Building Machine Learning Systems with Python - Richert, Coelho

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Index A AcceptedAnswerId attribute 93 additive smoothing 124 add-one smoothing 124 advanced baskets analysis 178 Amazon Linux Python packages, installing on 253 Amazon regions 249 Amazon Web Services. See AWS Apriori algorithm 174, 176 area under curve (AUC) 112, 191 as keypoint detection 216 Associated Press (AP) 76 association rule mining 176-178 association rules 176 attributes preselecting 93 processing 93 Auditory Filterbank Temporal Envelope (AFTE) 196 Automatic Music Genre Classification (AMGC) 193 AWS machine, creating 250-253 using 248, 24 B bag-of-word approach about 51, 217 challenges 51 bag-of-word approach, challenges about 51 less important words, removing 56, 57 raw text, converting into bag-of-words 52, 53 stemming 57 stop words, on steroids 60, 61 word count vectors, normalizing 56 words, counting 53-55 basic image processing about 201 filtering, for different effects 207 Gaussian blurring 205, 206 thresholding 202-205 basket analysis about 165, 172 association rule mining 176-178 supermarket shopping baskets, analyzing 173-175 useful predictions, obtaining 173 beer and diapers story 173 BernoulliNB 127 Bias-variance about 102 trade-off 102 big data expression 241, 242 binary classification 47, 48 binary matrix of recommendations using 166-168 blogs, machine language 262, 263 Body attribute 93 C classification about 33, 34 Naive Bayes, using for 121-123 poor answers, detecting 89
classification model building 35, 37 evaluating 38-40 loss function 40 search procedure 40 structure 40 classification performance improving, with Mel Frequency Cepstral Coefficients 193-196 classifier building, FFT used 186, 187 classes, using 130, 132 creating 95, 128-130 integrating, into site 115 parameters, tuning 132-136 performance, improving 101, 102 performance, measuring 97 slimming 114 training 97, 187 classifier, classy answers tuning 90 classifier performance measuring, receiver operator characteristic (ROC) used 190, 191 classifier performance, improving Bias-variance 102 high bias, fixing 102 high bias or low bias 103-105 high variance, fixing 103 classy answers classifier, tuning 90 classifying 90 instance, tuning 90 cloud machine jug, running on 254, 255 cluster generation automating, with starcluster 255-258 clustering about 50, 62 flat clustering 63 hierarchical clustering 63 KMeans algorithm 63-65 test data, obtaining for idea evaluation 65-67 cluster package 17 CommentCount attribute 93 complex classifiers building 40, 41 complex dataset 41 computer vision 199 confusion matrix used, for accuracy measurement in multiclass problems 188-190 constants package 18 correlation about 223 using 223-225 cost function 41 CountVectorizer 52 Coursera URL 261 CreationDate attribute 93 Cross Validated about 11, 262 URL 262 cross-validation 38, 39 cross-validation, for regression 151 cross-validation schedule 40 D data fetching 91, 92 slimming down, to chewable chunks 92 data analysis jug, using for 246-248 data, machine learning application cleaning 20, 21 preprocessing 20, 21 reading 19, 20 data sources, machine language 263 dimensionality reduction 222 dot() function 16 E Elastic net model 154 Elastic nets using, in scikit-Learn 154 ensemble learning 170 Enthought Python Distribution URL 12 [ 266 ]
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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 229 and 230: Computer Vision - Pattern Recogniti
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