How do non-technical people learn machine learning?

I drove forward.

The parking inspector starting speaking.

Do you have a valid Queensland drivers licence?

I answered.

Yes.

He kept going.

Well, you shouldn’t because you should know you can’t park in bus stops.

The Uber app guided me to pick up riders. I followed the app without paying attention to the signs. I was more focused on picking them up and getting them out of there. It was 2 am.

The fine came through. $250. I worked for free that night.

I paid it.

Then thought to myself.

I’m not driving Uber anymore.

Two weeks later I got offered an internship as a machine learning engineer.

9-months before that I started my own AI Masters Degree.

Before that, I graduated with a Food Science and Nutrition Degree. Non-technical as it gets.

Where do you start?

A) Delete non-technical from your vocabulary

Words have power. Real power.

They’re magic. It’s why when you list out the letters of a word it’s called spelling.

People isolate themselves with their words.

Some say play to your strengths, others say work on your weaknesses. Both good advice. Which one should you listen to?

As soon as you start saying you’re non-technical, you’re non-technical.

I was speaking to someone the other night.

I used to think my main strength was talking to people.

I told him.

I’ll never be the best engineer.

He snapped back.

Not with that attitude.

It changed me. I’m not trying to be the best engineer but referring to myself as never being the best was limiting my ability to grow.

I’m getting better. Much better. Why?

Because I told myself so.

You can too.

Belief is 50% of anything.

B) Use the placebo effect to your advantage

Here’s another.

Have you heard of the placebo effect?

It’s one of the most dominant forces in science. But it’s not limited to researchers in lab coats. You can use it too.

Example.

People who thought they were taking good medicine (but were actually only taking a placebo, or a sugar pill) got healthier.

What?

Why?

Because they thought they were taking the good medicine and the cosmic forces between the mind, body and universe set them on the track to better health.

I’ve simplified it and used cosmic forces on purpose. Because this effect is still unknown other than describing it as a belief which led to improvement.

What can you do?

The same thing. Take a placebo pill of learning machine learning.

Write it down.

This will be hard for me but I can learn it.

Again.

This will be hard for me but I can learn it.

All useful skills are hard to learn.

C) Get some coding foundations

The first two are most important. The rest snowballs as you go.

Someone commented on my LinkedIn the other night.

One of my favourite sayings from my professor was, "in theory, theory and practice are the same. In practice, they are completely different".

Good advice.

Could you learn to swim without ever touching the water?

If you want to get into machine learning, learn to code, it’s hard to begin with but you get better.

Practice a little every day. And if you miss a day, no problem, continue the next day.

It’s like how your 3-year-old self would’ve learned to talk.

In the beginning, you could only get a few sounds out. A few years later, you can have whole conversations.

Learning to code is the same. It starts out as a foreign language. But then as you learn more, you can start to string things together.

My brother is an accountant. He’s starting to learn machine learning. I recommended he start with Python on DataCamp. Python code reads similar to how you would read words. Plus, DataCamp teaches code from 0 to full-blown machine learning. He's been loving it.

D) Build a framework

Once you’ve been through a few DataCamp courses or learned some Python in general, start to piece together where you want to head next.

This is hard.

Because in the beginning it’s hard to know where you want to go and there’s a bunch of stuff out there.

So you’ve got two problems. Not knowing where to go and having too many things to choose from.

If you know you want to learn more machine learning, why not put together your own path?

What could this look like?

  1. 3–4 months of DataCamp

  2. 3–4 months of Coursera courses

  3. 3–4 months going through the fast.ai curriculum

Do you have to use these?

No.

I only recommend them because I’ve been through them as a part of my AI Masters Degree. The best advice comes from mentors who are 1–3 years ahead of you. Short enough to still remember the specifics and long enough to have made some mistakes.

Will it be easy?

No.

All useful skills are hard to learn.

Day by day you may not feel like you’re learning much. But by the end of the year (3 blocks of 4 months) you’ll be a machine learning practitioner.

E) You don’t need math*

*to get started.

When you look at machine learning resources, many of them have a bunch of math requirements.

Math isn’t taught well in schools so it scares people.

Like code, mathematics is another language. Mathematics is the language of nature.

If the math prerequisites of some of the courses you’ve been looking at are holding you back, you can get started without it.

The Python coding frameworks such as TensorFlow, PyTorch, NumPy and sklearn, abstract away the need to fully understand the math (don’t worry if you don’t know what these are you’ll find them later).

As you go forward and get better at the code, your project may demand knowledge of the math involved. Learn it then.

F) It’s always day one

Am I the best machine learning engineer?

No.

But two years ago I was asking myself the question, how I do learn machine learning with no technical skills?

The answer was simple, start learning the technical skills and don’t stop, but there were details.

Details like above.

Driving Uber on the weekends allowed me to pay for the courses I was doing to learn machine learning.

Getting a fine for picking up people in the wrong spot helped me make the decision to back myself.

A year into being a machine learning engineer and I’m more technical than when I started but there’s plenty more to learn.