Principal Component Analysis with NumPy
286 個評分

8,914 人已註冊
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
286 個評分
8,914 人已註冊
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
在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:
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
您的工作空間就是瀏覽器中的雲桌面,無需下載
在分屏視頻中,您的授課教師會為您提供分步指導
由 TA 提供
2020年10月30日Good Introductory project to gain insights into PCA using Numpy and python.
由 HP 提供
2020年9月8日This is a great project. The instructor facilitates clear and practically.
由 PP 提供
2020年5月31日Course is amazing, got many concepts clear, learned a lot. Would also be great if more than one datasets are taken as excercise.
由 AA 提供
2020年8月4日It's a good course for someone to try out his knowledge of the basic packages and the concepts and the maths behind PCA.
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