Building Machine Learning Systems with Python - Richert, Coelho

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Where to Learn More about Machine Learning We are at the end of our book and now take a moment to look at what else is out there that could be useful for our readers. There are many wonderful resources out there to learn more about machine learning (way too much to cover them all here). Our list can therefore represent only a small and very biased sampling of the resources we think are best at the time of writing. Online courses Andrew Ng is a Professor at Stanford who runs an online course in machine learning as a massive open online course (MOOC) at Coursera (http://www.coursera. org). It is free of charge, but may represent a significant investment of time and effort (return on investment guaranteed!). Books This book focused on the practical side of machine learning. We did not present the thinking behind the algorithms or the theory that justifies them. If you are interested in that aspect of machine learning, then we recommend Pattern Recognition and Machine Learning, C. Bishop , Springer Apply Italics to this. This is a classical introductory text in the field. It will teach you the nitty-gritties of most of the algorithms we used in this book.
Where to Learn More about Machine Learning If you want to move beyond an introduction and learn all the gory mathematical details, then Machine Learning: A Probabilistic Perspective, K. Murphy, The MIT Press, is an excellent option. It is very recent (published in 2012), and contains the cutting edge of ML research. This 1,100 page book can also serve as a reference, as very little of machine learning has been left out. Q&A sites The following are the two Q&A websites of machine learning: • MetaOptimize (http://metaoptimize.com/qa) is a machine learning Q&A website where many very knowledgeable researchers and practitioners interact • Cross Validated (http://stats.stackexchange.com) is a general statistics Q&A site, which often features machine learning questions as well As mentioned in the beginning of the book, if you have questions specific to particular parts of the book, feel free to ask them at TwoToReal (http://www.twotoreal.com). We try to be as quick as possible to jump in and help as best as we can. Blogs The following is an obviously non-exhaustive list of blogs that are interesting to someone working on machine learning: • Machine Learning Theory at http://hunch.net ° ° This is a blog by John Langford, the brain behind Vowpal Wabbit (http://hunch.net/~vw/), but guest posts also appear. ° ° The average pace is approximately one post per month. The posts are more theoretical. They also offer additional value in brain teasers. • Text and data mining by practical means at http://textanddatamining. blogspot.de ° ° The average pace is one per month, which is very practical and has always surprising approaches • A blog by Edwin Chen at http://blog.echen.me ° ° The average pace is one per month, providing more applied topics [ 262 ]
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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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