Coursera MOOC / Non-credit 0

Principal Component Analysis with NumPy

Name: Principal Component Analysis with NumPy
Price: 9.99 USD
Availability: InStock

About this 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.

63/100

CourseAsk score

What the provider tells you: 39/45
Who stands behind it: 8/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

Implement Principal Component Analysis from scratch using NumPy without relying on scikit-learn or other ML libraries
Perform exploratory data analysis on datasets
Create data visualizations using Seaborn and Matplotlib
Understand the underlying mathematics and mechanics of PCA through hands-on implementation

Course objectives

Build foundational understanding of PCA by implementing the algorithm manually
Apply dimensionality reduction techniques to real datasets
Develop proficiency in NumPy for numerical computing tasks

Machine Learning Data Analysis #python #data visualization #principal component analysis #pca #numpy #dimensionality reduction #exploratory data analysis #seaborn #matplotlib #jupyter #unsupervised learning #machine learning fundamentals #numerical computing #feature extraction

$9.99

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