Building Machine Learning Systems with Python - Richert, Coelho

More documents

Recommendations

Info

Downloading the example code You can download the example code files for all Packt books you have purchased from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you. Preface Errata Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our books—maybe a mistake in the text or the code—we would be grateful if you would report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this book. If you find any errata, please report them by visiting http://www.packtpub. com/submit-errata, selecting your book, clicking on the errata submission form link, and entering the details of your errata. Once your errata are verified, your submission will be accepted and the errata will be uploaded on our website, or added to any list of existing errata, under the Errata section of that title. Any existing errata can be viewed by selecting your title from http://www.packtpub.com/support. Piracy Piracy of copyright material on the Internet is an ongoing problem across all media. At Packt, we take the protection of our copyright and licenses very seriously. If you come across any illegal copies of our works, in any form, on the Internet, please provide us with the location address or website name immediately so that we can pursue a remedy. Please contact us at copyright@packtpub.com with a link to the suspected pirated material. We appreciate your help in protecting our authors, and our ability to bring you valuable content. Questions You can contact us at questions@packtpub.com if you are having a problem with any aspect of the book, and we will do our best to address it. [ 5 ]
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 22 and 23: Getting Started with Python Machine
Page 24 and 25: Chapter 1 What the book will teach
Page 26 and 27: In such a situation, there are many
Page 28 and 29: You will also find the book NumPy B
Page 30 and 31: Chapter 1 Of course, by using NumPy
Page 32 and 33: Chapter 1 However, the speed comes
Page 34 and 35: Chapter 1 Our first (tiny) machine
Page 36 and 37: Chapter 1 We are missing only 8 out
Page 38 and 39: Chapter 1 >>> print(res) [ 3.173897
Page 40 and 41: Chapter 1 The error is 179,983,507.
Page 42 and 43: Chapter 1 Clearly, the combination
Page 44 and 45: Chapter 1 Although we cannot look i
Page 46: Chapter 1 Summary Congratulations!
Page 49 and 50: Learning How to Classify with Real-
Page 65 and 66: Clustering - Finding Related Posts
Page 71 and 72:
Clustering - Finding Related Posts
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 90 and 91:
Topic Modeling In the previous chap
Page 92 and 93:
Chapter 4 Corpus is just the preloa
Page 94 and 95:
Chapter 4 Now we can see that many
Page 96 and 97:
Chapter 4 However, topics are often
Page 98 and 99:
Chapter 4 Subject: Re: High Prolact
Page 100 and 101:
We can also ask what the most talke
Page 102 and 103:
Chapter 4 If you are going to explo
Page 104 and 105:
Classification - Detecting Poor Ans
Page 106 and 107:
Chapter 5 Fetching the data Luckily
Page 108 and 109:
Preselection and processing of attr
Page 110 and 111:
Chapter 5 Creating our first classi
Page 112 and 113:
Chapter 5 With the majority of post
Page 114 and 115:
Chapter 5 # which we don't want to
Page 116 and 117:
With these four additional features
Page 118 and 119:
The only possibilities we have in t
Page 120 and 121:
Chapter 5 But this is not enough, a
Page 122 and 123:
Chapter 5 This means that we can no
Page 124 and 125:
Chapter 5 Method mean(scores) stdde
Page 126 and 127:
If instead our goal would have been
Page 128 and 129:
Chapter 5 Setting the threshold at
Page 130:
Chapter 5 Ship it! Let's assume we
Page 133 and 134:
Classification II - Sentiment Analy
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Regression - Recommendations We sta
Page 165 and 166:
Regression - Recommendations In the
Page 167 and 168:
Regression - Recommendations The Li
Page 169 and 170:
Regression - Recommendations Ridge,
Page 171 and 172:
Regression - Recommendations Howeve
Page 173 and 174:
Regression - Recommendations Settin
Page 175 and 176:
Regression - Recommendations Unfort
Page 177 and 178:
Regression - Recommendations reg.fi
Page 180 and 181:
Regression - Recommendations Improv
Page 182 and 183:
We are now going to use this binary
Page 184 and 185:
Chapter 8 Now, we iterate over all
Page 186 and 187:
coefficients = [] # We are now goin
Page 188 and 189:
Chapter 8 The beer and diapers stor
Page 190 and 191:
Chapter 8 Formally, Apriori takes a
Page 192 and 193:
Chapter 8 Refer to the following co
Page 194:
Chapter 8 Summary In this chapter,
Page 197 and 198:
Classification III - Music Genre Cl
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
Page 214 and 215:
Computer Vision - Pattern Recogniti
Page 216 and 217:
Chapter 10 However, some specialize
Page 218 and 219:
Chapter 10 Instead of rgb2gray, we
Page 220 and 221:
Chapter 10 This is still not perfec
Page 222 and 223:
Filtering for different effects The
Page 224 and 225:
Chapter 10 Now we filter the 3 chan
Page 226 and 227:
Chapter 10 We previously used an ex
Page 228 and 229:
Chapter 10 However, it is also poss
Page 230 and 231:
Feature sets may be combined easily
Page 232 and 233:
The descriptors_only=True flag mean
Page 234 and 235:
Chapter 10 The result is that each
Page 236 and 237:
Dimensionality Reduction Garbage in
Page 238 and 239:
Chapter 11 Detecting redundant feat
Page 240 and 241:
Chapter 11 Although the human eye i
Page 242 and 243:
Chapter 11 We can see that this sit
Page 244 and 245:
Chapter 11 Hence, we have to calcul
Page 246 and 247:
Chapter 11 Coming back to Scikit-le
Page 248 and 249:
Chapter 11 Feature extraction At so
Page 250 and 251:
Chapter 11 Scikit-learn provides th
Page 252 and 253:
Chapter 11 That's all. Note that in
Page 254 and 255:
Chapter 11 Let us have a look at a
Page 256 and 257:
Big(ger) Data While computers keep
Page 258 and 259:
Chapter 12 A task could be "call do
Page 260 and 261:
Chapter 12 Now we run jug status ag
Page 262 and 263:
Chapter 12 Jug is also specially op
Page 264 and 265:
Chapter 12 There are three modes of
Page 266 and 267:
We pick and click on EC2 (the secon
Page 268 and 269:
Chapter 12 Therefore, we will be ca
Page 270 and 271:
Chapter 12 You can assign a fixed I
Page 272 and 273:
Chapter 12 We need to create a new
Page 274:
Chapter 12 Summary We saw how to us
Page 277 and 278:
Where to Learn More about Machine L
Page 279 and 280:
Where to Learn More about Machine L
Page 281 and 282:
classification model building 35, 3
Page 283 and 284:
jug execute file 243 jugfile.jugdat
Page 285 and 286:
limitations 236 sketching 234 pears
Page 288 and 289:
Thank you for buying Building Machi
Page 290:
Instant Pygame for Python Game Deve
show all

Building Machine Learning Systems with Python - Richert, Coelho

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?