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Machine learning for hackers

Author: Drew Conway; John Myles White
Publisher: Sebastopol, CA : O'Reilly Media, 2012.
Edition/Format:   Print book : English : 1st edView all editions and formats
Summary:

Now that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to  Read more...

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Drew Conway; John Myles White
ISBN: 9781449303716 1449303714
OCLC Number: 783384312
Notes: "Case studies and algorithms to get you started"--Cover.
Description: xiii, 303 pages : illustrations ; 24 cm
Contents: Machine generated contents note: 1. Using R --
R for Machine Learning --
Downloading and Installing R --
IDEs and Text Editors --
Loading and Installing R Packages --
R Basics for Machine Learning --
Further Reading on R --
2. Data Exploration --
Exploration versus Confirmation --
What Is Data? --
Inferring the Types of Columns in Your Data --
Inferring Meaning --
Numeric Summaries --
Means, Medians, and Modes --
Quantiles --
Standard Deviations and Variances --
Exploratory Data Visualization --
Visualizing the Relationships Between Columns --
3. Classification: Spam Filtering --
This or That: Binary Classification --
Moving Gently into Conditional Probability --
Writing Our First Bayesian Spam Classifier --
Defining the Classifier and Testing It with Hard Ham --
Testing the Classifier Against All Email Types --
Improving the Results --
4. Ranking: Priority Inbox --
How Do You Sort Something When You Don't Know the Order? --
Ordering Email Messages by Priority. Contents note continued: Priority Features of Email --
Writing a Priority Inbox --
Functions for Extracting the Feature Set --
Creating a Weighting Scheme for Ranking --
Weighting from Email Thread Activity --
Training and Testing the Ranker --
5. Regression: Predicting Page Views --
Introducing Regression --
The Baseline Model --
Regression Using Dummy Variables --
Linear Regression in a Nutshell --
Predicting Web Traffic --
Defining Correlation --
6. Regularization: Text Regression --
Nonlinear Relationships Between Columns: Beyond Straight Lines --
Introducing Polynomial Regression --
Methods for Preventing Overfitting --
Preventing Overfitting with Regularization --
Text Regression --
Logistic Regression to the Rescue --
7. Optimization: Breaking Codes --
Introduction to Optimization --
Ridge Regression --
Code Breaking as Optimization --
8. PCA: Building a Market Index --
Unsupervised Learning --
9. MDS: Visually Exploring US Senator Similarity. Contents note continued: Clustering Based on Similarity --
A Brief Introduction to Distance Metrics and Multidirectional Scaling --
How Do US Senators Cluster? --
Analyzing US Senator Roll Call Data (101st--111th Congresses) --
10. kNN: Recommendation Systems --
The k-Nearest Neighbors Algorithm --
R Package Installation Data --
11. Analyzing Social Graphs --
Social Network Analysis --
Thinking Graphically --
Hacking Twitter Social Graph Data --
Working with the Google SocialGraph API --
Analyzing Twitter Networks --
Local Community Structure --
Visualizing the Clustered Twitter Network with Gephi --
Building Your Own "Who to Follow" Engine --
12. Model Comparison --
SVMs: The Support Vector Machine --
Comparing Algorithms.
Responsibility: Drew Conway and John Myles White.
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