Learning Data Mining with Python

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Table of Contents Chapter 5 – Extracting Features with Transformers 301 Adding noise 301 Vowpal Wabbit 302 Chapter 6 – Social Media Insight Using Naive Bayes 302 Spam detection 302 Natural language processing and part-of-speech tagging 302 Chapter 7 – Discovering Accounts to Follow Using Graph Mining 303 More complex algorithms 303 NetworkX 303 Chapter 8 – Beating CAPTCHAs with Neural Networks 303 Better (worse?) CAPTCHAs 303 Deeper networks 304 Reinforcement learning 304 Chapter 9 – Authorship Attribution 304 Increasing the sample size 304 Blogs dataset 304 Local n-grams 305 Chapter 10 – Clustering News Articles 305 Evaluation 305 Temporal analysis 305 Real-time clusterings 306 Chapter 11: Classifying Objects in Images Using Deep Learning 306 Keras and Pylearn2 306 Mahotas 306 Chapter 12 – Working with Big Data 307 Courses on Hadoop 307 Pydoop 307 Recommendation engine 307 More resources 308 Index 309 [ vii ]
Page 1 and 2: [ 1 ] www.allitebooks.com
Page 3 and 4: Learning Data Mining with Python Co
Page 5 and 6: About the Author Robert Layton has
Page 7 and 8: Christophe Van Gysel is pursuing a
Page 9 and 10: www.allitebooks.com
Page 11 and 12: Table of Contents Preprocessing usi
Page 13 and 14: Table of Contents Chapter 7: Discov
Page 15: Table of Contents GPU optimization
Page 19 and 20: Preface What this book covers Chapt
Page 21 and 22: Preface Conventions In this book, y
Page 23 and 24: Preface Errata Although we have tak
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Page 48 and 49: Classifying with scikit-learn Estim
Page 50 and 51: Chapter 2 Nearest neighbors can be
Page 52 and 53: Chapter 2 Loading the dataset The d
Page 54 and 55: Chapter 2 Finally, we take the last
Page 56 and 57: Chapter 2 Next, we use this functio
Page 58 and 59: Chapter 2 While there is a lot of v
Page 60 and 61: This gives a score of 82.3 percent
Page 62 and 63: Chapter 2 Pipelines take a list of
Page 64 and 65: Predicting Sports Winners with Deci
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Using pandas to load the dataset Th
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Chapter 3 Now that we have our data
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Chapter 3 You can change those indi
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Chapter 3 Using decision trees We c
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Chapter 3 The output is as follows:
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Chapter 3 Finally, we update our di
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To compensate for this, we could cr
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Chapter 3 This results in an immedi
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Chapter 3 If you are facing trouble
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Recommending Movies Using Affinity
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Extracting Features with Transforme
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Social Media Insight Using Naive Ba
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Chapter 6 Downloading data from a s
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In the preceding loop, we also perf
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Chapter 6 Next, we create a simple
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Chapter 6 For this cell, we will be
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Chapter 6 On running the preceding
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Chapter 6 The code is as follows: a
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Chapter 6 Here's an excerpt from Th
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As an example, for n=3, we extract
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Chapter 6 From here, we use Bayes'
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Now, we can compute the probability
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Chapter 6 Let's take a look at the
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We can nearly run our pipeline now,
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Chapter 6 Note that we aren't reall
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Chapter 6 Summary In this chapter,
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Discovering Accounts to Follow Usin
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Beating CAPTCHAs with Neural Networ
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Authorship Attribution Authorship a
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Authorship studies alone cannot pro
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Getting the data The data we will u
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Chapter 9 We create lists for stori
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Chapter 9 The use of function words
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Classifying with function words Nex
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Chapter 9 The derivation of these e
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Chapter 9 Character n-grams are fou
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Chapter 9 Accessing the Enron datas
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Next, we iterate through each of th
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Chapter 9 This document contains an
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Chapter 9 Evaluation It is generall
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Chapter 9 We can see that authors a
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Clustering News Articles Our system
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Clustering News Articles Now let's
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Clustering News Articles The URL fo
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Clustering News Articles As the las
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Clustering News Articles If there i
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Clustering News Articles At this po
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Clustering News Articles The algori
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Clustering News Articles The labels
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Clustering News Articles After this
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Clustering News Articles You can th
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Clustering News Articles In graph t
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Clustering News Articles How it wor
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Clustering News Articles We then wr
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Clustering News Articles We can the
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Clustering News Articles Summary In
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Classifying Objects in Images Using
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Working with Big Data The amount of
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Chapter 12 In big data, we can't lo
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Chapter 12 MapReduce originates fro
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Chapter 12 The map function takes a
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The Hadoop ecosystem is quite compl
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Chapter 12 We set a test filename s
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Chapter 12 Extracting the blog post
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Chapter 12 The first parameter, /bl
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Chapter 12 The first function is th
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We again redefine our word search r
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Chapter 12 One problem with using l
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Chapter 12 for line in inf: tokens
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Chapter 12 python extract_posts.py
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Next Steps… During the course of
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Appendix To install it, clone the r
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Chapter 4 - Recommending Movies Usi
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Chapter 7 - Discovering Accounts to
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Local n-grams https://github.com/ro
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Appendix Other image datasets are a
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Index A access keys 107 accuracy im
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example 2 features 2 follower infor
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K Kaggle about 308 URL 308 Keras UR
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preprocessing, using pipelines abou
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U UCL Machine Learning data reposit
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Python Data Analysis ISBN: 978-1-78
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Learning Data Mining with Python

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