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Applied Unsupervised Learning with Python Cover

Applied Unsupervised Learning with Python

Discover hidden patterns and relationships in unstructured data with Python

Publisher:Packt Publishing Limited
By: ,   and    
Paid access
|May 2019
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Description

Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured and unlabeled data

Key Features

  • Learn how to select the most suitable Python library to solve your problem
  • Compare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use them
  • Explore the applications of neural networks using real-world datasets

Book Description

Unsupervised learning is a useful and practical solution in situations where labeled data is not available.

Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products.

By the end of this book, you will have the skills you need to confidently build your own models using Python.

What you will learn

  • Understand the basics and importance of clustering
  • Build k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages
  • Explore dimensionality reduction and its applications
  • Use scikit-learn (sklearn) to implement and analyze principal component analysis (PCA) on the Iris dataset
  • Employ Keras to build autoencoder models for the CIFAR-10 dataset
  • Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data

Who this book is for

This course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.

Table of Contents

  1. Introduction to Clustering
  2. Hierarchical Clustering
  3. Neighborhood Approaches and DBSCAN
  4. An Introduction to Dimensionality Reduction and PCA
  5. Autoencoders
  6. t-Distributed Stochastic Neighbor Embedding (t-SNE)
  7. Topic Modeling
  8. Market Basket Analysis
  9. Hotspot Analysis
PDF ISBN: 978-1-78995-837-9
Publisher: Packt Publishing Limited
Copyright owner: © 2019 Packt Publishing
Publication date: 2019
Language: English
Pages: 482
Related subjects:
Computer sciences,
Computer sciences, other
BISAC subjects:
COM021000,
COM051360,
COM089000
Thema subjects:
UN,
UMX,
UYZF,
UNC

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