Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 9 Summary In this chapter, we stepped out of our comfort zone when we built a music genre classifier. Not having a deep understanding of music theory, at first we failed to train a classifier that predicts the music genre of songs <strong>with</strong> reasonable accuracy using FFT. But then we created a classifier that showed really usable performance using MFC features. In both cases, we used features that we understood only so much as to know how and where to put them into our classifier setup. The one failed, the other succeeded. The difference between them is that in the second case, we relied on features that were created by experts in the field. And that is totally OK. If we are mainly interested in the result, we sometimes simply have to take shortcuts—we only have to make sure to take these shortcuts from experts in the specific domains. And because we had learned how to correctly measure the performance in this new multiclass classification problem, we took these shortcuts <strong>with</strong> confidence. In the next chapter, we will look at how to apply techniques you have learned in the rest of the book to this specific type of data. We will learn how to use the mahotas computer vision package to preprocess images using traditional image processing functions. [ 197 ]
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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
Page 162 and 163: Regression - Recommendations You ha
Page 164 and 165: Chapter 7 The preceding graph shows
Page 166 and 167: Chapter 7 Root mean squared error a
Page 168 and 169: Penalized regression The important
Page 170 and 171: Chapter 7 P greater than N scenario
Page 172 and 173: Chapter 7 So, we can see that the d
Page 174 and 175: Chapter 7 Fortunately, scikit-learn
Page 176 and 177: [ 161 ] Chapter 7 The loading of th
Page 178: Chapter 7 Summary In this chapter,
Page 181 and 182: Regression - Recommendations Improv
Page 185 and 186: Weights Regression - Recommendation
Page 196 and 197: Classification III - Music Genre Cl
Page 198 and 199: Chapter 9 Matplotlib provides the c
Page 200 and 201: [ 185 ] Chapter 9
Page 202 and 203: Chapter 9 def create_fft(fn): sampl
Page 204 and 205: Chapter 9 ax.set_yticks(range(len(g
Page 206 and 207: Chapter 9 On the left-hand side gra
Page 208 and 209: [ 193 ] Chapter 9 Improving classif
Page 210 and 211: We get the following promising resu
Page 214 and 215: Computer Vision - Pattern Recogniti
Page 216 and 217: Chapter 10 However, some specialize
Page 218 and 219: Chapter 10 Instead of rgb2gray, we
Page 220 and 221: Chapter 10 This is still not perfec
Page 222 and 223: Filtering for different effects The
Page 224 and 225: Chapter 10 Now we filter the 3 chan
Page 226 and 227: Chapter 10 We previously used an ex
Page 228 and 229: Chapter 10 However, it is also poss
Page 230 and 231: Feature sets may be combined easily
Page 232 and 233: The descriptors_only=True flag mean
Page 234 and 235: Chapter 10 The result is that each
Page 236 and 237: Dimensionality Reduction Garbage in
Page 238 and 239: Chapter 11 Detecting redundant feat
Page 240 and 241: Chapter 11 Although the human eye i
Page 242 and 243: Chapter 11 We can see that this sit
Page 244 and 245: Chapter 11 Hence, we have to calcul
Page 246 and 247: Chapter 11 Coming back to Scikit-le
Page 248 and 249: Chapter 11 Feature extraction At so
Page 250 and 251: Chapter 11 Scikit-learn provides th
Page 252 and 253: Chapter 11 That's all. Note that in
Page 254 and 255: Chapter 11 Let us have a look at a
Page 256 and 257: Big(ger) Data While computers keep
Page 258 and 259: Chapter 12 A task could be "call do
Page 260 and 261: Chapter 12 Now we run jug status ag
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Chapter 12 Jug is also specially op
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Chapter 12 There are three modes of
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We pick and click on EC2 (the secon
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Chapter 12 Therefore, we will be ca
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Chapter 12 You can assign a fixed I
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Chapter 12 We need to create a new
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Chapter 12 Summary We saw how to us
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Where to Learn More about Machine L
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Where to Learn More about Machine L
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classification model building 35, 3
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jug execute file 243 jugfile.jugdat
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limitations 236 sketching 234 pears
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Thank you for buying Building Machi
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Instant Pygame for Python Game Deve
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Building Machine Learning Systems with Python - Richert, Coelho

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