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  • Book Overview & Buying Learning Data Mining with Python
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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
2
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
3
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
6
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
12
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

Chapter 2. Classifying with scikit-learn Estimators

The scikit-learn library is a collection of data mining algorithms, written in Python and using a common programming interface. This allows users to easily try different algorithms as well as utilize standard tools for doing effective testing and parameter searching. There are a large number of algorithms and utilities in scikit-learn.

In this chapter, we focus on setting up a good framework for running data mining procedures. This will be used in later chapters, which are all focused on applications and techniques to use in those situations.

The key concepts introduced in this chapter are as follows:

  • Estimators: This is to perform classification, clustering, and regression
  • Transformers: This is to perform preprocessing and data alterations
  • Pipelines: This is to put together your workflow into a replicable format
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Tech Concepts
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Programming languages
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Learning Data Mining with Python
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