Machine Learning for Hackers
If you’re an experienced programmer interested in crunching data, Machine Learning for Hackers will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks.
Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.
“We can see how many people are interested in learning about machine learning (ML), but don’t have the mathematical background to read traditional treatments of the book,” says White. “We wanted to get people interested in ML in a hands-on fashion in the way that chemistry sets can get children excited about chemistry before they have the scientific background to learn the subject rigorously.”
White says that he and coauthor Drew Conway wrote the book to match the tech community’s growing interest in ML.
He explains: “Our intended audience is anyone with a solid background in computing programming and a quantitative mind, but no formal training in advanced mathematics. For people who are experts in calculus and linear algebra, the traditional books on machine learning are probably more appropriate. But we find that most people we meet don’t have a strong enough command of those topics to learn ML from the traditional books in a timely fashion.”
Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.
- Develop a naive Bayesian classifier to determine if an email is spam, based only on its text
- Use linear regression to predict the number of page views for the top 1,000 websites
- Learn optimization techniques by attempting to break a simple letter cipher
- Compare and contrast U.S. Senators statistically, based on their voting records
- Build a “whom to follow” recommendation system from Twitter data.