Principal Component Analysis with NumPy

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In diesem Guided Project werden Sie:

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
IntermediateMittel
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

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.

Kompetenzen, die Sie erwerben werden

Data SciencePython ProgrammingSeabornNumpyPCA

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  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

How Guided Projects work

Ihr Arbeitsbereich ist ein Cloud-Desktop direkt in Ihrem Browser, kein Download erforderlich

Ihr Dozent leitet Sie in einem Video mit geteiltem Bildschirm Schritt für Schritt an.

Häufig gestellte Fragen

Häufig gestellte Fragen

  • By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

  • Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.

  • Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.

  • You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.

  • Guided Projects are not eligible for refunds. Lesen Sie unsere komplette Rückerstattungsrichtlinie.

  • Financial aid is not available for Guided Projects.

  • Auditing is not available for Guided Projects.

  • At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.

  • Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

  • Sie lernen durch Praxis, indem Sie Aufgaben in einer Split-Screen-Umgebung direkt in Ihrem Browser erledigen. Auf der linken Seite des Bildschirms erledigen Sie die Aufgabe in Ihrem Arbeitsbereich. Auf der rechten Seite des Bildschirms sehen Sie einen Dozenten, der Sie schrittweise durch das Projekt führt.

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