Master the Basics of Machine Learning With These 6 Resources
Great blog posts, podcasts and online courses to help you get started
It seems like machine learning and artificial intelligence are topics at the top of everyone’s mind in tech. Be it autonomous cars, robots, or machine intelligence in general, everyone’s talking about machines getting smarter and being able to do more.
At the same time, for many developers, machine learning and artificial intelligence are nebulous terms representing complex mathematical and data problems they just don’t have the time to explore and learn.
As I’ve spoken with lots of developers and CTOs about Fuzzy.io and our mission to make it easy for developers to start bringing intelligent decision-making to their software without needing huge amounts of data or AI expertise, some were curious to learn more about the greater landscape of machine learning. Here are some of the links to articles, podcasts and courses discussing some of the basics of machine learning that I’ve shared with them. Enjoy!
This guide, written by the awesome Raul Garreta of MonkeyLearn, is perhaps one of the best I’ve read. In one easy-to-read article, he describes a number of applications of machine learning, the types of algorithms that exist, and how to choose which algorithm to use.
This piece by Stephanie Yee and Tony Chu of the R2D3 project gives a great visual overview of the creation of a machine learning model. In this case, a model to determine whether an apartment is located in San Francisco or New York. It’s a great look into how machine learning models are created and how they work.
A great starting point on some of the basics of data science and machine learning. Every other week, they release a 10–15 minute episode where hosts, Kyle and Linhda Polich give a short primer on topics like k-means clustering, natural language processing and decision tree learning, often using analogies related to their pet parrot, Yoshi. This is the only place where you’ll learn about k-means clustering via placement of parrot droppings.
This weekly podcast, hosted by Katie Malone and Ben Jaffe, covers diverse topics in data science and machine learning: teaching specific concepts like Hidden Markov Models and how they apply to real-world problems and datasets. They make complex topics extremely accessible, and teach you new words like clbuttic.
Plan for this online course to take several months, but you’d be hard-pressed to find better teachers than Peter Norvig and Sebastian Thrun. Norvig quite literally wrote the book on AI, having co-authored Artificial Intelligence: A Modern Approach, the most popular AI textbook in the world. Thrun’s no slouch, having previously led Google driverless car initiative.
This 11-week long Stanford course is available online via Coursera. Its instructor is Andrew Ng, Chief Scientist at Chinese internet giant Baidu.
Given the scope of machine learning as a topic, the above really only just begins to scratch the surface. Got your own favorite resource? Suggest it in the comments!