The following free book is for anyone interested in machine learning and data mining.
About the book (by the author)
Machine learning is a set of tools, which roughly allow us to “teach” computers how to perform tasks by providing examples of how they should be done.
For example, let’s say we want to write a program to differentiate between legitimate email messages and spam. We can try writing a set of simple rules, for example flagging messages that contain certain attributes (like the word “viagra” or an apparently bogus header).
However, writing rules to precisely isolate which text is valid can be quite difficult to get right, which can result in a lot of lost spam messages, or worse, a lot of lost emails.
Even worse, spammers will actively adjust the way you send spam (typing “vi@gr@” for example) to fool these tactics. Writing effective rules and keeping them updated quickly becomes an arduous task.
Fortunately, machine learning has provided a solution. Modern spam filters are “learned” by example: we provide learning algorithms with example emails that we have manually labeled as “ham” (legitimate email) or “spam” (junk email), and Algorithms learn to differentiate them automatically. ,
To use machine learning in practice you need your own prior knowledge and experimentation to solve problems. But with the tools of machine learning, you can do amazing things!
Content (Subject)
- Introduction to Machine Learning
- linear regression
- nonlinear regression
- quadratic
- fundamentals of probability
- Probability Density Functions (PDF)
- Estimate
- even more
data sheet
year: 2015
Editor: University of Toronto
Language: English
Shape: 1649 KB
License: pending review
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