We were hosting a Meetup on robotics in Australia and it was question time.
Someone asked a question.
“How do I get into artificial intelligence and machine learning from a different background?”
Nick turned and called my name.
“Where’s Dan Bourke?”
I was backstage and talking to Alex. I walked over.
“Here he is,” Nick continued, “Dan comes from a health science background, he studied nutrition, then drove Uber, learned machine learning online and has now been with Max Kelsen as a machine learning engineer for going on a year.”
Nick is the CEO and Co-founder of Max Kelsen, a technology company in Brisbane.
I stood and kept listening.
“He has documented his journey online and if you have any questions, I’m sure he’d be happy to help.”
The questions finished and I went back to the food.
Ankit came over. He told me about the project he was working on to use machine learning to try and understand student learning better. He was combining lecture attendance rates, time spent on the online learning portal, quiz results, plus a few other things. He’d even built a front-end web portal to interact with the results.
Ankit’s work inspired me. It made me want to do better.
Then a few more people started coming over and asking questions about how to get into machine learning. All from different fields.
This is the hard part. I still see myself as a beginner.
But the best mentor is someone who’s 1–2 years in front of you. Someone who has just been through what you’re about to go through. Any longer and the advice gets fuzzy. You want it when it’s fresh.
My brother is getting into machine learning. Here’s what I’ve been saying to him (and myself if I were to start again).
A) Get some Python foundations (3–4 months)
The language doesn’t really matter. It could be R, Java, Python, whatever. What matters is picking one and sticking with it.
If you’re starting out, you’ll find it hard to go wrong with Python.
And if you want to get into applied machine learning, code is compulsory.
Pick a foundations course from online and follow it through for a couple of months. Bonus points if it’s geared towards teaching data science at the same time. DataCamp is great for this.
It’ll get hard at times but that’s the point. Learning a programming language is like learning another language and another way of thinking at the same time.
But you’ve done it before. Remember when you were 3? Probably not. But people all around you were using words and sounds you’d never heard before. Then after a while, you started using them too.
B) Start making things when you’re not ready
Apply what you’ve learned as soon as you can.
No matter how many courses you’ve completed, you’ll never be 100% ready.
Don’t get lured into completing more courses as a sign of competence.
This is one thing I’d change if I went back and started again.
Find a project of your own to work on and learn through being wrong.
Back to your 3-year-old self. Every 3rd word you said would’ve been wrong. No sentence structure, no grammar either. Everything just came out.
C) There’s a lot out there so reduce the clutter
There are plenty of courses out there. All of them great.
It’s hard to find a bad one.
But here’s the thing. Since there are so many, it can be hard to choose. Another trap which can hold you back.
To get around this, I made my own AI Masters Degree. My own custom track to follow.
You can copy it if you want. But I encourage you to spend a few days doing research of your own and seeing what’s best for you.