Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 1 However, the speed comes at a price. Using NumPy arrays, we no longer have the incredible flexibility of Python lists, which can hold basically anything. NumPy arrays always have only one datatype. >>> a = np.array([1,2,3]) >>> a.dtype dtype('int64') If we try to use elements of different types, NumPy will do its best to coerce them to the most reasonable common datatype: >>> np.array([1, "stringy"]) array(['1', 'stringy'], dtype='|S8') >>> np.array([1, "stringy", set([1,2,3])]) array([1, stringy, set([1, 2, 3])], dtype=object) Learning SciPy On top of the efficient data structures of NumPy, SciPy offers a magnitude of algorithms working on those arrays. Whatever numerical-heavy algorithm you take from current books on numerical recipes, you will most likely find support for them in SciPy in one way or another. Whether it is matrix manipulation, linear algebra, optimization, clustering, spatial operations, or even Fast Fourier transformation, the toolbox is readily filled. Therefore, it is a good habit to always inspect the scipy module before you start implementing a numerical algorithm. For convenience, the complete namespace of NumPy is also accessible via SciPy. So, from now on, we will use NumPy's machinery via the SciPy namespace. You can check this easily by comparing the function references of any base function; for example: >>> import scipy, numpy >>> scipy.version.full_version 0.11.0 >>> scipy.dot is numpy.dot True The diverse algorithms are grouped into the following toolboxes: SciPy package cluster Functionality Hierarchical clustering (cluster.hierarchy) Vector quantization / K-Means (cluster.vq) [ 17 ]
Getting Started with Python Machine Learning SciPy package constants fftpack integrate interpolate io linalg maxentropy ndimage odr optimize signal sparse spatial special stats Functionality Physical and mathematical constants Conversion methods Discrete Fourier transform algorithms Integration routines Interpolation (linear, cubic, and so on) Data input and output Linear algebra routines using the optimized BLAS and LAPACK libraries Functions for fitting maximum entropy models n-dimensional image package Orthogonal distance regression Optimization (finding minima and roots) Signal processing Sparse matrices Spatial data structures and algorithms Special mathematical functions such as Bessel or Jacobian Statistics toolkit The toolboxes most interesting to our endeavor are scipy.stats, scipy. interpolate, scipy.cluster, and scipy.signal. For the sake of brevity, we will briefly explore some features of the stats package and leave the others to be explained when they show up in the chapters. [ 18 ]
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 12 and 13: Table of Contents Tuning the instan
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 31: 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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Page 62 and 63: Chapter 2 Binary and multiclass cla
Page 64 and 65: Clustering - Finding Related Posts
Page 66 and 67: Chapter 3 How to do it More robust
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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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