By Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko

Summary

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

Purchase of the print booklet encompasses a loose 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 purposes. 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 info development extraction.

About the Book

Algorithms of the clever internet, moment Edition teaches you ways to create computer studying functions that crunch and wrangle facts accumulated from clients, internet functions, and site logs. during this completely revised version, you are going to examine clever algorithms that extract actual worth from info. Key laptop studying thoughts are defined with code examples in Python's scikit-learn. This booklet publications you thru algorithms to seize, shop, and constitution information streams coming from the net. you will discover advice engines and dive into class 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 advice engines work

About the Reader

Knowledge of Python is assumed.

About the Authors

Douglas McIlwraith is a desktop studying specialist and knowledge technology practitioner within the box of web advertising. Dr. Haralambos Marmanis is a pioneer within the adoption of computer studying thoughts for commercial recommendations. Dmitry Babenko designs purposes for banking, coverage, and supply-chain administration. Foreword via Yike Guo.

Table of Contents

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

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Additional info for Algorithms of the Intelligent Web

Example text

6. 6 gives what is known as the receiver operating characteristic (ROC) curve of two imaginary classifiers. This curve traces the TPR and FPR as we twist our conceptual knob, thereby changing the parameters of the classifier. As mentioned previously, the ideal is a classifier with TPR=1 and TNR=1 (FPR=0); thus models that trace a line closer to the top-left corner of the graph are conceptually better, because they do a better job at separating negative and positive classes correctly. In this graph, classifier 2 clearly does a better job and should be chosen over classifier 1.

This process is known as model training. We can use the expectationmaximization algorithm to do just this, and you’ll see this in the following section. There’s an important distinction to make here about the difference between sample and population distribution. First, we assume that data from the 334 users is representative of the entire population. We’re also making an assumption that the underlying distribution that generates the data is Gaussian and that the sample data is drawn from this.

This is a good question! In general, ML techniques focus on understanding and generalizing the underlying structure and relationships of a dataset, whereas predictive analytics is often about scoring, ranking, and making predictions about future data items and trends, frequently in a business or operational setting. 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.

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