Machine Learning for Hackers: Case Studies and Algorithms to Get You Started

Drew Conway Machine Learning for Hackers Reviews Summary

Ratings Breakdown

Rated 3.8 by 61 people

Pros from Reviews

  • parsimonious text
  • interesting examples
  • clever coding
  • less expensive and easier to understand than most Springer texts
  • excellent coverage of data cleaning and exploratory data analysis
  • good coverage of ggplot2, plyr, and reshape2 packages
  • immediately practical case studies
  • detailed description of building machine learning solutions

Cons from Reviews

  • substantial part of code is peripheral tasks
  • some code is out of date
  • too much time spent on R
  • not enough coverage of machine learning algorithms
  • errors in algorithms presentation
  • plots presented in black and white
  • trivial explanations of basic concepts
  • not recommended for beginners

Notable Features

Case Studies
Algorithms
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