Learning Data Mining with Python

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K Kaggle about 308 URL 308 Keras URL 306 kernel 111 kernel parameter 198 k-means algorithm about 223-226 assignment phase 223 updating phase 223 L Lasagne about 249-253 URL 250 Levenshtein edit distance about 180 computing 180 Locality-Sensitive Hashing (LSH) 298 local n-grams about 305 references 305 local optima 174 log probabilities using 291 M machine-learning workflow testing 20 training 20 Mahotas about 306 references 306 Manhattan distance 27 MapReduce about 274 defining 275, 276 Hadoop MapReduce 279 WordCount example 276-278 market basket analysis 61 matplotlib URL 26 MD5 algorithm using 219 metadata 106 Minimum Spanning Tree (MST) about 232 computing 232 movie recommendation problem about 64 dataset, obtaining 64 loading, with pandas 64 sparse data formats 65 mrjob package about 282 URL 294 multiple SVMs creating 197 N Naive Bayes about 121 algorithm 123, 124 Bayes' theorem 122, 123 working 124, 125 Naive Bayes algorithm Amazon's EMR infrastructure, training 293-295 blog posts, extracting 283, 284 classifier, running 288-293 mrjob package 282 Naive Bayes model, training 285-287 NaN (Not a Number) 94 National Basketball Association (NBA) about 41 URL 42 Natural Language ToolKit (NLTK) 120 nearest neighbor algorithm URL 298 network building 142-144 defining 304 [ 313 ]
NetworkX about 145 defining 303 URL 150, 303 neural network about 26, 162, 246, 247 back propagation (backprop) algorithm 173, 174 classifying 172, 173 defining 247 implementing 247, 248 implementing, with nolearn 254-257 Lasagne, defining 249-253 Theano, defining 248, 249 training 172, 173, 246 URL 308 words, predicting 175-179 neural network layers, Lasagne dropout layers 250 network-in-network layers 250 noise layers 250 neurons 162 news articles clustering algorithms, using as transformers 229, 230 data, obtaining 216-218 grouping 222, 223 k-means algorithm 223-226 obtaining 211, 212 reddit, as data source 215, 216 results, evaluating 226-228 topic information, extracting from clusters 228, 229 web API used, for obtaining data 212-215 n-grams about 120, 198 advantages 121 disadvantages 121 NLTK references 302 NLTK installation instructions URL 126 noise adding 301 nolearn package neural networks, implementing with 254-257 nonprogrammers, for Python language URL 4 O object classification 242 OneR 18 one-versus-all classifier creating 197 online learning about 236 defining 236 implementing 237-239 ordinal 85 output layer 164 overfitting 21 P pagination 140 pandas references 300 URL 42, 300 pandas documentation URL 59 pandas.read_csv function 44 parameters, ensemble process n_estimators 56 n_jobs 56 oob_score 56 petal length 16 petal width 16 pip 5, 145 pipeline about 38, 39 BernoulliNB classifier 128 creating 103, 126 DictVectorizer transformer 128 NLTKBOW transformer 128 URL 299 precision 129 [ 314 ]
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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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Chapter 11 Getting your code to run
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Chapter 11 Setting up the environme
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Page 295 and 296: Working with Big Data Big data What
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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 and 332: Next Steps… More resources Kaggle
Page 333 and 334: authorship, attributing 185-188 AWS
Page 335: feature extraction about 82 common
Page 339 and 340: scikit-learn package references 305
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Page 344: Learning Python Data Visualization
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Learning Data Mining with Python

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