Algorithms of the Intelligent Web by Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko PDF

By Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko

ISBN-10: 1617292583

ISBN-13: 9781617292583

Summary

Algorithms of the clever net, moment Edition teaches an important techniques to algorithmic internet information research, allowing you to create your individual computing device studying purposes that crunch, munge, and wrangle info gathered from clients, internet functions, sensors and site logs.

Purchase of the print booklet features a unfastened publication in PDF, Kindle, and ePub codecs from Manning Publications.

About the Technology

Valuable insights are buried within the tracks net clients go away as they navigate pages and functions. you could discover them by utilizing clever algorithms just like the ones that experience earned fb, Google, and Twitter a spot one of the giants of internet facts development extraction.

About the Book

Algorithms of the clever net, moment Edition teaches you the way to create computing device studying purposes that crunch and wrangle information accrued from clients, net functions, and web site logs. during this completely revised variation, you are going to examine clever algorithms that extract genuine price from information. Key desktop studying strategies are defined with code examples in Python's scikit-learn. This publication publications you thru algorithms to seize, shop, and constitution info streams coming from the net. you are going to discover suggestion engines and dive into category through statistical algorithms, neural networks, and deep learning.

What's Inside

  • Introduction to laptop learning
  • Extracting constitution from data
  • Deep studying and neural networks
  • How suggestion engines work

About the Reader

Knowledge of Python is assumed.

About the Authors

Douglas McIlwraith is a desktop studying professional and knowledge technological know-how practitioner within the box of web advertising. Dr. Haralambos Marmanis is a pioneer within the adoption of computing device studying innovations for commercial ideas. Dmitry Babenko designs purposes for banking, coverage, and supply-chain administration. Foreword by way of Yike Guo.

Table of Contents

  1. Building purposes for the clever web
  2. Extracting constitution from facts: clustering and remodeling your facts
  3. Recommending appropriate content
  4. Classification: putting issues the place they belong
  5. Case learn: click on prediction for on-line advertising
  6. Deep studying and neural networks
  7. Making the ideal choice
  8. The way forward for the clever web
  9. Appendix - shooting facts at the web

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Additional resources for Algorithms of the Intelligent Web

Example text

Target_names array(['setosa', 'versicolour', 'virginica'], dtype='|S10') Although we’re working with a simple dataset, the concepts that you can learn here are universal among machine-learning algorithms. You’ll see later in this chapter how you can draw on machine learning to extract the structure of the data and how different techniques can be adopted to achieve this. To illustrate the concept of structure, let’s try some thought experiments with the Iris dataset. It may be that all Virginicas are much bigger than the other flowers, such that the lengths and widths of the sepals and petals have significantly higher values.

1, it’s equivalent to understanding that x increases with y regardless of the class of data. The method we’ll cover is known as principal component analysis (PCA). Used on its own, this algorithm uncovers the principal directions of variance in your data—helping you understand which of your features are important and which are not. It can also be used to map your data into a smaller feature space—that is, one with fewer data features—without losing much of the information it captures. Consequently, it can be used as a preprocessing step to your clustering algorithms (or any other intelligent algorithm).

The leftmost points might be considered to be clustered in the range 0–6, whereas the points to the right might be clustered in the range 9–16. We don’t necessarily know that this is a good clustering, but if we were to use our judgment, we might hazard a guess that they’re significantly different enough to be considered different groups of points. This is essentially what all clustering algorithms seek to do: to create groups of data points that we may consider to be clusters of points or structure in the data.

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Algorithms of the Intelligent Web by Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko


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