Say Machine Learning to someone, and if they recognize the term, they'll probably think, "tech company." But while the origin stories of transformative technologies like machine learning, deep learning, and artificial intelligence often seem to take root in Silicon Valley, the truth is these are industry-agnostic innovations. Their impact is being felt across countless fields you might never have thought of as being ripe for technological advancement. Think about it like this: If you were a farmer, and someone came to you and said, there's a technology out there that can accurately predict your crop yields, would you be interested? Well, this is exactly what Descartes Labs does.

R is a purpose-built language meant for statistical computing, and is a clear winner for large-scale data-mining, visualization and reporting. You have easy access to a huge collection of packages (through the CRAN repository) that enable you to apply almost all kinds of Machine Learning algorithms, statistical tests and analysis procedures. The language itself has an elegant--albeit esoteric--syntax for expressing relationships, transforming data and performing parallelized operations.

You'll learn how to start with a question and/or a dataset, and use machine learning to turn them into insights. Naive Bayes: We jump in headfirst, learning perhaps the world's greatest algorithm for classifying text. The ability to generate new features independently and on the fly. Behind any great machine learning project is a great dataset that the algorithm can learn from. We were inspired by a treasure trove of email and financial data from the Enron corporation, which would normally be strictly confidential but became public when the company went bankrupt in a blizzard of fraud.