Learning Data Mining with Python

Cover
Copyright
Credits
About the Author
About the Reviewers
www.PacktPub.com
Table of Contents
Preface
Chapter 1: Getting Started with Data Mining
Introducing data mining
Using Python and the IPython notebook
Installing Python
Installing IPython
Installing scikit-learn
A simple affinity analysis example
What is affinity analysis?
Product recommendations
Loading the dataset with NumPy
Implementing a simple ranking of rules
Ranking to find the best rules
A simple classification example
What is classification?
Loading and preparing the dataset
Implementing the OneR algorithm
Testing the algorithm
Summary
Chapter 2: Classifying with scikit-learn
scikit-learn estimators
Nearest neighbors
Distance metrics
Loading the dataset
Moving towards a standard workflow
Running the algorithm
Setting parameters
Preprocessing using pipelines
An example
Standard preprocessing
Putting it all together
Pipelines
Summary
Chapter 3: Predicting Sports Winners with Decision Trees
Loading the dataset
Collecting the data
Using pandas to load the dataset
Cleaning up the dataset
Extracting new features
Decision trees
Parameters in decision trees
Using decision trees
Sports outcome prediction
Putting it all together
Random forests
How do ensembles work?
Parameters in Random forests
Applying Random forests
Engineering new features
Summary
Chapter 4: Recommending Movies Using Affinity Analysis
Affinity analysis
Algorithms for affinity analysis
Choosing parameters
The movie recommendation problem
Obtaining the dataset
Loading with pandas
Sparse data formats
The Apriori implementation
The Apriori algorithm
Implementation
Extracting association rules
Evaluation
Summary
Chapter 5: Extracting Features with Transformers
Feature extraction
Representing reality in models
Common feature patterns
Creating good features
Feature selection
Selecting the best individual features
Feature creation
Principal Component Analysis
Creating your own transformer
The transformer API
Implementation details
Unit testing
Putting it all together
Summary
Chapter 6: Social Media Insight Using Naive Bayes
Disambiguation
Downloading data from a social network
Loading and classifying the dataset
Creating a replicable dataset from Twitter
Text transformers
Bag-of-words
N-grams
Other features
Naive Bayes
Bayes' theorem
Naive Bayes algorithm
How it works
Application
Extracting word counts
Converting dictionaries to a matrix
Training the Naive Bayes classifier
Putting it all together
Evaluation using the F1-score
Getting useful features from models
Summary
Chapter 7: Discovering Accounts to Follow Using Graph Mining
Loading the dataset
Classifying with an existing model
Getting follower information from Twitter
Building the network
Creating a graph
Creating a Similarity graph
Finding subgraphs
Connected components
Optimizing criteria
Summary
Chapter 8: Beating CAPTCHAs with Neural Networks
Artificial neural networks
An introduction to neural networks
Creating the dataset
Drawing basic CAPTCHAs
Splitting the image into individual letters
Creating a training dataset
Adjusting our training dataset to our methodology
Training and classifying
Back propagation
Predicting words
Improving accuracy using a dictionary
Ranking mechanisms for words
Putting it all together
Summary
Chapter 9: Authorship Attribution
Attributing documents to authors
Applications and use cases
Authorship attribution
Getting the data
Function words
Counting function words
Classifying with function words
Support vector machines
Classifying with SVMs
Kernels
Character n-grams
Extracting character n-grams
Using the Enron dataset
Accessing the Enron dataset
Creating a dataset loader
Putting it all together
Evaluation
Summary
Chapter 10: Clustering News Articles
Obtaining news articles
Using a Web API to get data
Reddit as a data source
Getting the data
Extracting text from arbitrary websites
Finding the stories in arbitrary websites
Putting it all together
Clustering news articles
The k-means algorithm
Evaluating the results
Extracting topic information from clusters
Using clustering algorithms as transformers
Clustering ensembles
Evidence accumulation
How it works
Implementation
Online learning
An introduction to online learning
Implementation
Summary
Chapter 11: Classifying Objects in Images Using Deep Learning
Object classification
Application scenario and goals
Use cases
Deep neural networks
Intuition
Implementation
An introduction to Theano
An introduction to Lasagne
Implementing neural networks with nolearn
GPU optimization
When to use GPUs for computation
Running our code on a GPU
Setting up the environment
Application
Getting the data
Creating the neural network
Putting it all together
Summary
Chapter 12: Working with Big Data
Big data
Application scenario and goals
MapReduce
Intuition
A word count example
Hadoop MapReduce
Application
Getting the data
Naive Bayes prediction
The mrjob package
Extracting the blog posts
Training Naive Bayes
Putting it all together
Training on Amazon's EMR infrastructure
Summary
Appendix: Next Steps…
Chapter 1 – Getting Started with Data Mining
Scikit-learn tutorials
Extending the IPython Notebook
Chapter 2 – Classifying with scikit-learn Estimators
Scalability with the nearest neighbor
More complex Pipelines
Comparing classifiers
Chapter 3: Predicting Sports Winners with Decision Trees
More on pandas
More complex features
Chapter 4 – Recommending Movies Using Affinity Analysis
New datasets
The Eclat algorithm
Chapter 5 – Extracting Features with Transformers
Adding noise
Vowpal Wabbit
Chapter 6 – Social Media Insight Using Naive Bayes
Spam detection
Natural language processing and part-of-speech tagging
Chapter 7 – Discovering Accounts to Follow Using Graph Mining
More complex algorithms
NetworkX
Chapter 8 – Beating CAPTCHAs with Neural Networks
Better (worse?) CAPTCHAs
Deeper networks
Reinforcement learning
Chapter 9 – Authorship Attribution
Increasing the sample size
Blogs dataset
Local n-grams
Chapter 10 – Clustering News Articles
Evaluation
Temporal analysis
Real-time clusterings
Chapter 11: Classifying Objects in Images Using Deep Learning
Keras and Pylearn2
Mahotas
Chapter 12 – Working with Big Data
Courses on Hadoop
Pydoop
Recommendation engine
More resources
Index