Article Series: An Introduction to Machine Learning for Software Developers

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Introduction

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.

In this series, we give an introduction to some powerful but generally applicable techniques in machine learning. These include deep learning but also more traditional methods that are often all the modern business needs.

After reading the articles in the series, you should have the knowledge necessary to embark on concrete machine learning experiments in a variety of areas on your own.

Download all the articles in this series as a PDF.

Contents

Introduction to Machine Learning with Python

This series explores various topics and techniques in machine learning, arguably the most talked-about area of technology and computer science over the past several years. In this article, Michael Manapat begins with an extended "case study" in Python: how can we build a machine learning model to detect credit card fraud?

Practicing Machine Learning with Optimism

Using machine learning to solve real-world problems often presents challenges that weren't initially considered during the development of the machine learning method. Alyssa Frazee addresses a few examples of such issues and hopefully provides some suggestions (and inspiration) for how to overcome the challenges using straightforward analyses on the data you already have.

Anomaly Detection for Time Series Data with Deep Learning

This article introduces neural networks, including brief descriptions of feed-forward neural networks and recurrent neural networks, and describes how to build a recurrent neural network that detects anomalies in time series data. To make the discussion concrete, Tom Hanlon shows how to build a neural network using Deeplearning4j, a popular open-source deep-learning library for the JVM.

Real-World, Man-Machine Algorithms

In this article, Edwin Chen and Justin Palmer talk about the end-to-end flow of developing machine learning models: where you get training data, how you pick the ML algorithm, what you must address after your model is deployed, and so forth.

Book Review: Andrew McAfee and Erik Brynjolfsson's "The Second Machine Age"

Andrew McAffee and Erik Brynjolfsson begin their book The Second Machine Age with a simple question: what innovation has had the greatest impact on human history?

Series Manager

Michael Manapat (@mlmanapat) leads work on Stripe’s machine learning products, including Stripe Radar.

Prior to Stripe, he was an engineer at Google and a postdoctoral fellow in and lecturer on applied mathematics at Harvard.

He received a Ph.D. in mathematics from MIT.

Related Topics:

• AI, ML & Data Engineering
• Python
• Dynamic Languages
• Neural Networks
• Deep Learning
• Artificial Intelligence
• Random Forests
• Fraud Detection
• Adversarial Machine Learning
• An introduction to Machine Learning
• Machine Learning
• Article Series
• Stripe
• Decision Trees
• AlphaGo

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