Building Machine Learning Systems with Python - Richert, Coelho

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sklearn.naive_bayes package 127 sklearn package 52 sobel filtering 213 sparse package 18 sparsity 78 spatial package 18 specgram() function 183 special package 18 spectrogram 182, 183 Speeded Up Robust Features. See SURF starcluster cluster generation, automating <strong>with</strong> 255-258 Starcluster URL, for documentation 258 stats package 18 stemming about 57 NLTK, installing 58 NLTK, using 58 supermarket shopping baskets analyzing 173-175 supervised learning 33, 34 support vector machines (SVM) 9 SURF 216 system demonstrating, for new post 68-72 T Talkbox SciKit 193 task about 242 example 243-245 term frequency - inverse document frequency (TF-IDF) 60 testing error 38 text preprocessing phase achievements 61 goals 61 thresholding 202-205 Title attribute 93 topic model about 75, 81 building 76-80 topic modeling 75, 86 topics about 76 selecting 86, 87 topic space similarity, comparing 80-82 training error 38 transform method 54 tweets cleaning 136-138 Twitter data fetching 118 TwoToReal about 262 URL 262 U University of California at Irvine (UCI) 42 V vectorization 51 vectorizer extending, <strong>with</strong> NLTK's stemmer 59 ViewCount attribute 93 visualization, Iris dataset 34, 35 W wave format MP3 files, converting into 182 Wikipedia modeling 83-86 URL, for dumps 83 word count vectors normalizing 56 wordle URL 80 word sense disambiguation 142 word types about 138 determining 139, 140 wrappers using 230, 231 [ 271 ]
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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 235 and 236: Computer Vision - Pattern Recogniti
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Page 247 and 248: Dimensionality Reduction n_ feature
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Page 257 and 258: Big(ger) Data • Your algorithms c
Page 259 and 260: Big(ger) Data sleep(4) return 2*x @
Page 261 and 262: Big(ger) Data Looking under the hoo
Page 263 and 264: Big(ger) Data def write_result(ofna
Page 265 and 266: Big(ger) Data Amazon Web Services i
Page 267 and 268: Big(ger) Data In EC2 parlance, a ru
Page 269 and 270: Big(ger) Data In this system, pip i
Page 271 and 272: Big(ger) Data Keys, keys, and more
Page 273 and 274: Big(ger) Data We can use the same j
Page 276 and 277: Where to Learn More about Machine L
Page 278 and 279: • Machined Learnings at http://ww
Page 280 and 281: Index A AcceptedAnswerId attribute
Page 282 and 283: F false negative 41 false positive
Page 284 and 285: inary matrix of recommendations, us
Page 288 and 289: Thank you for buying Building Machi
Page 290: Instant Pygame for Python Game Deve
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