Implement the gradient descent algorithm from scratch
Perform logistic regression with NumPy and Python
Create data visualizations with Matplotlib and Seaborn
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.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction and Project Overview
Load the Data and Import Libraries
Visualize the Data
Define the Logistic Sigmoid Function 𝜎(𝑧)
Compute the Cost Function 𝐽(𝜃) and Gradient
Cost and Gradient at Initialization
Implement Gradient Descent
Plotting the Convergence of 𝐽(𝜃)
Plotting the Decision Boundary
Predictions 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
Very helpful for learning logistic regression without using any libraries. Before taking this project one should have a clear understanding of Logistic Regression, then it will be very helpful
Problem was that rhyme could not run for more than the alloted time because I had many errors in between because of which I couldn't complete my whole code in the given time.
Very Interesting and useful course. It helped me gain additional values and techniques about logistic regression
Its a good course. Instructor is good. Lot of concepts cleared and enough practice has done.
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