Machine learning has long powered many products we interact with daily—from "intelligent" assistants like Apple's Siri and Google Now, to recommendation engines like Amazon's that suggest new products to buy, to the ad ranking systems used by Google and Facebook.

More recently, machine learning has entered the public consciousness because of advances in "deep learning"—these include AlphaGo's defeat of Go grandmaster Lee Sedol and impressive new products around image recognition and machine translation.

While much of the press around machine learning has focused on achievements that were not previously possible, the full range of machine learning methods—from traditional techniques that have been around for decades to more recent approaches with neural networks—can be deployed to solve many important (but perhaps more prosaic) problems that businesses face. Examples of these applications include, but are by no means limited to, fraud prevention, time-series forecasting, and spam detection.

InfoQ has curated a series of articles for this introduction to machine learning eMagazine covering everything from the very basics of machine learning (what are typical classifiers and how do you measure their performance?), to production considerations (how do you deal with changing patterns in data after you’ve deployed your model?), to newer techniques in deep learning. After reading through this series, you should be ready to start on a few machine learning experiments of your own.

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