Course

Unsupervised Learning

Learn how to distill messy data into meaningful groups with unsupervised machine learning! By the end of this course, you’ll know how to perform cluster analyses and dimensionality reduction using Python’s powerful machine-learning package, scikit-learn.
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  • DAYS
  • HRS
  • MIN
  • SEC
  • Estimated Time
    15 hours

  • Enroll by
    September 22, 2021

    Get access to classroom immediately on enrollment

  • Prerequisites
    Intermediate Python, basic understanding of Machine Learning and Supervised Learning concepts

What You Will Learn

Syllabus

Unsupervised Learning

Unsupervised learning gives you the power to find common patterns within data without knowing what to look for, and its application is virtually limitless — from market segmentation to computer vision to fraud detection. In this course, you’ll learn a multitude of techniques, including hierarchical and density-based clustering, gaussian mixture models, cluster validation, principal component analysis (PCA), and independent component analysis (ICA). In addition, you’ll apply what you’ve learned to identify customer segments within complex demographic data for a mail-order sales company.

In this course, you’ll learn a multitude of techniques, including hierarchical and density-based clustering, gaussian mixture models, cluster validation, principal component analysis (PCA), and independent component analysis (ICA). In addition, you’ll apply what you’ve learned to identify customer segments within complex demographic data for a mail-order sales company.

Prerequisite Knowledge

Intermediate Python, basic understanding of Machine Learning and Supervised Learning concepts.

  • Clustering

    Learn the basics of clustering data and cluster data with the K-means algorithm.

  • Hierarchical and Density-Based Clustering

    Cluster data with Single Linkage Clustering and DBSCAN, a clustering method that captures the insight that clusters are dense group of points.

  • Gaussian Mixture Models

    Cluster data with Gaussian Mixture Models and optimize Gaussian Mixture Models with Expectation Maximization.

  • Dimensionality Reduction

    Reduce the dimensionality of the data using Principal Component Analysis and Independent Component Analysis.

  • Course Project: Creating Customer Segments

    In this project, you will apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data.

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Unsupervised Learning

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    • Maximum flexibility to learn at your own pace.
    • Cancel anytime.
  • Learn

    How to cluster and decompose data at will with Python’s powerful machine-learning library, scikit-learn.
  • Average Time

    On average, successful students take 15 hours to complete this program.
  • Benefits include

    • Real-world projects from industry experts
    • Technical mentor support

Program Details

  • Do I need to apply? What are the admission criteria?
    No. This Nanodegree program accepts all applicants regardless of experience and specific background.
  • What are the prerequisites for enrollment?

    In order to succeed in this program, we recommend having:

    • Experience in Intermediate Python.
    • Basic understanding of machine learning concepts such as model training and validating.
    • Basic understanding of supervised learning concepts such as labels, prediction, forecasting, and linear regression.
    • Basic mathematics and statistics knowledge to perform simple probability calculations.
  • How is this course structured?
    The Unsupservised Learning course is comprised of content and curriculum to support one project. We estimate that students can complete the program in 15 hours.

    The project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
  • How long is this course?
    Access to this course runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our programs.
  • Can I switch my start date? Can I get a refund?
    Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.
  • What software and versions will I need in this course?
    You will need a computer running a 64-bit operating system with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.x and supporting packages. Most modern Windows, OS X, and Linux laptops or desktop will work well; we do not recommend a tablet since they typically have less computing power. We will provide you with instructions to install the required software packages.

Unsupervised Learning

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