Can a biology student get into machine learning?

Our class went on an excursion. We played with different kinds of food compounds which could shape themselves around the outside of a balloon. And then got taught about these tools which could output very small drops.

‘What are these called?’ I asked.


We got back to school. The teacher turned and asked what I thought of the trip.

‘I liked the tour but it was very focused on science.’

‘That’s what it was all about.’

She was right. We went to a science institute.

The same teacher asked me to be captain of debating. It was tradition to get up and talk in front of the school. I got up and gave a talk. Everyone clapped but my speech wasn’t as good as I wanted it to be.

I was set out to do law. I’d see lawyers on the TV. All it looked like was a form of debating where everyone wears suits and says ‘objection!’ Followed by something smart.

I thought, ‘I could do that.’

A few episodes of Law & Order and everyone becomes a lawyer.

We got our grades, I got 7/25, lower was better. Not as good as I hoped but I expected it. Most of my senior year was devoted to running our Call of Duty team. We were number one in Australia.

The letters came, it was time to choose what to study at university. I read the headings in bold and left the rest to read later. I was set out to do law.

We were on the waterfront riding scooters. There was a girl there I knew from primary school. I had a crush on her in grade four. For Easter, my mum gave me two chocolates to take in, a big one and a small one. The big one was for my teacher, Mrs Thompson. When I got to school I gave the big one to the girl. But she still liked Tony Black.

She was smart. That’s why I liked her.

‘What are you studying?’ I asked.


‘What’s that?’

‘Biomedical science, it’s what you study before getting into medicine.’

‘Oh, that’s what I’m doing.’

I wasn’t. I hadn’t filled out the form. I was set out to do law.

I got home and checked the study guide. Biomedical science required a score of 11/25. I was eligible. I put it down as my number one preference. Same as the girl.

The email came a few weeks later. I got into my number one preference. A Bachelor of Science majoring in Biomedical Science.

We went to orientation day together. I spent $450 on textbooks. I used my mum's card. There was a biology one with 1200 pages. It had a red spine and a black cover. The latest edition.

Our timetables were the same. 30-something contact hours per week. I lived 45-minutes from university by car. 90-minutes by train and bus. The first lecture of the week was at 8 am on Monday. BIOL1020. Why someone chose this time for a lecture still confuses me.

The lecturer started.

‘30% of you will fail this course.’

‘That won’t be me.’

It was me.

My report card in high school went something like this.

  • Maths - B

  • Extension Maths - C

  • Physics - B

  • Religion - A+ (most of religion was storytelling, debating helped with this)

  • English - B

  • Geography - B

  • Sports - A

Not a single biology course. I was set out for law.

I took the same course the next year. I passed. It took me a year to get some foundations in biology. By then the girl was already through to second year. She was smart. That’s why I liked her.

Being a doctor sounded cool.

‘I’m going to be a doctor,’ I told people at parties.

But by end of my second year, my grades were still poor.

The Dean of Science emailed me. Not him. One of his secretaries. But it said I had to go and see him. My grades were bad. The email was the warning. Improve or we’ll kick you out.

I met with the Dean. He told me I could change courses if I wanted to. I changed to food science and nutrition. Still within the health world but less biology. I wasn’t set out for law.

My grades improved and I graduated three years later. Five years to do a three-year degree.

People asked when I finished.

‘What are you going to do with your nutrition degree?’

‘Stay healthy.’

I thought it was a good plan.

I was working at Apple. They paid for language courses. I signed up for Japanese and Chinese. Japanese twice a week. Chinese once a week.

My study routine was solid. The main skill I learned at university was learning how to learn.

I was getting pretty good. When Chinese customers came in, I’d ask them if they had a backup of their iPhone in Chinese.

‘Nĭ yŏu méiyŏu beifan?’

They loved it.

I passed the level 2 Japanese exam the night before flying to Japan. Being solo for a month meant plenty of walking. Plenty of listening to podcasts. Most of them were about technology or health. Two things I’m interested in. And all the ones about technology kept mentioning machine learning.

On the trains between cities, I’d read articles online.

I went to Google.

‘What is machine learning?’

‘How to learn machine learning?’

I quit Apple two months after getting back from Japan. Travelling gave me a new perspective. Cliche but true.

My friend quit too. We worked on an internet startup for a couple of months. AnyGym, the Airbnb of fitness facilities. It failed. Partly due to lack of meaning, partly due to the business model of gyms depending on people not showing up. We wanted to do the opposite.

Whilst building the website, the internet was exploding with machine learning.

I did more research. The same Google searches.

‘What is machine learning?’

‘How to learn machine learning?’

Udacity’s Deep Learning Nanodegree came up. The trailer videos looked epic and the colours of the website were good on the eye. I read everything on the page and didn’t understand most of it. I got to the bottom and saw the sign-up price, thought about it, scrolled back to the top and then back to the bottom. I closed my laptop.

The prerequisites contained some words I’d never heard of.

Python programming, statistics and probability, linear algebra.

More research. Google again.

‘How to learn Python?’

‘What is linear algebra?’

I had some savings from Apple but they were supposed to last a while. Signing up for the Nanodegree would take a big chunk out.

I signed up. Class started in 3-weeks.

Back to the internet. It was time to learn Python.

‘How hard could it be?’ I thought.

Treehouse’s Python course looked good. I enrolled. I went through it fast. 3-4 hours every day.

Emails came through for the Deep Learning Nanodegree. There was a Slack channel for introductions. I joined it and starting reading.

‘Hey everyone, I’m Sanjay, I’m a software engineering at Google.’

‘Hello, I’m Yvette, I live in San Francisco and am a data scientist at Intuit.’

I kept reading. More of the same.

Mine went something like this.

‘Nice to meet you all! I’m Daniel, I started learning programming 3-weeks ago.’

After seeing the experience level of others, I emailed Udacity support asking what the refund policy was. ‘Two weeks,’ they said. I didn’t reply.

Four months later, I graduated from the Deep Learning Foundations Nanodegree. It was hard. All my assignments were either a couple of days late or right on time. I was learning Python and math I needed as I needed it.

I wanted to keep building upon the knowledge I’d gained. So I explored the internet for more courses like the Deep Learning Nanodegree. I found a few, Andrew Ng’s, the Udacity AI Nanodegree, and put them together.

My self-created AI Masters Degree was born. I named it that because it’s easier than saying, ‘I’m stringing together a bunch of courses.’ Plus, people kind of understand what a Masters Degree is.

8-months into it I got a message from Ashlee on LinkedIn.

‘Hey Dan, what you’re posting is great, would you like to meet Mike?’

I met Mike.

‘If you’re into technology and health, you should meet Cam.’

I met Cam. I told him I was into technology and health and what I had been studying.

‘Would you like to come in on Thursday to see what it’s like?’

I went in on Thursday.

It was a good day. The team were exploring some data with Pandas.

‘Should I come back next Thursday?’ I asked.


A couple of Thursday’s later I sat down with the CEO and lead Machine Learning Engineer. They offered me a role. I accepted.

One of our biggest projects is in healthcare. Immunotherapy Outcome Prediction (IOP). The goal is to use genome data to better predict who is most likely to respond to immunotherapy. Right now about it’s effective in about 42% of people. But the hard part is figuring out which 42%.

To help with the project we hired a biologist and a neuroscientist and a few others.

Before joining, they hadn’t done much machine learning at all. But thanks to the resources available online and a genuine curiosity to learn more, they’ve produced some world class work.

We had a phone call with the head of Google’s Genomics team the other day.

‘I’m really impressed by your work.’

They’ve done an amazing job. But compliments should always be accepted with a grain of salt and a smile. Results on paper and results in the real world are two different things.

The team know that.

Can a biology student get into AI and machine learning?

I’m not a good example because I failed biology. Almost twice.

But I sit across from two who have done it.

The formula?

You’ve already got it. The same one which led you to learn more about biology. Be curious and have the courage to be wrong.

Biology textbooks get rewritten every 5-years or so right?

Back to day one BIOL1020. The lecturer had another saying.

‘What you learn this year will probably be wrong in 5-years.’

It’s the same in machine learning. Except the math. Math sticks around.

Photo from    Learning Intelligence 37 — Learning Data Science with my Brother.    You can see my biology textbook gathering dust in the background.

Photo from Learning Intelligence 37 — Learning Data Science with my Brother. You can see my biology textbook gathering dust in the background.