Principal Component Analysis with NumPy

4.6
별점
278개의 평가
제공자:
Coursera Project Network
8,378명이 이미 등록했습니다.
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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

Clock1.5 hours
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

  • Data Science
  • Python Programming
  • Seaborn
  • Numpy
  • PCA

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

검토

PRINCIPAL COMPONENT ANALYSIS WITH NUMPY의 최상위 리뷰

모든 리뷰 보기

자주 묻는 질문

자주 묻는 질문

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.