Building Machine Learning Systems with Python - Richert, Coelho

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Table of Contents Tuning the instance 90 Tuning the classifier 90 Fetching the data 91 Slimming the data down to chewable chunks 92 Preselection and processing of attributes 93 Defining what is a good answer 94 Creating our first classifier 95 Starting with the k-nearest neighbor (kNN) algorithm 95 Engineering the features 96 Training the classifier 97 Measuring the classifier's performance 97 Designing more features 98 Deciding how to improve 101 Bias-variance and its trade-off 102 Fixing high bias 102 Fixing high variance 103 High bias or low bias 103 Using logistic regression 105 A bit of math with a small example 106 Applying logistic regression to our postclassification problem 108 Looking behind accuracy – precision and recall 110 Slimming the classifier 114 Ship it! 115 Summary 115 Chapter 6: Classification II – Sentiment Analysis 117 Sketching our roadmap 117 Fetching the Twitter data 118 Introducing the Naive Bayes classifier 118 Getting to know the Bayes theorem 119 Being naive 120 Using Naive Bayes to classify 121 Accounting for unseen words and other oddities 124 Accounting for arithmetic underflows 125 Creating our first classifier and tuning it 127 Solving an easy problem first 128 Using all the classes 130 Tuning the classifier's parameters 132 Cleaning tweets 136 Taking the word types into account 138 Determining the word types 139 [ iii ]
Table of Contents Successfully cheating using SentiWordNet 141 Our first estimator 143 Putting everything together 145 Summary 146 Chapter 7: Regression – Recommendations 147 Predicting house prices with regression 147 Multidimensional regression 151 Cross-validation for regression 151 Penalized regression 153 L1 and L2 penalties 153 Using Lasso or Elastic nets in scikit-learn 154 P greater than N scenarios 155 An example based on text 156 Setting hyperparameters in a smart way 158 Rating prediction and recommendations 159 Summary 163 Chapter 8: Regression – Recommendations Improved 165 Improved recommendations 165 Using the binary matrix of recommendations 166 Looking at the movie neighbors 168 Combining multiple methods 169 Basket analysis 172 Obtaining useful predictions 173 Analyzing supermarket shopping baskets 173 Association rule mining 176 More advanced basket analysis 178 Summary 179 Chapter 9: Classification III – Music Genre Classification 181 Sketching our roadmap 181 Fetching the music data 182 Converting into a wave format 182 Looking at music 182 Decomposing music into sine wave components 184 Using FFT to build our first classifier 186 Increasing experimentation agility 186 Training the classifier 187 Using the confusion matrix to measure accuracy in multiclass problems 188 An alternate way to measure classifier performance using receiver operator characteristic (ROC) 190 [ iv ]
Page 2 and 3: Building Machine Learning Systems w
Page 4 and 5: Credits Authors Willi Richert Luis
Page 6 and 7: Luis Pedro Coelho is a Computationa
Page 8 and 9: Maurice HT Ling completed his PhD.
Page 10 and 11: Table of Contents Preface 1 Chapter
Page 14 and 15: Table of Contents Improving classif
Page 16 and 17: Preface You could argue that it is
Page 18 and 19: Preface What you need for this book
Page 20: Downloading the example code You ca
Page 23 and 24: Getting Started with Python Machine
Page 48 and 49: Learning How to Classify with Real-
Page 50 and 51: Chapter 2 We are using Matplotlib;
Page 52 and 53: Chapter 2 The last few lines select
Page 54 and 55: Chapter 2 error = 0.0 for ei in ran
Page 56 and 57: Chapter 2 We can play around with t
Page 58 and 59: Chapter 2 Features and feature engi
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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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Dimensionality Reduction Sketching
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Dimensionality Reduction However, t
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Dimensionality Reduction To underst
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Dimensionality Reduction In order t
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Dimensionality Reduction Asking the
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Dimensionality Reduction n_ feature
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Dimensionality Reduction Sketching
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Dimensionality Reduction Limitation
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Dimensionality Reduction Now, MDS t
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Dimensionality Reduction Of course,
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Big(ger) Data • Your algorithms c
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Big(ger) Data sleep(4) return 2*x @
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Big(ger) Data Looking under the hoo
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Big(ger) Data def write_result(ofna
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Big(ger) Data Amazon Web Services i
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Big(ger) Data In EC2 parlance, a ru
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Big(ger) Data In this system, pip i
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Big(ger) Data Keys, keys, and more
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Big(ger) Data We can use the same j
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Where to Learn More about Machine L
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• Machined Learnings at http://ww
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Index A AcceptedAnswerId attribute
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F false negative 41 false positive
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inary matrix of recommendations, us
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sklearn.naive_bayes package 127 skl
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NumPy Beginner's Guide - Second Edi
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Building Machine Learning Systems with Python - Richert, Coelho

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