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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
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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
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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

Decision trees

Decision trees are a class of supervised learning algorithm like a flow chart that consists of a sequence of nodes, where the values for a sample are used to make a decision on the next node to go to.

Decision trees

As with most classification algorithms, there are two components:

  • The first is the training stage, where a tree is built using training data. While the nearest neighbor algorithm from the previous chapter did not have a training phase, it is needed for decision trees. In this way, the nearest neighbor algorithm is a lazy learner, only doing any work when it needs to make a prediction. In contrast, decision trees, like most classification methods, are eager learners, undertaking work at the training stage.
  • The second is the predicting stage, where the trained tree is used to predict the classification of new samples. Using the previous example tree, a data point of ["is raining", "very windy"] would be classed as "bad weather".

There are many algorithms...

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