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Python machine learning cookbook 2nd edition by Prateek Joshi ISBN 1786467682 9781786467683

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ISBN 10: 1786467682
ISBN 13: 9781786467683
Author: Prateek Joshi

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.

With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

Python machine learning cookbook 2nd Table of contents:

  1. Preface
  2. What this book covers
  3. What you need for this book
  4. Who this book is for
  5. Sections
  6. Getting ready
  7. How to do it…
  8. How it works…
  9. There’s more…
  10. See also
  11. Conventions
  12. Reader feedback
  13. Customer support
  14. Downloading the example code
  15. Downloading the color images of this book
  16. Errata
  17. Piracy
  18. Questions
  19. 1. The Realm of Supervised Learning
  20. Introduction
  21. Preprocessing data using different techniques
  22. Getting ready
  23. How to do it…
  24. Mean removal
  25. Scaling
  26. Normalization
  27. Binarization
  28. One Hot Encoding
  29. Label encoding
  30. How to do it…
  31. Building a linear regressor
  32. Getting ready
  33. How to do it…
  34. Computing regression accuracy
  35. Getting ready
  36. How to do it…
  37. Achieving model persistence
  38. How to do it…
  39. Building a ridge regressor
  40. Getting ready
  41. How to do it…
  42. Building a polynomial regressor
  43. Getting ready
  44. How to do it…
  45. Estimating housing prices
  46. Getting ready
  47. How to do it…
  48. Computing the relative importance of features
  49. How to do it…
  50. Estimating bicycle demand distribution
  51. Getting ready
  52. How to do it…
  53. There’s more…
  54. 2. Constructing a Classifier
  55. Introduction
  56. Building a simple classifier
  57. How to do it…
  58. There’s more…
  59. Building a logistic regression classifier
  60. How to do it…
  61. Building a Naive Bayes classifier
  62. How to do it…
  63. Splitting the dataset for training and testing
  64. How to do it…
  65. Evaluating the accuracy using cross-validation
  66. Getting ready…
  67. How to do it…
  68. Visualizing the confusion matrix
  69. How to do it…
  70. Extracting the performance report
  71. How to do it…
  72. Evaluating cars based on their characteristics
  73. Getting ready
  74. How to do it…
  75. Extracting validation curves
  76. How to do it…
  77. Extracting learning curves
  78. How to do it…
  79. Estimating the income bracket
  80. How to do it…
  81. 3. Predictive Modeling
  82. Introduction
  83. Building a linear classifier using Support Vector Machine (SVMs)
  84. Getting ready
  85. How to do it…
  86. Building a nonlinear classifier using SVMs
  87. How to do it…
  88. Tackling class imbalance
  89. How to do it…
  90. Extracting confidence measurements
  91. How to do it…
  92. Finding optimal hyperparameters
  93. How to do it…
  94. Building an event predictor
  95. Getting ready
  96. How to do it…
  97. Estimating traffic
  98. Getting ready
  99. How to do it…
  100. 4. Clustering with Unsupervised Learning
  101. Introduction
  102. Clustering data using the k-means algorithm
  103. How to do it…
  104. Compressing an image using vector quantization
  105. How to do it…
  106. Building a Mean Shift clustering model
  107. How to do it…
  108. Grouping data using agglomerative clustering
  109. How to do it…
  110. Evaluating the performance of clustering algorithms
  111. How to do it…
  112. Automatically estimating the number of clusters using DBSCAN algorithm
  113. How to do it…
  114. Finding patterns in stock market data
  115. How to do it…
  116. Building a customer segmentation model
  117. How to do it…
  118. 5. Building Recommendation Engines
  119. Introduction
  120. Building function compositions for data processing
  121. How to do it…
  122. Building machine learning pipelines
  123. How to do it…
  124. How it works…
  125. Finding the nearest neighbors
  126. How to do it…
  127. Constructing a k-nearest neighbors classifier
  128. How to do it…
  129. How it works…
  130. Constructing a k-nearest neighbors regressor
  131. How to do it…
  132. How it works…
  133. Computing the Euclidean distance score
  134. How to do it…
  135. Computing the Pearson correlation score
  136. How to do it…
  137. Finding similar users in the dataset
  138. How to do it…
  139. Generating movie recommendations
  140. How to do it…
  141. 6. Analyzing Text Data
  142. Introduction
  143. Preprocessing data using tokenization
  144. How to do it…
  145. Stemming text data
  146. How to do it…
  147. How it works…
  148. Converting text to its base form using lemmatization
  149. How to do it…
  150. Dividing text using chunking
  151. How to do it…
  152. Building a bag-of-words model
  153. How to do it…
  154. How it works…
  155. Building a text classifier
  156. How to do it…
  157. How it works…
  158. Identifying the gender
  159. How to do it…
  160. Analyzing the sentiment of a sentence
  161. How to do it…
  162. How it works…
  163. Identifying patterns in text using topic modeling
  164. How to do it…
  165. How it works…
  166. 7. Speech Recognition
  167. Introduction
  168. Reading and plotting audio data
  169. How to do it…
  170. Transforming audio signals into the frequency domain
  171. How to do it…
  172. Generating audio signals with custom parameters
  173. How to do it…
  174. Synthesizing music
  175. How to do it…
  176. Extracting frequency domain features
  177. How to do it…
  178. Building Hidden Markov Models
  179. How to do it…
  180. Building a speech recognizer
  181. How to do it…
  182. 8. Dissecting Time Series and Sequential Data
  183. Introduction
  184. Transforming data into the time series format
  185. How to do it…
  186. Slicing time series data
  187. How to do it…
  188. Operating on time series data
  189. How to do it…
  190. Extracting statistics from time series data
  191. How to do it…
  192. Building Hidden Markov Models for sequential data
  193. Getting ready
  194. How to do it…
  195. Building Conditional Random Fields for sequential text data
  196. Getting ready
  197. How to do it…
  198. Analyzing stock market data using Hidden Markov Models
  199. How to do it…
  200. 9. Image Content Analysis
  201. Introduction
  202. Operating on images using OpenCV-Python
  203. How to do it…
  204. Detecting edges
  205. How to do it…
  206. Histogram equalization
  207. How to do it…
  208. Detecting corners
  209. How to do it…
  210. Detecting SIFT feature points
  211. How to do it…
  212. Building a Star feature detector
  213. How to do it…
  214. Creating features using visual codebook and vector quantization
  215. How to do it…
  216. Training an image classifier using Extremely Random Forests
  217. How to do it…
  218. Building an object recognizer
  219. How to do it…
  220. 10. Biometric Face Recognition
  221. Introduction
  222. Capturing and processing video from a webcam
  223. How to do it…
  224. Building a face detector using Haar cascades
  225. How to do it…
  226. Building eye and nose detectors
  227. How to do it…
  228. Performing Principal Components Analysis
  229. How to do it…
  230. Performing Kernel Principal Components Analysis
  231. How to do it…
  232. Performing blind source separation
  233. How to do it…
  234. Building a face recognizer using Local Binary Patterns Histogram
  235. How to do it…
  236. 11. Deep Neural Networks
  237. Introduction
  238. Building a perceptron
  239. How to do it…
  240. Building a single layer neural network
  241. How to do it…
  242. Building a deep neural network
  243. How to do it…
  244. Creating a vector quantizer
  245. How to do it…
  246. Building a recurrent neural network for sequential data analysis
  247. How to do it…
  248. Visualizing the characters in an optical character recognition database
  249. How to do it…
  250. Building an optical character recognizer using neural networks
  251. How to do it…
  252. 12. Visualizing Data
  253. Introduction
  254. Plotting 3D scatter plots
  255. How to do it…
  256. Plotting bubble plots
  257. How to do it…
  258. Animating bubble plots
  259. How to do it…
  260. Drawing pie charts
  261. How to do it…
  262. Plotting date-formatted time series data
  263. How to do it…
  264. Plotting histograms
  265. How to do it…
  266. Visualizing heat maps
  267. How to do it…
  268. Animating dynamic signals
  269. How to do it…
  270. Index

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