Building Machine Learning Systems with Python - Richert, Coelho

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inary matrix of recommendations, using 166, 168 movie neighbors, viewing 168, 169 multiple methods, combining 169-171 MP3 files converting, into wave format 182 multiclass classification 47, 48 multiclass problems confusion matrix, used for accuracy measurement 188-190 multidimensional regression 151 multidimensional scaling. See MDS MultinomialNB 127 music decomposing, into sine wave components 184, 186 music data fetching 182 Music Information Retrieval (MIR) 193 N Naive Bayes used, for classification 121-123 Naive Bayes classifier about 118-120 accounting, for arithmetic underflows 125-127 accounting, for oddities 124 accounting, for unseen words 124 Naive Bayes classifiers BernoulliNB 127 GaussianNB 127 MultinomialNB 127 Natural Language Toolkit. See NLTK ndimage (n-dimensional image) 200 ndimage package 18 nearest neighbor classification 44-46 nearest neighbor search (NNS) 9 Netflix 159 NLTK installing 58 using 58 NLTK's stemmer used, for extending vectorizer 59 norm() function 54 np.linalg.lstsq function 150 NumPy about 200 indexing 15 learning 13-15 non-existing values, handling 15 runtime behaviors, comparing 16, 17 URL, for tutorials 12 O odr package 18 OpenCV 200 opinion mining 117 optimize package 18 Oracle Grid Engine (OGE) 242 ordinary least squares (OLS) regression 147 Otsu threshold 202, 205 overfitting 25 OwnerUserId attribute 93 P packages, SciPy cluster 17 constants 18 fftpack 18 integrate 18 interpolate 18 io 18 linalg 18 maxentropy 18 ndimage 18 odr 18 optimize 18 signal 18 sparse 18 spatial 18 special 18 stats 18 parameters tweaking 72 partial results reusing 245 Part Of Speech (POS) 117, 139 pattern recognition 210, 211 PCA about 222, 233 applying 234, 235 [ 269 ]
limitations 236 sketching 234 pearsonr() function 223 penalized regression 153 Elastic net 154 L1 penalty 153 L2 penalty 153 Lasso model 154 Penn Treebank Project URL 139 P greater than N scenarios about 155 hyperparameters, setting 158 prediction, rating 159-162 recommendations, rating 159-162 text example 156, 157 PNG 200 polyfit() function 22 Portable Batch System (PBS) 242 postclassification problem logistic regression, applying to 108, 109 posts clustering 67 relatedness, measuring 50, 51 PostType attribute 93 principal component analysis. See PCA Pybrain URL 264 pymining 178 pyplot package 21 <strong>Python</strong> about 200 installing 12 <strong>Python</strong> packages installing, on Amazon Linux 253 Q Q&A sites about 11, 262 Cross Validated 262 MetaOptimize 262 R read_fft() function 187 receiver operator characteristic (ROC) about 190 used, for measuring classifier performance 190, 191 redundant features detecting, filters used 223 redundant features detection correlation, using 223-225 mutual information 225-229 regression used, for predicting house prices 147-150 Ridge regression 154 Ridley-Calvard method 205 root mean squared error (RMSE) 150, 151 S salt and pepper noise adding 207, 208 center, inserting in focus 208-210 save() function 187 Scale-Invariant Feature Transform. See SIFT Scikit 63 scikit-image (Skimage) 200 scikit-Learn Elastic nets, using in 154 Lasso, using in 154 SciPy about 12, 17, 200 packages 18 URL 12 Score attribute 93 Secure Shell (SSH) 252 Securities and Exchange Commission (SEC) 156 Seeds dataset 42 sentiment analysis, tweet 117 SentiWordNet about 141 URL 141 used, for cheating 141 SIFT 200, 216 signal package 18 similarity comparing, in topic space 80-82 sine wave components music, decomposing into 184-186 sklearn.feature_selection package 231 sklearn.lda 75 [ 270 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Learning How to Classify with Real-
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Chapter 2 We are using Matplotlib;
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Chapter 2 The last few lines select
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Chapter 2 error = 0.0 for ei in ran
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Chapter 2 We can play around with t
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Chapter 2 Features and feature engi
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Chapter 2 In the preceding screensh
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Chapter 2 Binary and multiclass cla
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Clustering - Finding Related Posts
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Chapter 3 How to do it More robust
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Chapter 3 This means that the first
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Chapter 3 ... post = posts[i] ... i
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Chapter 3 If you have a clear pictu
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Chapter 3 Extending the vectorizer
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Chapter 3 0.0 >>> print(tfidf("b",
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Chapter 3 Flat clustering divides t
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Because the cluster centers are mov
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Chapter 3 'D:\\data\\379\\raw\\comp
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As we have learned previously, we w
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Chapter 3 Position Similarity Excer
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Chapter 3 But before you go there,
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Topic Modeling For those who are in
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Topic Modeling Sparsity means that
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Topic Modeling Although daunting at
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Topic Modeling … for tj,v in t:
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Topic Modeling Finally, we build th
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Topic Modeling Alternatively, we ca
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Topic Modeling Topic modeling was f
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Classification - Detecting Poor Ans
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Classification II - Sentiment Analy
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Chapter 6 Getting to know the Bayes
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Using Naive Bayes to classify Given
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Chapter 6 This denotation "" leads
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Chapter 6 Similarly, we do this for
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Chapter 6 A quick look at the previ
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Chapter 6 To keep our experimentati
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Chapter 6 Y = np.zeros(Y.shape[0])
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Chapter 6 ° ° Experiment with whe
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Chapter 6 We have to be patient whe
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Chapter 6 First, we define a range
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Chapter 6 Determining the word type
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Chapter 6 Successfully cheating usi
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Chapter 6 Our first estimator Now w
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Chapter 6 for d in documents: allca
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Regression - Recommendations You ha
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Chapter 7 The preceding graph shows
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Chapter 7 Root mean squared error a
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Penalized regression The important
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Chapter 7 P greater than N scenario
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Chapter 7 So, we can see that the d
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Chapter 7 Fortunately, scikit-learn
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[ 161 ] Chapter 7 The loading of th
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Chapter 7 Summary In this chapter,
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Regression - Recommendations Improv
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Weights Regression - Recommendation
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Classification III - Music Genre Cl
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Chapter 9 Matplotlib provides the c
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[ 185 ] Chapter 9
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Chapter 9 def create_fft(fn): sampl
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Chapter 9 ax.set_yticks(range(len(g
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Chapter 9 On the left-hand side gra
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[ 193 ] Chapter 9 Improving classif
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We get the following promising resu
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Chapter 9 Summary In this chapter,
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Computer Vision - Pattern Recogniti
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Page 233 and 234: Computer Vision - Pattern Recogniti
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Page 280 and 281: Index A AcceptedAnswerId attribute
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