Unsupervised Machine Learning

This learning track introduces the fundamental concepts of dimensionality reduction through the PCA technique along with its Python implementation and explores the theory and implementation of various Unsupervised learning algorithms  like Hierarchical Clustering, Non-Hierarchical Clustering, K-Means Clustering along with Cluster analysis and evaluation.

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Unsupervised Machine Learning
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    Difficulty: Intermediate

    Foundational knowledge or experience in machine learning is recommended

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    Duration: Approximately 5 Weeks

    Suggested learning pace is 5hr/week

Course Overview

  • Learn the basic principles of dimensionality reduction techniques and their importance, along with the working and implementation of the PCA technique to deal with the high dimensionality of a dataset.
  • Understand the basic idea of Unsupervised Learning and the theoretical concepts behind various clustering algorithms.
  • Learn how to implement the various clustering algorithms in Python and perform detailed cluster analysis through visualizations.
  • Learn how to apply the concepts learned on live data across industries to generate insights.

What’s included

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Shareable Certificate

Earn a sharable certificate upon completion

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Lifetime Access

Access this learning track 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 learning track on a desktop

Skills You Will Learn

PCA

Objective Segmentation

Subjective Segmentation

Hierarchical Clustering

Non-Hierarchical Clustering

K-Means Clustering

Cluster Visualization

Cluster Evaluation

Cluster Analysis

Syllabus

  • Concepts and Application of Objective and Subjective Segmentation
  • Understanding Principal Component Analysis (PCA)
  • Clustering algorithms in Python
  • PCA in Python

  • Customer Segmentation in Retail – Application of K-Means Clustering Algorithm
  • Customer Segmentation in Insurance- Application of K-Means Clustering Algorithm
  • Application of Non-Hierarchical Clustering in HR Analytics Domain
  • Application of various clustering techniques to group the steel type based on its mechanical properties
  • Customer Segmentation Based on the Transaction History using Advance Clustering Techniques

How it Works

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Learner Outcomes

Complete learning tracks to earn sharable certificates and badges. These awarded items will be look great in your portfolio, resume, and LinkedIn as you showcase your skills and project experience to employers and colleagues.

  • Develop a working understanding of PCA, a dimensionality reduction technique, using Python.
  • Strong theoretical understanding of various clustering algorithms like Hierarchical, Non-Hierarchical and K-Means clustering.
  • Ability to implement different clustering algorithms to fulfil various objectives like segmentation, followed by a detailed analysis of the cluster results through visualization and relevant metrics.
  • Interpret and visualize the outcome of the various applied techniques.
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