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Index A access keys 107 accuracy improving, dictionary used 180 activation function 162 Adult dataset URL 83 Advertisements dataset URL 93 affinity analysis about 61, 62 algorithms 62, 63 dataset, loading with NumPy 8-10 defining 7 example 7 parameters, selecting 63 product recommendations 8 ranking of rules, implementing 10-13 ranking, to find best rules 13-15 Amazon S3 console URL 293 API endpoint URL 213 application about 263, 279 data, obtaining 263-282 defining 126 dictionaries, converting to matrix 127 Naive Bayes algorithm 282 Naive Bayes classifier, training 127 neural network, creating 266 neural network, training with training dataset 267, 268 word counts, extracting 126 apps, Twitter account URL 107 Apriori algorithm 68 Apriori implementation about 66, 67 Apriori algorithm 68 defining 69-71 arbitrary websites data mining, using 220 HTML file, parsing 221, 222 nodes, ignoring 221 stories, finding 218-220 text, extracting from 218-221 Artificial Neural Networks 162, 163 association rules evaluating 76-79 extracting 72-76 authorship analysis about 187 applications 186, 187 authorship, attributing 187, 188 data, obtaining 189-191 defining 186 use cases 186, 187 authorship analysis, problems authorship clustering 186 authorship profiling 186 authorship verification 186 [ 309 ]
authorship, attributing 185-188 AWS CLI installing 294 AWS console URL 260 B back propagation (backprop) algorithm 173, 174 bagging 55 BatchIterator instance creating 265 Bayes' theorem about 122, 123 equation 122 bias 55 big data about 272 use cases 273, 274 Bleeding Edge code installing 299 URL 299 blog posts extracting 283, 284 blogs dataset 304 C CAPTCHA creating 167 defining 303 references 303 CART (Classification and Regression Trees) 48 character n-grams about 198, 199 extracting 199, 200 CIFAR-10 about 243 URL 243 classification about 16 algorithm, testing 20-22 dataset, loading 16, 17 dataset, preparing 16, 17 examples 16 OneR algorithm, implementing 18, 19 classifiers comparing 299 closed problem 187 cluster evaluation URL 226 clustering 222 coassociation matrix defining 230, 231 complex algorithms references 303 complex features references 300 confidence about 11 computing 11 connected components 151-153 Cosine distance 28 Coursera about 308 references 308 CPU defining 258 cross-fold validation framework defining 32 CSV (Comma Separated Values) 42 D data, blogging URL 280 data, Corpus URL 280 data mining defining 2, 3 dataset about 2 CAPTCHAs, drawing 166, 167 classifying, with existing model 137-139 cleaning up 44 creating 165 data, collecting 42 [ 310 ]
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[ 1 ] www.allitebooks.com
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Learning Data Mining with Python Co
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About the Author Robert Layton has
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Christophe Van Gysel is pursuing a
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www.allitebooks.com
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Table of Contents Preprocessing usi
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Table of Contents Chapter 7: Discov
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Table of Contents GPU optimization
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Preface If you have ever wanted to
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What you need for this book It shou
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Preface Reader feedback Feedback fr
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Getting Started with Data Mining We
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Chapter 1 In the preceding dataset,
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After you have the above "Hello, wo
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Chapter 1 Windows users may need to
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Chapter 1 The dataset we are going
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Chapter 1 As an example, we will co
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We get the names of the features fo
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Chapter 1 Two rules are near the to
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Chapter 1 The scikit-learn library
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We then iterate over all the sample
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Chapter 1 Overfitting is the proble
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Chapter 1 Summary In this chapter,
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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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Page 321 and 322: Next Steps… Extending the IPython
Page 323 and 324: Next Steps… Chapter 3: Predicting
Page 325 and 326: Next Steps… Vowpal Wabbit http://
Page 327 and 328: Next Steps… Deeper networks These
Page 329 and 330: Next Steps… Real-time clusterings
Page 331: Next Steps… More resources Kaggle
Page 335 and 336: feature extraction about 82 common
Page 337 and 338: NetworkX about 145 defining 303 URL
Page 339 and 340: scikit-learn package references 305
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Page 344: Learning Python Data Visualization
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