Learn Machine Learning
A friend of code(love), Matt Fogel is doing awesome things with machine learning at fuzzy.io. He’s shared this valuable list of resources to learn machine learning that he usually gives his friends who ask him for more information.
You’ll see his original post here: https://medium.com/@mattfogel/master-the-basics-of-machine-learning-with-these-6-resources-63fea5a21c1c#.ta2bhsq8y
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.
Yet for many developers, machine learning and artificial intelligence are dense terms representing complex problems they just don’t have time to learn.
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. A lot of them were curious to learn more about the greater landscape of machine learning.
You can describe machine learning as using techniques to help computers learn new ways of uncovering insights from data. This deep dive into the topic will explore many elements outside of this short guide if you’re interested in learning more.
What you need to understand before you learn machine learning is that it’s not a magic buzzword that will help solve every problem with you. Machine learning is a practical way to get more data insights with less work. Nothing more, nothing less.
To quote a professor in the field, “Machine learning is not magic; it can’t get something from nothing. What it does is get more from less. Programming, like all engineering, is a lot of work: we have to build everything from scratch. Learning is more like farming, which lets nature do most of the work. Farmers combine seeds with nutrients to grow crops. Learners combine knowledge with data to grow programs.”
If that excites you, here are some of the links to articles, podcasts and courses about machine learning that I’ve shared with my friends who were eager to learn more. I hope you 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 that determines whether an apartment is located in San Francisco or New York based on the traits they hold. It’s a great look into how machine learning models are created and how they work in practice.
3– Data Skeptic
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 the hosts (Kyle and Linhda Polich) give a short primer on topics like k-means clustering, natural language processing and decision tree learning. They often use 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. They teach specific advanced 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 either. He previously led the 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 and one of the pioneers of online education.
This list is really only scratching some of the complex and multifaceted topic that is machine learning. If you have your own favorite resource, please suggest it in the comments and start a discussion around it!