Linear Regression with NumPy and Python
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.
Skills you will develop
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:
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
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
Reviews
TOP REVIEWS FROM LINEAR REGRESSION WITH NUMPY AND PYTHON
Great course , concepts were also explained nicely but somewhere I felt lost and was like what's going on . But rather than that everything was great and great experiencing ML while viewing it.
Good 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.
It was nice to know how to implement the knowledge I have already gathered. Some prior experience of basic level surely required to understand effectively. Overall worth mine time.
Cloud Server was lagging very much. Content was great. Despite of Linear Regression being one of the most basic algorithms, I got to learn quite a few things. Thank You!
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