Back to Principal Component Analysis with NumPy
Learner Reviews & Feedback for Principal Component Analysis with NumPy by Coursera Project Network
280 ratings
About the Course
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.
Top reviews
TS
Oct 4, 2020
V\n\ne\n\nr\n\ny\n\ng\n\no\n\no\n\nd\n\nG\n\nu\n\ni\n\nd\n\ne\n\nd\n\ne\n\np\n\nr\n\no\n\nj\n\ne\n\nc\n\nt
TA
Oct 30, 2020
Good Introductory project to gain insights into PCA using Numpy and python.
Loading...
Coursera Footer
© 2022 Coursera Inc. All rights reserved.
