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Hands-On Unsupervised Learning with Python

You're reading from   Hands-On Unsupervised Learning with Python Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789348279
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Unsupervised Learning FREE CHAPTER 2. Clustering Fundamentals 3. Advanced Clustering 4. Hierarchical Clustering in Action 5. Soft Clustering and Gaussian Mixture Models 6. Anomaly Detection 7. Dimensionality Reduction and Component Analysis 8. Unsupervised Neural Network Models 9. Generative Adversarial Networks and SOMs 10. Assessments 11. Other Books You May Enjoy

Technical requirements

The code presented in this chapter requires:

  • Python 3.5+ (Anaconda distribution: https://www.anaconda.com/distribution/is highly recommended)
  • Libraries:
    • SciPy 0.19+
    • NumPy 1.10+
    • scikit-learn 0.20+
    • pandas 0.22+
    • Matplotlib 2.0+
    • seaborn 0.9+

The dataset can be obtained through UCI. The CSV file can be downloaded from https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data and doesn't need any preprocessing except for the addition of the column names that will occur during the loading stage.

The examples are available on the GitHub repository:

https://github.com/PacktPublishing/HandsOn-Unsupervised-Learning-with-Python/Chapter02.

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