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

By Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko


Algorithms of the clever net, moment Edition teaches crucial methods to algorithmic net info research, allowing you to create your personal computing device studying purposes that crunch, munge, and wrangle information accumulated from clients, net functions, sensors and site logs.

Purchase of the print booklet incorporates a loose booklet in PDF, Kindle, and ePub codecs from Manning Publications.

About the Technology

Valuable insights are buried within the tracks net clients depart as they navigate pages and purposes. you could discover them through the use of clever algorithms just like the ones that experience earned fb, Google, and Twitter a spot one of the giants of net information trend extraction.

About the Book

Algorithms of the clever net, moment Edition teaches you ways to create computer studying purposes that crunch and wrangle facts amassed from clients, internet purposes, and web site logs. during this absolutely revised version, you are going to examine clever algorithms that extract genuine worth from information. Key laptop studying options are defined with code examples in Python's scikit-learn. This e-book publications you thru algorithms to catch, shop, and constitution facts streams coming from the net. you are going to discover suggestion engines and dive into type 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 computer studying professional and information technology practitioner within the box of web advertising. Dr. Haralambos Marmanis is a pioneer within the adoption of laptop studying innovations for commercial suggestions. Dmitry Babenko designs functions for banking, coverage, and supply-chain administration. Foreword by means of Yike Guo.

Table of Contents

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

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Extra resources for Algorithms of the Intelligent Web

Example text

Although this may appear to be a somewhat fuzzy comparison, there’s a large degree of overlap among the intelligentalgorithm classes we’ve introduced, and these categories aren’t concrete and definite. Confusingly, analysts don’t usually create PA solutions. Predictive analytics is about creating models that, given some information, can respond quickly and efficiently with useful output that predicts some future behavior. Software engineers and data scientists often create such systems. Just to add to the confusion, PA models may use ML and AI approaches!

Target? And what did we create? Bunch. This is a dictionary-like object with several attributes (for more details, see the scikit-learn documentation). target is a 1-D array of classes. 1, we printed out an array of the attributes (or features) of the flower, along with the class of the flower. But what do these feature values and classes relate to? Each feature relates to the length and width of the petal and sepal, whereas the class relates to the type of flower that was sampled. The next listing provides a summary of this information.

By now, you should have a fairly good, albeit high-level, understanding of intelligent algorithms and how you’re going to use them. You’re probably sufficiently motivated and anxious to dive into the details. We won’t disappoint you. Every following chapter is loaded with new and valuable code. 18 These should help guide you as you progress through this book and, later on, the field of intelligent algorithms. 1 Your data is not reliable Your data may be unreliable for many reasons. That’s why you should always examine whether the data you’ll work with can be trusted before you start considering specific intelligent-algorithmic solutions to your problem.

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