Building Machine Learning Systems with Python - Richert, Coelho

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F false negative 41 false positive 41 Fast Fourier Transform. See FFT Fast Fourier transformation 17 feature engineering 9, 43 feature extraction about 233 LDA 236, 237 PCA 233 features about 34, 43 computing, from images 211, 212 designing 98-101 engineering 96 selecting 222 writing 212-214 feature selection 43 feature selection methods 232 FFT used, for building classifier 186, 187 fftpack package 18 filtering for different effects 207 filters disadvantage 229 used, for detecting features 223 fit_transform() method 237 flat clustering 63 G Gaussian blurring 205, 206 GaussianNB 127 genfromtxt() function 20 gensim package 76 good answers defining 94 graphical processing units (GPUs) 249 GTZAN dataset about 182 URL, for downloading 182 H Haralick texture features 211 harder dataset classifying 215, 216 hierarchical clustering 63 hierarchical Dirichlet process (HDP) 87 house prices predicting, with regression 147-150 hyperparameters setting 158 I image analysis 199 image processing 199, 200 images displaying 200, 201 features, computing from 211, 212 loading 200, 201 indexing, NumPy 15 installation, Python 12 installation, Python packages on Amazon Linux 253 instance 252 instance, classy answers tuning 90 integrate package 18 interest point detection 216 International Society forMusic Information Retrieval (ISMIR) 193 interpolate package 18 io package 18 Iris dataset about 33, 34 visualization 34, 35 J JPEG 200 jug about 242 partial results, reusing 245 running, on cloud machine 254, 255 URL, for documentation 248 used for breaking up pipeline, into tasks 242 using, for data analysis 246-248 working 246 jug cleanup 248 [ 267 ]
jug execute file 243 jugfile.jugdata directory 243 jugfile.py file 243 jug invalidate 248 jug status --cache 248 K Kaggle URL 263 keys 256 KMeans 63-65 k-means clustering 218 k-nearest neighbor (kNN) algorithm 95 L labels 90 Laplace smoothing 124 Lasso about 154 using, in scikit-Learn 154 Latent Dirichlet allocation (LDA) 75 learning algorithm selecting 22-30 Levenshtein distance 50 Lidstone smoothing 124 lift 176 linalg package 18 linear discriminant analysis (LDA) 222, 236 LinearRegression class 152 Load Sharing Facility (LSF) 242 local feature representations 216-219 logistic regression applying, to postclassification problem 108, 109 example 106, 107 using 105 logistic regression classifier 187 loss function 40 M machine learning application about 19 data, cleaning 20, 21 data, preprocessing 20, 21 data, reading 19, 20 learning algorithm, selecting 22-30 machine learning (ML) about 7 additional resources 264 blogs 262, 263 books 261 data sources 263 goals 8 in real world 160 online courses 261 Q&A sites 262 supervised learning competitions 263, 264 Machine Learning Repository 263 Machine Learning Toolkit (MILK) URL 264 machines creating 250-253 Mahotas 201, 202 mahotas computer vision package 199, 200 mahotas.features 211 massive open online course (MOOC) 261 Matplotlib about 12, 35, 183 URL 12 matshow() function 188 maxentropy package 18 MDS 222-240 Mel Frequency Cepstral Coefficients used, for improving classification performance 193-196 Mel Frequency Cepstral Coefficients (MFCC) 193 Mel Frequency Cepstrum (MFC) 193 MetaOptimize about 262 URL 262 MetaOptimized 11 mfcc() function 193 mh.features.haralick function 211 MLComp URL 66 Modular toolkit for Data Processing (MDP) URL 264 movie recommendation dataset about 165 [ 268 ]
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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 231 and 232: Computer Vision - Pattern Recogniti
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Page 280 and 281: Index A AcceptedAnswerId attribute
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Building Machine Learning Systems with Python - Richert, Coelho

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