Unsupervised Learning

Machine Learning    |    Beginner
  • 12 videos | 25m 45s
  • Includes Assessment
  • Earns a Badge
Rating 4.1 of 67 users Rating 4.1 of 67 users (67)
Unsupervised learning can provide powerful insights on data without the need to annotate examples. Explore unsupervised learning, clustering, anomaly detection, and dimensional reduction.

WHAT YOU WILL LEARN

  • Describe unsupervised learning and some of the problems it can solve
    Describe rule association and how the apriori algorithm performs tasks
    Use the apriori algorithm for rule association in python
    Describe clustering and the types of problems it applies to
    Describe the k-means clustering algorithm
    Use scikit-learn to build clusters in python
  • Describe anomaly detection, the types of problems solved with anomaly detection, and some approaches to anomaly detection
    Use scikit-learn to perform anomaly detection
    Describe the problems with dimensionality and why efforts to reduce dimensionality should be taken
    Describe principal component analysis for dimensionality reduction
    Use scikit-learn to perform dimensionality reduction
    Perform dimensionality reduction and clustering tasks in python

IN THIS COURSE

  • 2m 22s
    After completing this video, you will be able to describe unsupervised learning and some of the problems it can solve. FREE ACCESS
  • 1m 44s
    Upon completion of this video, you will be able to describe rule association and how the Apriori algorithm performs tasks. FREE ACCESS
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    3.  A Priori in Python
    3m 22s
    In this video, you will learn how to use the Apriori algorithm for rule association in Python. FREE ACCESS
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    4.  Clustering
    1m 39s
    After completing this video, you will be able to describe clustering and the types of problems it can help solve. FREE ACCESS
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    5.  K-means Clustering
    1m 48s
    After completing this video, you will be able to describe the k-means clustering algorithm. FREE ACCESS
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    6.  Clustering in Python
    1m 38s
    In this video, you will learn how to use scikit-learn to build clusters in Python. FREE ACCESS
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    7.  Anomaly Detection
    2m 20s
    After completing this video, you will be able to describe anomaly detection, the types of problems that can be solved with anomaly detection, and some approaches to anomaly detection. FREE ACCESS
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    8.  Anomaly Detection in Python
    2m 5s
    In this video, you will use scikit-learn to perform anomaly detection. FREE ACCESS
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    9.  Dimensionality Reduction
    2m 1s
    After completing this video, you will be able to describe the problems with dimensionality and why efforts to reduce dimensionality should be made. FREE ACCESS
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    10.  Principal Component Analysis
    1m 36s
    After completing this video, you will be able to describe principal component analysis for reducing dimensions. FREE ACCESS
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    11.  Dimensionality Reduction in Python
    3m 6s
    In this video, find out how to use scikit-learn to perform dimensionality reduction. FREE ACCESS
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    12.  Exercise: Unsupervised Learning in Python
    2m 5s
    During this video, you will learn how to perform dimensionality reduction and clustering tasks using Python. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

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