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Learning Data Mining with Python

Learning Data Mining with Python

By : Robert Layton
3.7 (7)
Buy this Book
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Learning Data Mining with Python

Learning Data Mining with Python

3.7 (7)
By: Robert Layton
Buy this Book

Overview of this book

If you are a programmer who wants to get started with data mining, then this book is for you.
Table of Contents (15 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Started with Data Mining
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1. Getting Started with Data Mining
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Introducing data mining
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Using Python and the IPython Notebook
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A simple affinity analysis example
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A simple classification example
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What is classification?
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Summary
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2. Classifying with scikit-learn Estimators
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2. Classifying with scikit-learn Estimators
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scikit-learn estimators
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Preprocessing using pipelines
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Pipelines
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Summary
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3. Predicting Sports Winners with Decision Trees
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3. Predicting Sports Winners with Decision Trees
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Loading the dataset
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Decision trees
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Sports outcome prediction
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Random forests
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Summary
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4. Recommending Movies Using Affinity Analysis
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4. Recommending Movies Using Affinity Analysis
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Affinity analysis
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The movie recommendation problem
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The Apriori implementation
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Extracting association rules
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Summary
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5. Extracting Features with Transformers
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5. Extracting Features with Transformers
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Feature extraction
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Feature selection
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Feature creation
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Creating your own transformer
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Summary
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6. Social Media Insight Using Naive Bayes
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6. Social Media Insight Using Naive Bayes
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Disambiguation
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Text transformers
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Naive Bayes
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Application
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Summary
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7. Discovering Accounts to Follow Using Graph Mining
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7. Discovering Accounts to Follow Using Graph Mining
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Loading the dataset
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Finding subgraphs
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Summary
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8. Beating CAPTCHAs with Neural Networks
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8. Beating CAPTCHAs with Neural Networks
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Artificial neural networks
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Creating the dataset
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Training and classifying
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Improving accuracy using a dictionary
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Summary
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9. Authorship Attribution
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9. Authorship Attribution
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Attributing documents to authors
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Function words
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Support vector machines
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Character n-grams
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Using the Enron dataset
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Summary
10
10. Clustering News Articles
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10. Clustering News Articles
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Obtaining news articles
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Extracting text from arbitrary websites
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Grouping news articles
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Clustering ensembles
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Online learning
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Summary
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11. Classifying Objects in Images Using Deep Learning
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11. Classifying Objects in Images Using Deep Learning
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Object classification
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Application scenario and goals
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Deep neural networks
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GPU optimization
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Setting up the environment
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Application
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Summary
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12. Working with Big Data
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12. Working with Big Data
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Big data
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Application scenario and goals
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MapReduce
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Application
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Summary
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A. Next Steps…
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A. Next Steps…
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Chapter 1 – Getting Started with Data Mining
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Chapter 2 – Classifying with scikit-learn Estimators
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Chapter 3: Predicting Sports Winners with Decision Trees
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Chapter 4 – Recommending Movies Using Affinity Analysis
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Chapter 5 – Extracting Features with Transformers
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Chapter 6 – Social Media Insight Using Naive Bayes
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Chapter 7 – Discovering Accounts to Follow Using Graph Mining
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Chapter 8 – Beating CAPTCHAs with Neural Networks
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Chapter 9 – Authorship Attribution
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Chapter 10 – Clustering News Articles
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Chapter 11: Classifying Objects in Images Using Deep Learning
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Chapter 12 – Working with Big Data
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More resources
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Index
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Index

Appendix A. Next Steps…

During the course of this book, there were lots of avenues not taken, options not presented, and subjects not fully explored. In this Appendix, I've created a collection of next steps for those wishing to undertake extra learning and progress their data mining with Python. Consider this Hero mode, the second question, of the book.

This appendix is broken up by chapter, with articles, books, and other resources for learning more about data mining. Also included are some challenges to extend the work performed in the chapter. Some of these will be small improvements; some will be quite a bit more work—I've made a note on those tasks that are noticeably more extensive than the others.

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