Book Image

Python Machine Learning (Wiley)

By : Wei-Meng Lee
Book Image

Python Machine Learning (Wiley)

By: Wei-Meng Lee

Overview of this book

With computing power increasing exponentially and costs decreasing at the same time, this is the best time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. Python Machine Learning begins by covering some fundamental libraries used in Python that make machine learning possible. You'll learn how to manipulate arrays of numbers with NumPy and use pandas to deal with tabular data. Once you have a firm foundation in the basics, you'll explore machine learning using Python and the scikit-learn libraries. You'll learn how to visualize data by plotting different types of charts and graphs using the matplotlib library. You'll gain a solid understanding of how the various machine learning algorithms work behind the scenes. The later chapters explore the common machine learning algorithms, such as regression, clustering, and classification, and discuss how to deploy the models that you have built, so that they can be used by client applications running on mobile and desktop devices. By the end of the book, you'll have all the knowledge you need to begin machine learning using Python.
Table of Contents (16 chapters)
Free Chapter
CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
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  • A
  • accuracy, computing of, 168–171
  • algorithms
    • categories of in ML, 5
    • comparing ML algorithms, 258–260
    • evaluating ML algorithms, 260–261, 277–279
    • supervised learning algorithms, 5
    • Two‐Class Decision Jungle algorithm, 258, 259, 260
    • Two‐Class Logistic Regression algorithm, 258, 259, 260
    • Two‐Class Support Vector Machine algorithm, 258
    • unsupervised learning algorithms, 5, 7
  • Anaconda, 88–1
  • apply() function, 57, 58, 59
  • area under the curve (AUC), 174
  • argsort() function, 33
  • arrange() function, 20
  • array assignment, 34–38
  • array indexing, 22–26
  • array math, 27–34
  • arrays
    • copying by reference, 34–35
    • copying by value (deep copy), 37
    • copying by view (shallow copy), 36–37
    • creating NumPy arrays, 20–21
    • reshaping of, 26–27
    • slicing of, 23–25
  • asmatrix() function, 30
  • auc() function, 174
  • Auto MPG Data Set, 98
  • Azure Machine Learning Studio (MAML)
    • comparing against other algorithms, 258–...