2014 will be the year you'll learn Machine Learning
This article was originally posted in my newsletter in which I write about how to bootstrap Machine Learning. After all the positive feedback I got, I thought I'd repost it here!
In order to explain why I am so excited about 2014, and why you should be too, let me go over some history and recent developments related to Machine Learning and Prediction APIs.
Demand for Machine Learning talent exceeding supply
In May 2011, McKinsey published its Big Data white paper in which it predicted a shortage of people with expertise in Machine Learning:
"A significant constraint on realizing value from big data will be a shortage of talent, particularly of people with deep expertise in statistics and machine learning, and the managers and analysts who know how to operate companies by using insights from big data."
McKinsey estimates that demand could be 50 to 60% greater than its projected supply by 2018...
Funnily enough, this study came out in the same month and year that Google Prediction API was released to the public. If you've been following me, you'll know that I believe that such APIs are the key to resolving this talent issue, because they democratize Machine Learning.
#Prediction #APIs are the solution to the shortage of #MachineLearning talent predicted by McKinsey in 2011 https://t.co/y7FbPRA9B3
— Louis Dorard (@louisdorard) March 6, 2014ML most popular course, but high drop-out rate
Last December 31 I stumbled upon an article from The New York Times in which a Machine Learning Stanford course was mentioned:
The largest class on campus this fall at Stanford was a graduate level machine-learning course covering both statistical and biological approaches, taught by the computer scientist Andrew Ng. More than 760 students enrolled. “That reflects the zeitgeist,” said Terry Sejnowski, a computational neuroscientist at the Salk Institute, who pioneered early biologically inspired algorithms. “Everyone knows there is something big happening, and they’re trying find out what it is.”
This was also picked up by Forbes in Why Is Machine Learning (CS 229) The Most Popular Course At Stanford. Besides, Andrew Ng's online ML course is one of the most popular on Coursera. Unfortunately, these courses do not tell you about Prediction APIs but about the inner workings of ML algorithms. This is way more technical than necessary, and I believe that this is why there's a 90% drop-out rate. Anyway, it all testifies to the fact that people are increasingly aware of ML, that they want to learn Machine Learning and to use it in what they are doing.
Prediction APIs on the way to become mainstream
I believe that 2014 will be the year of ML because of what's going on around Prediction APIs: they make it so much quicker and easier to do Machine Learning, they are gaining popularity, more and more are popping up, and resources to teach people how to use them are starting to appear. Also, I believe that competition will push API providers to introduce awesome new features but also to find ways to make it easier and quicker to start coding with Prediction APIs. As an example of that, Codenvy announced an integration of BigML last December, which allows to set up an ML coding environment in just a few clicks.
What do you think? Will you choose to learn traditional ML on Coursera, which just started on March 3 (you can still enroll), or Prediction APIs with Bootstrapping Machine Learning, which is coming out next month (April 2014)?
