# Principal Component Analysis with NumPy

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En este proyecto guiado, tú:

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

1.5 hours
Intermedio
No se necesita descarga
Video de pantalla dividida
Inglés (English)
Solo escritorio

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.

## Habilidades que desarrollarás

Data SciencePython ProgrammingSeabornNumpyPCA

## Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

1. Introduction and Overview

2. Load the Data and Libraries

3. Visualize the Data

4. Data Standardization

5. Compute the Eigenvectors and Eigenvalues

6. Singular Value Decomposition (SVD)

7. Selecting Principal Components Using the Explained Variance

8. Project Data Onto a Lower-Dimensional Linear Subspace

## Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

## Preguntas Frecuentes

¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.