Unsupervised Machine Learning for Customer Market Segmentation

4.8
별점
221개의 평가
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Coursera Project Network
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Understand how to leverage the power of machine learning to transform marketing departments and perform customer segmentation

Compile and fit unsupervised machine learning models such as PCA and K-Means to training data.

Understand the theory and intuition behind Principal Component Analysis (PCA) and k-means clustering machine learning algorithm

Learn how to obtain the optimal number of clusters using the elbow method

Clock2 hours
Beginner초급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

In this hands-on guided project, we will train unsupervised machine learning algorithms to perform customer market segmentation. Market segmentation is crucial for marketers since it enables them to launch targeted ad marketing campaigns that are tailored to customer's specific needs. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

개발할 기술

Artificial Intelligence (AI)Machine LearningclusteringPython Programmingunsupervised machine learning

단계별 학습

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

  1. Understand the problem statement and business case

  2. Import libraries and datasets

  3. Visualize and explore datasets

  4. Understand the theory and intuition behind k-means clustering machine learning algorithm

  5. Learn how to obtain the optimal number of clusters using the elbow method

  6. Use Scikit-Learn library to find the optimal number of clusters using elbow method

  7. Apply k-means using Scikit-Learn to perform customer segmentation

  8. Apply Principal Component Analysis (PCA) technique to perform dimensionality reduction and data visualization

안내형 프로젝트 진행 방식

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

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

검토

UNSUPERVISED MACHINE LEARNING FOR CUSTOMER MARKET SEGMENTATION의 최상위 리뷰

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