TensorFlow: K-means Clustering

TensorFlow    |    Intermediate
  • 15 videos | 59m 34s
  • Includes Assessment
  • Earns a Badge
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Discover how to differentiate between supervised and unsupervised machine learning techniques. The construction of clustering models and their application to classification problems is also covered.

WHAT YOU WILL LEARN

  • Distinguish between supervised and unsupervised learning algorithms
    Identify the characteristics of supervised learning algorithms
    Identify the characteristics of unsupervised learning algorithms
    Recognize use cases where unsupervised learning can be applied
    Define the objectives of clustering algorithms
    Describe the process of k-means clustering to group data
    Describe how to implement k-means clustering
  • Recall how to install tensorflow and work with jupyter notebooks
    Generate random data for clustering algorithms
    Perform k-means clustering using a tensorflow estimator
    Explore the iris dataset of flowers
    Perform clustering and classification on the iris dataset
    Recall characteristics of unsupervised learning algorithms
    Describe the process and use cases of clustering

IN THIS COURSE

  • 2m 18s
  • 4m 37s
    During this video, you will learn how to distinguish between supervised and unsupervised learning algorithms. FREE ACCESS
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    3.  Supervised Learning Characteristics
    4m 29s
    To find out how to identify the characteristics of supervised learning algorithms, consult a machine learning expert or read a machine learning textbook. FREE ACCESS
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    4.  Unsupervised Learning Characteristics
    3m 5s
    To find out how to identify the characteristics of unsupervised learning algorithms, consult a reliable source. FREE ACCESS
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    5.  Unsupervised Learning Use Cases
    3m 43s
    Upon completion of this video, you will be able to recognize use cases where unsupervised learning can be applied. FREE ACCESS
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    6.  Objectives of Clustering Techniques
    6m 26s
    Find out how to define the objectives of clustering algorithms. FREE ACCESS
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    7.  K-means Clustering
    3m 41s
    Upon completion of this video, you will be able to describe the process of k-means clustering and how it can be used to group data. FREE ACCESS
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    8.  K-means Clustering Algorithm
    3m 20s
    After completing this video, you will be able to describe how to implement k-means clustering. FREE ACCESS
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    9.  Install TensorFlow and Work with Jupyter Notebooks
    2m 21s
    Upon completion of this video, you will be able to recall how to install TensorFlow and work with Jupyter notebooks. FREE ACCESS
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    10.  Generate Random Data for K-means Clustering
    3m 5s
    In this video, you will learn how to generate random data for use with clustering algorithms. FREE ACCESS
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    11.  K-means Clustering Using Estimators
    7m 39s
    To find out how to perform k-means clustering using a TensorFlow estimator, consult TensorFlow's documentation. FREE ACCESS
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    12.  The Iris Dataset
    3m 44s
    In this video, you will explore the Iris dataset of flowers. FREE ACCESS
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    13.  Clustering the Iris Dataset
    4m 50s
    To find out how to perform clustering and classification on the Iris dataset, consult a statistics or machine learning textbook, or search for a tutorial online. FREE ACCESS
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    14.  Exercise: Working with Unsupervised Learning
    2m 50s
    Upon completion of this video, you will be able to recall characteristics of unsupervised learning algorithms. FREE ACCESS
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    15.  Exercise: Working with Clustering
    3m 26s
    After completing this video, you will be able to describe the process and use cases of clustering. FREE ACCESS

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