What Is Pandas Python Library?
March 21, 2024
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Instructor: Snehan Kekre
12,774 already enrolled
Included with
(390 reviews)
Recommended experience
Beginner level
Prior programming experience in Python and machine learning theory is recommended.
(390 reviews)
Recommended experience
Beginner level
Prior programming experience in Python and machine learning theory is recommended.
Implement the gradient descent algorithm from scratch
Perform logistic regression with NumPy and Python
Create data visualizations with Matplotlib and Seaborn
Add to your LinkedIn profile
Only available on desktop
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
Prior programming experience in Python and machine learning theory is recommended.
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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Practice new skills by completing job-related tasks.
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Follow along with pre-recorded videos from experts using a unique side-by-side interface.
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Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
390 reviews
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Reviewed on Aug 29, 2020
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
Reviewed on Apr 3, 2020
Thank You... Very nice and valuable knowledge provided.
Reviewed on Jul 14, 2020
Gain more understanding about LR and gradient descent practically.
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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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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.