Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Harness the power of Python to analyze data and create insightful predictive models

Arrow left icon
Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784396053
Length 344 pages
Edition 1st Edition
Languages
Tools
Concepts
Arrow right icon
Author (1):
Arrow left icon
Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Extracting Features with Transformers 6. Social Media Insight Using Naive Bayes 7. Discovering Accounts to Follow Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Classifying Objects in Images Using Deep Learning 12. Working with Big Data A. Next Steps… Index

What this book covers

Chapter 1, Getting Started with Data Mining, introduces the technologies we will be using, along with implementing two basic algorithms to get started.

Chapter 2, Classifying with scikit-learn Estimators, covers classification, which is a key form of data mining. You'll also learn about some structures to make your data mining experimentation easier to perform..

Chapter 3, Predicting Sports Winners with Decision Trees, introduces two new algorithms, Decision Trees and Random Forests, and uses them to predict sports winners by creating useful features.

Chapter 4, Recommending Movies Using Affinity Analysis, looks at the problem of recommending products based on past experience and introduces the Apriori algorithm.

Chapter 5, Extracting Features with Transformers, introduces different types of features you can create and how to work with different datasets.

Chapter 6, Social Media Insight Using Naive Bayes, uses the Naive Bayes algorithm to automatically parse text-based information from the social media website, Twitter.

Chapter 7, Discovering Accounts to Follow Using Graph Mining, applies cluster and network analysis to find good people to follow on social media.

Chapter 8, Beating CAPTCHAs with Neural Networks, looks at extracting information from images and then training neural networks to find words and letters in those images.

Chapter 9, Authorship Attribution, looks at determining who wrote a given document, by extracting text-based features and using support vector machines.

Chapter 10, Clustering News Articles, uses the k-means clustering algorithm to group together news articles based on their content.

Chapter 11, Classifying Objects in Images Using Deep Learning, determines what type of object is being shown in an image, by applying deep neural networks.

Chapter 12, Working with Big Data, looks at workflows for applying algorithms to big data and how to get insight from it.

Appendix, Next Steps…, goes through each chapter, giving hints on where to go next for a deeper understanding of the concepts introduced.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime