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Machine Learning with Swift

You're reading from   Machine Learning with Swift Artificial Intelligence for iOS

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787121515
Length 378 pages
Edition 1st Edition
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Authors (3):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Oleksandr Baiev Oleksandr Baiev
Author Profile Icon Oleksandr Baiev
Oleksandr Baiev
Alexander Sosnovshchenko Alexander Sosnovshchenko
Author Profile Icon Alexander Sosnovshchenko
Alexander Sosnovshchenko
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Toc

Table of Contents (14) Chapters Close

Preface 1. Getting Started with Machine Learning FREE CHAPTER 2. Classification – Decision Tree Learning 3. K-Nearest Neighbors Classifier 4. K-Means Clustering 5. Association Rule Learning 6. Linear Regression and Gradient Descent 7. Linear Classifier and Logistic Regression 8. Neural Networks 9. Convolutional Neural Networks 10. Natural Language Processing 11. Machine Learning Libraries 12. Optimizing Neural Networks for Mobile Devices 13. Best Practices

Classification – Decision Tree Learning

In the previous chapter, we discussed different types of machine learning, including supervised classification tasks; in this chapter, we will build our first Swift application for this. We will discuss main components of machine learning development stack, and will also exercise in data generation, exploratory analysis, preprocessing, and models training and evaluation in Python. After this, we will transfer our model to Swift. We will also discuss a specific class of supervised learning algorithms—decision tree learning and its extension: random forest.

The following topics are waiting for us in this chapter:

  • Machine learning software development stack
  • Python toolbox for machine learning: IPython, SciPy, scikit-learn
  • Dataset generation and exploratory analysis
  • Data preprocessing
  • Decision tree learning and random forest
  • Assessing...
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