Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Learning Data Mining with Python
  • Table Of Contents Toc
  • Feedback & Rating feedback
Learning Data Mining with Python

Learning Data Mining with Python

By : Robert Layton
3.7 (7)
Buy this Book
close
close
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)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Free Chapter
1
1. Getting Started with Data Mining
Icon
1. Getting Started with Data Mining
Icon
Introducing data mining
Icon
Using Python and the IPython Notebook
Icon
A simple affinity analysis example
Icon
A simple classification example
Icon
What is classification?
Icon
Summary
2
2. Classifying with scikit-learn Estimators
Icon
2. Classifying with scikit-learn Estimators
Icon
scikit-learn estimators
Icon
Preprocessing using pipelines
Icon
Pipelines
Icon
Summary
3
3. Predicting Sports Winners with Decision Trees
Icon
3. Predicting Sports Winners with Decision Trees
Icon
Loading the dataset
Icon
Decision trees
Icon
Sports outcome prediction
Icon
Random forests
Icon
Summary
4
4. Recommending Movies Using Affinity Analysis
Icon
4. Recommending Movies Using Affinity Analysis
Icon
Affinity analysis
Icon
The movie recommendation problem
Icon
The Apriori implementation
Icon
Extracting association rules
Icon
Summary
5
5. Extracting Features with Transformers
Icon
5. Extracting Features with Transformers
Icon
Feature extraction
Icon
Feature selection
Icon
Feature creation
Icon
Creating your own transformer
Icon
Summary
6
6. Social Media Insight Using Naive Bayes
Icon
6. Social Media Insight Using Naive Bayes
Icon
Disambiguation
Icon
Text transformers
Icon
Naive Bayes
Icon
Application
Icon
Summary
7
7. Discovering Accounts to Follow Using Graph Mining
Icon
7. Discovering Accounts to Follow Using Graph Mining
Icon
Loading the dataset
Icon
Finding subgraphs
Icon
Summary
8
8. Beating CAPTCHAs with Neural Networks
Icon
8. Beating CAPTCHAs with Neural Networks
Icon
Artificial neural networks
Icon
Creating the dataset
Icon
Training and classifying
Icon
Improving accuracy using a dictionary
Icon
Summary
9
9. Authorship Attribution
Icon
9. Authorship Attribution
Icon
Attributing documents to authors
Icon
Function words
Icon
Support vector machines
Icon
Character n-grams
Icon
Using the Enron dataset
Icon
Summary
10
10. Clustering News Articles
Icon
10. Clustering News Articles
Icon
Obtaining news articles
Icon
Extracting text from arbitrary websites
Icon
Grouping news articles
Icon
Clustering ensembles
Icon
Online learning
Icon
Summary
11
11. Classifying Objects in Images Using Deep Learning
chevron up
Icon
11. Classifying Objects in Images Using Deep Learning
Icon
Object classification
Icon
Application scenario and goals
Icon
Deep neural networks
Icon
GPU optimization
Icon
Setting up the environment
Icon
Application
Icon
Summary
12
12. Working with Big Data
Icon
12. Working with Big Data
Icon
Big data
Icon
Application scenario and goals
Icon
MapReduce
Icon
Application
Icon
Summary
13
A. Next Steps…
Icon
A. Next Steps…
Icon
Chapter 1 – Getting Started with Data Mining
Icon
Chapter 2 – Classifying with scikit-learn Estimators
Icon
Chapter 3: Predicting Sports Winners with Decision Trees
Icon
Chapter 4 – Recommending Movies Using Affinity Analysis
Icon
Chapter 5 – Extracting Features with Transformers
Icon
Chapter 6 – Social Media Insight Using Naive Bayes
Icon
Chapter 7 – Discovering Accounts to Follow Using Graph Mining
Icon
Chapter 8 – Beating CAPTCHAs with Neural Networks
Icon
Chapter 9 – Authorship Attribution
Icon
Chapter 10 – Clustering News Articles
Icon
Chapter 11: Classifying Objects in Images Using Deep Learning
Icon
Chapter 12 – Working with Big Data
Icon
More resources
14
Index
Icon
Index

Application

Back on your main computer now, open the first IPython Notebook we created in this chapter—the one that we loaded the CIFAR dataset with. In this major experiment, we will take the CIFAR dataset, create a deep convolution neural network, and then run it on our GPU-based virtual machine.

Getting the data

To start with, we will take our CIFAR images and create a dataset with them. Unlike previously, we are going to preserve the pixel structure—that is,. in rows and columns. First, load all the batches into a list:

import numpy as np
batches = []
for i in range(1, 6):
    batch_filename = os.path.join(data_folder, "data_batch_{}".format(i))
    batches.append(unpickle(batch1_filename))
    break

The last line, the break, is to test the code—this will drastically reduce the number of training examples, allowing you to quickly see if your code is working. I'll prompt you later to remove this line, after you have tested that the code works.

Next, create...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
Start a 7-day FREE trial
End of Section 7
Next Section

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist download
font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY