Building Machine Learning Systems with Python - Richert, Coelho

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• Machined Learnings at http://www.machinedlearnings.com Appendix ° ° The average pace is one per month, providing more applied topics; often revolving around learning big data • FlowingData at http://flowingdata.com ° ° The average pace is one per day, with the posts revolving more around statistics • Normal deviate at http://normaldeviate.wordpress.com ° ° The average pace is one per month, covering theoretical discussions of practical problems. Although being more of a statistics blog, the posts often intersect with machine learning. • Simply statistics at http://simplystatistics.org ° ° There are several posts per month, focusing on statistics and big data • Statistical Modeling, Causal Inference, and Social Science at http:// andrewgelman.com ° ° There is one post per day with often funny reads when the author points out flaws in popular media using statistics Data sources If you want to play around with algorithms, you can obtain many datasets from the Machine Learning Repository at University of California at Irvine (UCI). You can find it at http://archive.ics.uci.edu/ml. Getting competitive An excellent way to learn more about machine learning is by trying out a competition! Kaggle (http://www.kaggle.com) is a marketplace of ML competitions and has already been mentioned in the introduction. On the website, you will find several different competitions with different structures and often cash prizes. The supervised learning competitions almost always follow the following format: • You (and every other competitor) are given access to labeled training data and testing data (without labels). • Your task is to submit predictions for the testing data. • When the competition closes, whoever has the best accuracy wins. The prizes range from glory to cash. [ 263 ]
Where to Learn More about Machine Learning Of course, winning something is nice, but you can gain a lot of useful experience just by participating. So, you have to stay tuned, especially after the competition is over and participants start sharing their approaches in the forum. Most of the time, winning is not about developing a new algorithm; it is about cleverly preprocessing, normalizing, and combining the existing methods. What was left out We did not cover every machine learning package available for Python. Given the limited space, we chose to focus on Scikit-learn. However, there are other options, and we list a few of them here: • Modular toolkit for Data Processing (MDP ) at http://mdp-toolkit. sourceforge.net • Pybrain at http://pybrain.org • Machine Learning Toolkit (MILK) at http://luispedro.org/software/ milk ° ° This package was developed by one of the authors of this book, and covers some algorithms and techniques that are not included in Scikit-learn. A more general resource is at http://mloss.org, which is a repository of open source machine learning software. As is usually the case with repositories such as this one, the quality varies between excellent, well-maintained software and projects that were one-offs and then abandoned. It may be worth checking out if your problem is very specific and none of the more general packages address it. Summary We are now truly at the end. We hope you have enjoyed the book and feel well equipped to start your own machine learning adventure. We also hope you have learned the importance of carefully testing your methods, in particular, of using correct cross-validation and not reporting training test results, which are an over-inflated estimate of how good your method really is. [ 264 ]
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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 280 and 281: Index A AcceptedAnswerId attribute
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Building Machine Learning Systems with Python - Richert, Coelho

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