Principal Component Analysis (PCA) in Python

This course introduces the fundamental concepts of dimensionality reduction and explores the technique of PCA in detail along with its Python implementation on a real-world business scenario.

  • icon-videos 1 Video
  • icon-data 1 Coding Case
  • icon-reading 1 Reading
  • icon-quiz 1 Quiz
principal component analysis in python
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    Difficulty: Beginner

    No prior education or professional experience required

  • Asset 1
    Duration: Approximately 3 hours

Course Overview

  • Many machine learning problems involve thousands or millions of features in the data. With these many features, not only is the processing is slow but finding the optimal solution is also challenging.
  • This problem is handled using the dimensionality reduction algorithm. One such technique is Principal Component Analysis or PCA.
  • Learn all the fundamental concepts behind the working of PCA technique and its implementation in Python.
  • Gain practical experience by applying the knowledge and coding skills on real-world based business scenarios.

What’s included

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Lifetime Access

Access this project for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend completing this project on a desktop

Skills you will learn

Introduction to Dimensionality Reduction

Fundamental Concepts of Principal Component Analysis (PCA)

Implementation of PCA in Python Programming Language

Dimensionality Reduction

Feature Selection

Theory Behind PCA

java

Javascript

Associated Learning Tracks

Syllabus

This course focuses on explaining Principal Component Analysis and implementing in Python programming language.

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