287 ratings
9,046 already enrolled
Implement Principal Component Analysis (PCA) from scratch with NumPy and Python
Conduct basic exploratory data analysis (EDA)
Create simple data visualizations with Seaborn and Matplotlib
Welcome to this 2 hour long project-based course on Principal Component Analysis 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 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 implement and apply PCA from scratch using NumPy in Python, conduct basic exploratory data analysis, and create simple data visualizations with Seaborn and Matplotlib. 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.
Data Science
Python Programming
Seaborn
Numpy
PCA
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
Data Standardization
Compute the Eigenvectors and Eigenvalues
Singular Value Decomposition (SVD)
Selecting Principal Components Using the Explained Variance
Project Data Onto a Lower-Dimensional Linear Subspace
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 AA
Aug 4, 2020It's a good course for someone to try out his knowledge of the basic packages and the concepts and the maths behind PCA.
by TA
Oct 30, 2020Good Introductory project to gain insights into PCA using Numpy and python.
by TS
Oct 4, 2020V
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by HP
Sep 8, 2020This is a great project. The instructor facilitates clear and practically.
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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