Part 5: Learning from Data
by Luca Massaron, John Paul Mueller
Python for Data Science For Dummies, 2nd Edition
- Cover
- Introduction
- Part 1: Getting Started with Data Science and Python
- Part 2: Getting Your Hands Dirty with Data
- Part 3: Visualizing Information
- Part 4: Wrangling Data
- Part 5: Learning from Data
- Part 6: The Part of Tens
- Chapter 21: Ten Essential Data Resources
- Discovering the News with Subreddit
- Getting a Good Start with KDnuggets
- Locating Free Learning Resources with Quora
- Gaining Insights with Oracle’s Data Science Blog
- Accessing the Huge List of Resources on Data Science Central
- Learning New Tricks from the Aspirational Data Scientist
- Obtaining the Most Authoritative Sources at Udacity
- Receiving Help with Advanced Topics at Conductrics
- Obtaining the Facts of Open Source Data Science from Masters
- Zeroing In on Developer Resources with Jonathan Bower
- Chapter 22: Ten Data Challenges You Should Take
- Meeting the Data Science London + Scikit-learn Challenge
- Predicting Survival on the Titanic
- Finding a Kaggle Competition that Suits Your Needs
- Honing Your Overfit Strategies
- Trudging Through the MovieLens Dataset
- Getting Rid of Spam E-mails
- Working with Handwritten Information
- Working with Pictures
- Analyzing Amazon.com Reviews
- Interacting with a Huge Graph
- Chapter 21: Ten Essential Data Resources
- Index
- About the Authors
- Advertisement Page
- Connect with Dummies
- End User License Agreement
Part 5
Learning from Data
IN THIS PART …
Understanding four basic, but essential, algorithms.
Employing cross-validation, selection, and optimization techniques.
Using linear and nonlinear tricks to increase complexity.
Working with data ensembles to produce a better result.
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