
by AD
May 24, 2020It is a great project and an excellent experience to learn practical exposure to Linear regression with nmpy and python. I am waiting to get another project.
by AA
Nov 1, 2020Really Good Content, I learnt more however the instructor didn't explain the mathematical expressions of Gradient Descent and matplotlib in details.
by GN
Jun 27, 2020Good for those with an understanding of regression and the maths behind. Ideal for those who just need help implementing the concept in code as opposed to learn it for the first time.
By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
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At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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