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Page 3 and 4: Learning Data Mining with Python Co
Page 5 and 6: About the Author Robert Layton has
Page 7: Christophe Van Gysel is pursuing a
Page 11 and 12: Table of Contents Preprocessing usi
Page 13 and 14: Table of Contents Chapter 7: Discov
Page 15 and 16: Table of Contents GPU optimization
Page 18 and 19: Preface If you have ever wanted to
Page 20 and 21: What you need for this book It shou
Page 22 and 23: Preface Reader feedback Feedback fr
Page 24 and 25: Getting Started with Data Mining We
Page 26 and 27: Chapter 1 In the preceding dataset,
Page 28 and 29: After you have the above "Hello, wo
Page 30 and 31: Chapter 1 Windows users may need to
Page 32 and 33: Chapter 1 The dataset we are going
Page 34 and 35: Chapter 1 As an example, we will co
Page 36 and 37: We get the names of the features fo
Page 38 and 39: Chapter 1 Two rules are near the to
Page 40 and 41: Chapter 1 The scikit-learn library
Page 42 and 43: We then iterate over all the sample
Page 44 and 45: Chapter 1 Overfitting is the proble
Page 46: Chapter 1 Summary In this chapter,
Page 49 and 50: Classifying with scikit-learn Estim
Page 59 and 60:
Classifying with scikit-learn Estim
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Predicting Sports Winners with Deci
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Recommending Movies Using Affinity
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Chapter 4 The classic algorithm for
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Chapter 4 When loading the file, we
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Chapter 4 We will sample our datase
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Chapter 4 Implementation On the fir
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Chapter 4 We want to break out the
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The process starts by creating dict
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movie_name_data.columns = ["MovieID
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To do this, we will compute the tes
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Chapter 4 - Train Confidence: 1.000
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Extracting Features with Transforme
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Chapter 5 Thought should always be
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Chapter 5 Other features describe a
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Chapter 5 Similarly, we can convert
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Chapter 5 [18, 19, 20], [21, 22, 23
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Chapter 5 Next, we create our trans
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Chapter 5 This returns a different
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Also, we want to set the final colu
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Chapter 5 The downside to transform
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Chapter 5 A transformer is akin to
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We can then create an instance of t
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Chapter 5 Putting it all together N
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Social Media Insight Using Naive Ba
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Discovering Accounts to Follow Usin
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Chapter 7 Next, we will need a list
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Chapter 7 Make sure the filename is
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Chapter 7 cursor = results['next_cu
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Chapter 7 Next, we are going to rem
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Chapter 7 Creating a graph Now, we
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Chapter 7 As you can see, it is ver
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Chapter 7 Next, we will only add th
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Chapter 7 The difference in this gr
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Chapter 7 We can graph the entire s
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Chapter 7 Optimizing criteria Our a
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Chapter 7 Next, we need to get the
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• method='nelder-mead': This is u
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Beating CAPTCHAs with Neural Networ
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Chapter 8 The red lines indicate th
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Chapter 8 The combination of an app
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Chapter 8 Next we set the font of t
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Chapter 8 We can then extract the s
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Chapter 8 Our targets are integer v
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Chapter 8 Then we iterate over our
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Chapter 8 From these predictions, w
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Chapter 8 This code correctly predi
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The result is shown in the next gra
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Chapter 8 However, it isn't very go
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Chapter 8 Summary In this chapter,
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Authorship Attribution Attributing
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Authorship Attribution If we cannot
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Authorship Attribution After taking
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Authorship Attribution This dataset
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Authorship Attribution "instead", "
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Authorship Attribution Support vect
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Authorship Attribution Kernels When
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Authorship Attribution We can reuse
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Authorship Attribution With our dat
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Authorship Attribution We then reco
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Authorship Attribution If it doesn'
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Authorship Attribution Finally, we
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Clustering News Articles In most of
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Chapter 10 API Endpoints are the ac
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The token object is just a dictiona
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Chapter 10 We then create a list to
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Chapter 10 We are going to use MD5
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Chapter 10 Next, we develop the cod
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Chapter 10 We use clustering techni
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Chapter 10 The k-means algorithm is
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Chapter 10 We only fit the X matrix
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Chapter 10 We then print out the mo
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Chapter 10 Our function definition
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Chapter 10 The result from the prec
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Chapter 10 Implementation Putting a
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Chapter 10 Neural networks can also
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We then call the partial_fit functi
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Classifying Objects in Images Using
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Chapter 11 This dataset comes from
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You can change the image index to s
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Chapter 11 Each of these issues has
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Chapter 11 Using Theano, we can def
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Chapter 11 Building a neural networ
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Chapter 11 Finally, we create Thean
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Chapter 11 return [image,] return s
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Chapter 11 Next, we define how the
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Chapter 11 Getting your code to run
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Chapter 11 Setting up the environme
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This will unzip only one Coval.otf
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Chapter 11 First we create the laye
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Chapter 11 Finally, we set the verb
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Chapter 11 Summary In this chapter,
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Working with Big Data Big data What
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Working with Big Data Governments a
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Working with Big Data We start by c
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Working with Big Data The final ste
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Working with Big Data Getting the d
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Working with Big Data If we aren't
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Working with Big Data Before we sta
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Working with Big Data The first val
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Working with Big Data This gives us
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Working with Big Data Next, we crea
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Working with Big Data Then, make a
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Working with Big Data Left-click th
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Working with Big Data The result is
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Next Steps… Extending the IPython
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Next Steps… Chapter 3: Predicting
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Next Steps… Vowpal Wabbit http://
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Next Steps… Deeper networks These
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Next Steps… Real-time clusterings
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Next Steps… More resources Kaggle
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authorship, attributing 185-188 AWS
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feature extraction about 82 common
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NetworkX about 145 defining 303 URL
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scikit-learn package references 305
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Thank you for buying Learning Data
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Learning Python Data Visualization
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