Our talented team of machine learning scientists recently put together an internal educational program called: “Machine learning for everyone”. The intent behind it was to provide a comprehensive but accessible primer on machine learning to everyone in the company.
Given the success of the program, I decided to turn it into a blog series and share it with everyone out here on “the internets”. There’s definitely a widespread misconception that you need to have a PhD in machine learning science or data science to understand what this new(ish) technology is all about.
We’re going to change that. Starting now.
So, who’s this series for?
Short answer: everyone, which means you. Whether you’re already an ML/AI buff or know next to nothing and want to step up your game, there’s something here for everyone. That said, our main objective is to keep things simple and straightforward.
Why should I care about machine learning?
Because you should… In all seriousness, machine learning is cropping up everywhere. If you don’t believe me, keep scrolling, and you’ll see what I mean.
FYI, this is just the tip of the iceberg.
Machine learning is enabling AI to beat us at our favorite games (Starcraft II, anyone?). And if you didn’t think robots could be funnyish, think again. AI comedians have been among us since 2017.
Finally, machine learning is making the distant fantasy of driverless cars possible in our lifetime.
Understanding machine learning puts us in a position to think up better solutions to some of the problems we’ve been grappling with for so long. It’s also the first step toward AI adoption. More importantly, though, by immersing ourselves in this AI-first world, we can get a firsthand look into the future and ensure that when it finally gets here, we’re prepared.
What we’re covering
Here, we’ll cover the basics, including different machine learning techniques and how AI is being operationalized today. When you’re done reading, you’ll be down with machine learning lingo and understand just how this tech is shaping the world today.
Examining a machine learning workflow.
We’re going to get down in the trenches with an in-depth look at how machine learning scientists turn real-world problems into tasks machines can solve. Since the NBA’s cancelled, and I’m missing my Raptors, we’re going to explore a dataset and use machine learning to assess whether or not Steph Curry is overpaid, because why not?
Neural networks and deep learning, simplified.
Neural networks are notoriously convoluted if you aren’t already in the know. But no more. In this installment, we’ll unpack how neural networks work in a computer vision application by using classification and object detection to identify kittens in animal photos. Everybody loves kittens.
Scoring an ‘A’ in reinforcement learning.
If we were actually testing you on reinforcement learning, here’s everything you’d need to get an A. We’ll look at AlphaGo, the first AI to beat a human at Go and explore exactly how it used reinforcement learning to do it. Finally, we’ll look at how integrate.ai uses reinforcement learning and what makes it so special.
By the end of this course, you’ll be spitting machine learning lingo with ease, which is bound to impress your friends and family. More than that, it’ll impress your coworkers (and your boss), as you list off some of the unique problems your business faces and how AI can offer a quick fix.
Okay, phew. Now that that’s over and done with, stay tuned for our first lesson, where we’ll unpack the fundamentals of machine learning. See you then!
A Simple Tool to Start Making Decisions with the Help of AI by Ajay Agrawal, Joshua Gans and Avi Goldfarb in the HBR
An Executive’s Guide to AI by McKinsey
AI School by Microsoft
Machine Learning by Stanford
Online courses by The Elements of AI
Online courses by fast.ai
About the author
On top of being a solutions engineer at integrate.ai, Ryan Lui is also a machine learning enthusiast. He has spent 15 years helping enterprises find creative solutions to some of their biggest business problems by making innovative technology accessible. He has worked at Netsuite, SAP, Oracle and Deloitte. He is also a former child model.