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Python Machine Learning By Example 1st edition by Yuxxi Liu ISBN 1783553111 978-1783553112

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ISBN 10: 1783553111
ISBN 13:  978-1783553112
Author: Yuxxi Liu

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning.

 

This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques.

 

Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.

Python Machine Learning By Example 1st Table of contents:

  1. What this book covers
  2. What you need for this book
  3. Who this book is for
  4. Conventions
  5. Reader feedback
  6. Customer support
  7. Downloading the example code
  8. Errata
  9. Piracy
  10. Questions
  11. Getting Started with Python and Machine Learning
  12. What is machine learning and why do we need it?
  13. A very high level overview of machine learning
  14. A brief history of the development of machine learning algorithms
  15. Generalizing with data
  16. Overfitting, underfitting and the bias-variance tradeoff
  17. Avoid overfitting with cross-validation
  18. Avoid overfitting with regularization
  19. Avoid overfitting with feature selection and dimensionality reduction
  20. Preprocessing, exploration, and feature engineering
  21. Missing values
  22. Label encoding
  23. One-hot-encoding
  24. Scaling
  25. Polynomial features
  26. Power transformations
  27. Binning
  28. Combining models
  29. Bagging
  30. Boosting
  31. Stacking
  32. Blending
  33. Voting and averaging
  34. Installing software and setting up
  35. Troubleshooting and asking for help
  36. Summary
  37. Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
  38. What is NLP?
  39. Touring powerful NLP libraries in Python
  40. The newsgroups data
  41. Getting the data
  42. Thinking about features
  43. Visualization
  44. Data preprocessing
  45. Clustering
  46. Topic modeling
  47. Summary
  48. Spam Email Detection with Naive Bayes
  49. Getting started with classification
  50. Types of classification
  51. Applications of text classification
  52. Exploring naive Bayes
  53. Bayes’ theorem by examples
  54. The mechanics of naive Bayes
  55. The naive Bayes implementations
  56. Classifier performance evaluation
  57. Model tuning and cross-validation
  58. Summary
  59. News Topic Classification with Support Vector Machine
  60. Recap and inverse document frequency
  61. Support vector machine
  62. The mechanics of SVM
  63. Scenario 1 – identifying the separating hyperplane
  64. Scenario 2 – determining the optimal hyperplane
  65. Scenario 3 – handling outliers
  66. The implementations of SVM
  67. Scenario 4 – dealing with more than two classes
  68. The kernels of SVM
  69. Choosing between the linear and RBF kernel
  70. News topic classification with support vector machine
  71. More examples – fetal state classification on cardiotocography with SVM
  72. Summary
  73. Click-Through Prediction with Tree-Based Algorithms
  74. Brief overview of advertising click-through prediction
  75. Getting started with two types of data, numerical and categorical
  76. Decision tree classifier
  77. The construction of a decision tree
  78. The metrics to measure a split
  79. The implementations of decision tree
  80. Click-through prediction with decision tree
  81. Random forest – feature bagging of decision tree
  82. Summary
  83. Click-Through Prediction with Logistic Regression
  84. One-hot encoding – converting categorical features to numerical
  85. Logistic regression classifier
  86. Getting started with the logistic function
  87. The mechanics of logistic regression
  88. Training a logistic regression model via gradient descent
  89. Click-through prediction with logistic regression by gradient descent
  90. Training a logistic regression model via stochastic gradient descent
  91. Training a logistic regression model with regularization
  92. Training on large-scale datasets with online learning
  93. Handling multiclass classification
  94. Feature selection via random forest
  95. Summary
  96. Stock Price Prediction with Regression Algorithms
  97. Brief overview of the stock market and stock price
  98. What is regression?
  99. Predicting stock price with regression algorithms
  100. Feature engineering
  101. Data acquisition and feature generation
  102. Linear regression
  103. Decision tree regression
  104. Support vector regression
  105. Regression performance evaluation
  106. Stock price prediction with regression algorithms
  107. Summary
  108. Best Practices
  109. Machine learning workflow
  110. Best practices in the data preparation stage
  111. Best practice 1 – completely understand the project goal
  112. Best practice 2 – collect all fields that are relevant
  113. Best practice 3 – maintain consistency of field values
  114. Best practice 4 – deal with missing data
  115. Best practices in the training sets generation stage
  116. Best practice 5 – determine categorical features with numerical values
  117. Best practice 6 – decide on whether or not to encode categorical features
  118. Best practice 7 – decide on whether or not to select features and if so, how
  119. Best practice 8 – decide on whether or not to reduce dimensionality and if so how
  120. Best practice 9 – decide on whether or not to scale features
  121. Best practice 10 – perform feature engineering with domain expertise
  122. Best practice 11 – perform feature engineering without domain expertise
  123. Best practice 12 – document how each feature is generated
  124. Best practices in the model training, evaluation, and selection stage
  125. Best practice 13 – choose the right algorithm(s) to start with
  126. Naive Bayes
  127. Logistic regression
  128. SVM
  129. Random forest (or decision tree)
  130. Neural networks
  131. Best practice 14 – reduce overfitting
  132. Best practice 15 – diagnose overfitting and underfitting
  133. Best practices in the deployment and monitoring stage
  134. Best practice 16 – save, load, and reuse models
  135. Best practice 17 – monitor model performance
  136. Best practice 18 – update models regularly
  137. Summary

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