This part sucks

At the start you were making plenty of progress.

One problem solved. Then another. And another.

Then something happens. The results dry up. But the effort stays the same.

It happens after the first few months of weight loss and the kilos stop dropping off.

It happens after the first few courses on data science and the knowledge gains slow down.

It happens when you hit the end of all the machine learning models you know how to use.

The dip.

In 2012, I failed statistics. For the second time. The dip.

2013, learning to code, hit the dip, stopped.

2015, competing in bodybuilding, dip, kept going, got on stage, got a taste for the other side.

In 2017, we started a website, AnyGym, the Airbnb of gyms. Then we stopped. It got hard. We hit the dip and gave up.

But the taste remained.

Now every time it gets hard I think about the dip.

I ask a question.

‘Is it worth it to keep going?’

Sometimes it’s not.

To pass the dip of one thing, you have to quit the dip of many other things.


So many people are learning machine learning. What should you do to stand out?

There it was. Podcasts, YouTube, blog posts, machine learning here there changing this changing that changing it all.

I had to learn. I started. Andrew Ng’s Machine Learning course on Coursera. A bunch of blog posts. It was hard but I was hooked. I kept going. But I needed some structure. I put a few courses together in my own AI Masters Degree. I’m still working through it. It won’t finish. The learning never stops.


You know this. You’ve seen it happening. You’ve seen the blog posts, you’ve seen the Quora answers, you’ve seen the endless papers the papers which are hard to read the good ones which come explained well with code.

Everyone is learning machine learning.

Machine learning is learning everyone.

How do you stand out?

How how how.

A) Start with skills

The ones you know about, math, code, probability, statistics. All of these could take decades to learn well on their own. But decades is too long. Everyone is learning machine learning. You have to stand out from everyone.

There are courses for these things and courses are great. Courses are great to build a foundation.

Read the books, do the courses, structure what you’re learning.

This week I’m practising code for 30-minutes per day. 30-minutes. That’s what I have to do. When I don’t feel like practicing. I’ll remind myself. These are the skills I have to learn. It’ll be yes or no. It’s my responsibility. I’ll do it. Yes.

Why skills?

Because skills are non-negotiable. Every field requires skills. Machine learning is no different.

If you’re coming from zero, spend a few months getting into the practical work of one thing, math, code, statistics, something. My favourite is code, because it’s what the rest come back to.

If you’re already in the field, a few months, a fear years in, reaccess your skills, what needs improving? What are you good at? How could you become the best in the world at it? If you can’t become the best in the world, combine with something else you’re good at and become the best in the world at the crossover.

B) Got skills? Good. Show them.

Ignore this if you want.

Ignore it and only pay attention to the above. Only pay attention to getting really good at what you’re doing. If you’re the best in the world at what you do, it’s inevitable the world will find out.

What if you aren’t the best in the world yet?

Share your work.

You make a website.

I made this up. It might exist.

On your website you share what you’ve been up to. You write an article on an intuitive interpretation of gradient descent. There’s code there and there’s math there. You’ve been working on your skills so to give back you share what you’ve learned in a way others can understand.

The code tab links to your GitHub. On your GitHub you’ve got examples of different algorithms and comments around them and a tutorial on exploratory data analysis of a public health dataset since your interest in health. You’ve ingested a few recent papers and tried to apply it to something.

LinkedIn is your resume, you’ve listed your education, your contributions to different projects the porjects you’ve built the ones you’ve worked on. Every so often you share an update of your latest progress. This week I worked on adding in some new functions to my health project.

You’re getting a bit of traction but it’s time to step it up. You’re after the machine learning role at Airbnb. Their website is so well designed you stayed at their listings you’re a fan of what the work they do you know you could bring them value with your machine learning skills.

You make another website.

I made this one up too. Kudos if you’re already on it.

You send it to a few people on the Airbnb recruitment team you found on LinkedIn with a message.

Hi, my name is Charlie, I hope this finds you well.

I’ve seen the Machine Learning Engineer role on your careers page and I’d like to apply.

I made this website which shows my solutions to some of your current challenges.

If you check it out, I’d love your advice on what best to do next.

5/6 of the people you message click on it. This is where they see what you’ve done. You built a recommendation engine. It runs live in the browser. It uses your machine learning skills. Airbnb need a machine learning engineer who has experience with recommendation engines. They recommend a few things.

3 reply with next steps of what to do. The other 2 refer to other people.

How many other people sent through a website showcasing their skills?


Maybe you don’t want a job. Maybe you want to research. Maybe you want to get into a university. The same principles apply.

Get good at what you do. Really good.

Share your work.

How much?

80% skills.

20% sharing.


The “2 Year Self Apprenticeship”

My friend sent me this post. 

The ‘2 Year Self Apprenticeship’ model by @lewismocker

The ‘2 Year Self Apprenticeship’ model by @lewismocker

Reading it was a form of confirmation bias. It was as if I was reading what I’d been subconsciously (or consciously? How do you tell?) doing the past 2-years.

I’m in between step 4 and 5. 

It started with creating my own AI Masters Degree. That turned into a job as a machine learning engineer. And the creating hasn’t stopped. Publishing work online has opened more doors for more me than any of my previous ventures.

I haven’t figured out 6 yet. But it’ll come. In the meantime, I’ll keep making.

Enough about me. What can you take away from this?

The post already says enough. I won’t repeat any of it. But I can add a lesson or two.

A) Choosing yourself is hard but worth it

It’s not for everyone. The traditional paths are there for a reason. They’ve stood the test of time. They work for some but not for others. 

When I was younger I thought I’d be a TV star one day. My mum took me to an audition for an advertisement company. I was nervous but I liked being the centre of attention. After the audition we never heard back. Dreams shattered.

Then one day my mum found out the company went broke. I was 10. 10-year-olds don’t understand companies going broke. Why wasn’t I going to be a TV star?

Everywhere I went I felt like a combination of special and the one who didn’t fit in. I liked that. Maybe everyone feels it? Probably.

Aghh. Enough about me. That’s a 2 count. 

When you pick your own path, you’ll have people questioning what you’re doing. You’ll get advice from all angles.

But there will be something inside of you telling you to push forward. You can’t explain it. When you try to tell someone else, they might get it, they might not. All the advice they give comes from a kind place but they’re not in your head. They don’t have to lay in bed at night with your thoughts. They don’t have to sit down at lunchtime and stare out the window with the feeling in your gut of the thing that’s pulling you. 

Then you do it. You make the decision you’ve had sitting in your brain your body your soul. And it happens. The whole universe starts getting behind you. But it doesn’t make it any easier. You’ll keep coming up against obstacles keep questioning.

Is this the right thing? 

Will things work out?

Where’s the answer? 

Yes, maybe, no, it doesn’t exist, all valid answers. 

Choosing yourself is a daily practice. You make the decision. Then you follow up with the effort.

Then tomorrow happens. And you repeat. 

B) Online is great but people are better

The internet is amazing. It has lowered the barrier to entry to education, to creating, to making, to sharing, to meeting, to finding. You know this. But it’s not perfect. You know this too. 

You can learn from the best in the world and then remix their ideas with yours and share them. Others can find your work and learn from it and do the same. The snowball gets bigger. 

The one thing the technology hasn’t replicated yet is the feeling of connection. Online communities are everywhere but they’re not the same as sitting down at a table with like-minded people.

Someone messaged me the other day. ‘Hello Daniel, I’m a self-made XYZ as well.’

The message meant well and I thanked the person for the kind words. But I’m not self-made. There’s no such thing as self-made. 

This one is an asterisk on the end of the ‘2 Year Self Apprenticeship*’.

*Take advantage of the online resources available to you. But don’t forget about your offline relationships.

An offline relationship can be completely online but it takes more than the odd like to convey it. Interact with those who are in your circle. Message the people whose work you enjoy, share it and say why you like it. These kind of acts are what keep the snowball growing. 

Keep learning. Keep making. 




Slow learning, fast

‘Will this guarantee me a role in machine learning?’

‘If I do these courses, will I be job ready?’

‘Are these courses accepted by the industry?’

I responded to some emails this morning asking these questions.

My content may seem as though everything happens fast.

It’s wrong. What gets published is a fraction of what happens in real life.

Learning things takes time. A 10-minute video is often a highlight reel of a week’s worth of effort.

A single article may describe a month worth of different lessons.

One principle I try to follow is to always invest in my myself. Someone smarter than me told me you never go wrong investing in yourself.

When I get up in the morning and I don’t feel like working on the things I have to work on, I ask myself, ‘what’s the alternative?’ Most of the time I know the answer. The alternative to studying is not studying. And that won’t help me learn what I need to learn.

I have to remind myself learning is hard. It’s supposed to be hard. Hard is good. Hard is fun. If everything was too easy, we’d get bored.

Industry will have different opinions on what courses are valid. No role is ever guaranteed. And no course will ever 100% prepare you for a job. But that doesn’t mean all hope is lost.

You can keep building your skills. Skills are more important  than certificates.

You can move fast in the short-term but be patient in the long-term. This means working what you need to work on day in day out and letting it accumulate over time.

You can communicate your skills as a form of validation. Learned something? Prove it by showing your work. People love seeing what others have done. And the ones who don’t are not the ones you’re worried about anyway.

The only thing that’s guaranteed?

Learning never stops.

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.


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.


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



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.


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 curriculum

Do you have to use these?


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?


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?


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.

6 Tips to Keep Yourself From Getting Distracted Whilst Studying

Tomorrow happens.

I get up. I’m tired. I went to bed late. Distracted by my phone. Sophie was texting me.

Did she reply?

She didn’t. But there’s other red bubbles. More red bubbles. I tap one. Then another. I’ve been up for 46-minutes. I have no idea what I’ve done.

I decide that’s enough phone time for today. It goes in the drawer.


A) $1000/hour

If there was $1000 on the table, would you take it?

Yes of course, I would.

How about if someone else was at the table?

They turn to you.

Give me $1000.

Would you give to them?

I wouldn’t.

And if you wouldn’t either, why do you give away your time so easily?

Value your time at $1000 per hour.

“Would I pay $1000 per hour to do this?”

Study and education. Yes.

Random online internet surfing. No.

Seeing those people you knew 6-years ago buy a boat and go fishing with their new friends. No.

The list of things gets shorter real quick.


B) Keep the energy bar high

Tomorrow happens again.

My energy bar is already low.

Poor sleep and poor foods over the past couple of days have clogged everything.

My energy. Clogged.

My gut. Clogged.

My brain. Clogged.

How am I supposed to study whilst everything is clogged?

I decide I need to sleep more and eat better and begin at once.


C) What the hell am I doing?

A third tomorrow happens.

That’s three yesterdays with nothing. Three nothings.

But my energy bar is full today. My phone is away. I’m ready.

I sit down.

The void consumes me. There’s a pile of this and that and a stack of thoughts. I’m drowning in thoughts. Too much. What the hell am I doing?

I spend 17-minutes trying to decide. I figure it out. I put a few things down. Lost in thought but found in the words.

Math work.

Coding practice.


Yeah, that’s good. Three things, that’s enough.


D) Time it

Math work got done but none of the others.

I’m starting to get this starting to get the hang of things.

Math work was good. My energy bar was high. Top sleep and food does the trick.

It got me. Got me good. The math. I got lost. Lost in the patterns. I was on a roll. Connecting the dots. Playing.

I lost track of time. These things happen when you’re playing.

Next time I must put a time limit on each one. Not too much. But not too little I can’t do what I need to do.


E) Playing

I set up a timer. I want these things done. I’m getting better.

A full energy bar.

Valued time. $1000 per hour!

A list of things to do.

Look at me go!

The timer is running. 25-minutes of playing.

I learned it from the math study.

Work is playing. Studying is playing. I’ve convinced myself. They’re the same. It helps you know. It does.

I think if I can learn this and then that, I’m in a game, I can turn it into a game. Study becomes play.


F) Enough

All this time playing I finish exhausted. All of the list done. A depleted energy bar. Thousands of dollars of time and effort dedicated.

I lay in bed. Still intrigued. The best way to be. But I know I must rest. The sleep will help. Help me focus. Keep my energy bar up.

I can it enough. When I’ve reached it, I call it. I’ve done a good job today. There’s more to do though. There’s always more to do.

My list of what to do.

My timer to help with the list. $1000 per hour.

My full energy bar. The sleep and food. And the bending exercises.

My phone away.

These things will help.

I lay in bed. Still intrigued.

Tomorrow happens.


"How do you stay motivated whilst studying?" — Ask a Machine Learning Engineer Anything

Every month, I host a livestream on my channel where I answer some of the most common questions I get, plus as many of the live questions as I can.

"How can I get a job in machine learning?”

“Where’s the best place to learn machine learning?”

“How do you manage your time?”

“How do you stay fit whilst studying?”

“What do you think of Coursera, EdX, Udacity and Udemy?”

“Should I go to university to study data science?”

Read More

How I study five days a week

I had no job.

Then I started driving Uber on the weekends to pay for my studies.

I loved meeting new people but I hated driving a car all the time. Traffic, stop, start, fuel, the air, the aircon, all of it.

I studied machine learning. All day, five days a week. And it was hard. It's still hard.

9-months in, I got a job.

It's the best job I've ever had.

A) Fix your environment

Your grandfather’s first orange farm failed.

The soil was good. The seeds were there. All the equipment too. What happened?

It was too cold. Oranges need warm temperatures to grow.

Your grandfather had the skills to grow oranges but there was no chance they were growing in a cold climate.

When he moved to a warmer city, he started another orange farm.

12-months later, your grandfather was serving the best orange juice in town.

Studying is like growing oranges.

You could have a laptop, an internet connection, the best books and still not be motivated to study.


Because your environment is off.

Your room is filled with distractions.

You try to study with friends but they aren’t as dedicated as you.

Whatsapp goes off every 7-minutes.

What can you do?

Clean your room. Find a different study group. Friends are great when it comes to friend time but study time is study time. Put your phone in a drawer for an hour.

Fix your environment and let the knowledge juices flow.

B) Set the system up so you always win

Problem 13 has you stumped. You’re stuck.

You wanted to get it done yesterday but couldn’t.

Now it’s time to study but you know how hard you worked yesterday and got nowhere.

You’re putting it off.

You know you should be doing it.

But you’re putting it off.

It’s a cycle.


The pile of books stares at you. Problem 13.

You set a timer. 25-minutes.

You know you might not solve the problem but you can sit down for 25-minutes and try.

4-minutes in, it’s hell. Burning hell. But you keep going.

24-minutes in and you don’t want to stop.

The timer goes off and you set another. And then another. After 3 sessions, you solve the problem.

You can't always control whether you make progress with study. But you can control how much time you spend on something.

Can control: four 25-minute sessions per day.

Can't control: finishing every task you start every day.

Set the system up so you always win.

C) Sometimes do nothing

I did the Coursera Learning How to Learn course the other day.

One of the main topics was focused versus diffused thinking.

Focused thinking happens when you're doing a single task.

Diffused thinking happens when you're not thinking about anything in particular.

The best learning happens at the crossover of these two.

It's why you have some of your best thoughts in the shower. Because there's nothing else happening.

When you let diffused thinking takeover, it gives your brain space to tie together all of the things it absorbed during focused thinking.

The catch is, for it to work properly, you need time in both.

If you've set the system up so you do four 25-minute sessions of focused work, go for a walk after. Have a nap. Sit and think about what you've learned.

The world could do with more of nothing.

D) Embrace the suck

Studying sucks.

You learn one thing and forget it the next day.

Then another and forget it.



You spend the whole weekend studying, go to work on Monday and no one knows.

Then after a year of studying something you realise how much more there is to still learn.

When will it end?

It doesn't. It's always day one.

Embrace the suck.

E) The 3-year-old principle

I was at the park the other day.

There was a young boy running around having the time of his life.

Up the slide, down the slide, in the tree, out of the tree, in the dirt, out of the dirt, up the hill, down the hill.

He was laughing and jumping then laughing again.

His mum came over to pick him up.

"Come on, Charlie, we've got to go."

He kept laughing as she carried him away, waving his blue plastic shovel.

What is it that fascinated him?

He was playing. He was having fun. The whole world was new.

Our culture has a strict divide between work and play.

Study is seen as work.

You're supposed to study to get more work. You're supposed to work to earn money. The money buys you leisure time. Then and only then can you be like Charlie and run around laughing.

If you have it in your head study is work, it will be hell.

But suppose, you have the idea about it that studying is the process of going through one topic and then to the next.

Connecting different things like a game.

The same feeling about it as you might have as if you were Charlie going down the slide.

You learn one thing, you use it to learn something else, you get stuck, you get over it, you learn another thing. And you make a dance out of it.

Do this and you'll finish a study session with more energy than you started.

This is the 3-year-old principle. Seeing everything as play.

That's enough for now.

It's bedtime.

That's a bonus.

F) Sleep

Poor sleep means poor studying.

Don't trade sleep for more study time. Do the opposite.


What to study

Is far more important than where to study. 

How you learn is more important than how long.

The best teachers are the ones which inspire you to learn more.

The best books are the ones which you don’t want to stop reading.

The internet has provided access to some of the greatest teachers and learning materials.

Now you have the choice of who your teacher is and what you read. 

If you try one and don’t like it, you can move on.  There will be more information on the topic somewhere else.

“Education is becoming more and more accessible. What’s scarce is a willingness to learn.” — Naval

And a willingness to learn comes with a willingness to be wrong.

'That won't be me'

‘30% of you are going to fail this course.’

‘Look to the person to your left and the person to your right.’

‘One of you isn’t going to pass.’

I looked to the left and to the right.

The people sitting next to me did the same.

‘That won’t be me,’ I thought.

It was me.

The professor wasn’t finished.

‘What you learn now will be different in 5-years.’

We were in a biology class.

7-years later a new textbook came out with a bunch of updates.

He was right. Twice.

Not only did I fail the course, I failed a course which (as of now) was teaching the wrong information.

If the beard is grey, trust what they say.

17-year-old me didn’t respect the grey beard.


Courses are great

Blog posts are great.

Many of the resources you find online for data science and machine learning are great.

After what I've been looking up the past few months, I've struggled to find something bad. Only things which were slightly outside of the scope I was after.

I get asked a lot what the best place to learn is.

I can't answer it.


Because I haven't tried everywhere.

I can only speak of what I've tried.

But you could choose anything. Follow it. Put the effort in. Build off it. Then repeat.

And you'll always win.


Because investing in companies and businesses may make you money.

But investing in yourself always pays off.

An In-depth Review of Coursera’s Learning How to Learn Course

If you could have any super power, what would it be?



Super strength?

When I was younger my answer was always ‘I’d have the superpower to use any superpower.’ The equivalent of wishing for more wishes when a genie appears.

Now if you asked me again, I’d tweak my answer.

‘I’d have the superpower to learn anything.’

Similar but not as much of a cop-out.

But why bother having to learn something if you had the choice for any superpower, why not go straight to knowing everything?

Because learning is the fun part. Knowing everything already would be a boring life. Have you ever seen how a 4-year-old walks into a park? Now compare that to a 40-year-old.

If you can learn how to learn, you can apply it to any other skill. Want to learn programming? Apply your ability to learn. Want to learn Chinese? Apply your ability to learn.

To improve my ability to learn, I went through the Learning How to Learn Course on Coursera. It’s one of the most popular online courses of all time. And I can see why.

It’s taught in a simple and easy to understand manner. The concepts are applicable immediately. Even throughout the course, as you learn something in part 1, it resurfaces in part 2, 3 and 4, reinforcing the new information.

If you’ve seen the course before and are thinking about signing up, close this article and do it now, it’s worth it.

Otherwise, keep reading for a non-exhaustive list of some of my favourite takeaways.

Part 1 — What is learning?

Focused and Diffused thinking

How do you answer such a question? What is learning?

The course breaks it down with the combination of two kinds of thinking, focused and diffused.

Focused thinking involves working on a singular task. For example, reading this post. You’re devoting concentration to figuring out what the words on the screen mean.

Diffused thinking happens when you’re not focused on anything. You’ve probably experienced this on a walk through nature or when you’re laying in bed about to go to sleep. No thoughts but at the same time, all of the thoughts.

Learning happens at the crossover of these two kinds of thinking.

You do focus on something intently for a period of time and then take a break and let your thoughts diffuse.

If thinking was a flashlight, focused thinking would be a small beam of intense light and diffused thinking would be a wider beam of less intense light.

If thinking was a flashlight, focused thinking would be a small beam of intense light and diffused thinking would be a wider beam of less intense light.

How often have you been stuck on a problem and then thought of answer in the shower?

This is an example of the two kinds of thinking at work. It doesn’t always happen consciously either.

Have you ever not known what step to take next on an assignment, left it overnight, come back and saw the solution immediately?

If you want to learn something, it’s important to take advantage of the two kinds of thinking. Combine intense periods of focus with intense periods of nothing.

After studying, take a walk, have a nap or sit around and do nothing. And don’t feel bad for it. You’re giving yourself the best opportunity to let the diffused mind do its thing.

Procrastination and how to combat it

You’re working a problem. You reach a difficult point. It’s uncomfortable to keep going so you start to feel unhappy. To fix this feeling, you seek something pleasurable. You open another tab, Facebook. You see the red notifications and click them. It feels good to have someone connecting with you. You see Sarah has invited you to her birthday. You look at the invites list, Gary is there. Gary is there. You’re not the biggest fan of Gary. Back in middle school he was a prick. ‘Hey bro!’ he’d yell as he smacked you on the back. Hard. A bit more scrolling. Some advertisement for new shoes. The same ones you saw yesterday. They look real nice, black with orange. ‘I’m going to order them,’ you think.

You look down at your notes. You forget where you were up to.

What the hell just happened?

You were working on a problem. And you reached a difficult point. It was uncomfortable and you started to feel unhappy. You sought out something pleasurable. It worked. But only temporarily. Now you’re back to where you were and you’re even more upset at the fact you got distracted.

So how do you fix it?

The course suggests to use the Pomodoro technique. I can vouch for this one.

It’s simple.

You set a timer and do nothing except what you wanted to work on for the duration. Even if it gets difficult, you keep working on it until the timer is done.

A typical Pomodoro timer is 25-minutes with a 5-minute break afterwards. During the break you can do whatever you want before starting another 25-minute session.

But you can use any combination of working/break time.

For example, to complete this course, I used 1-hour Pomodoro timers with a 15-30 minute break in between.

  • 1-hour studying.

  • 15-minute break.

  • 1-hour studying.

  • 15-minute break.

  • 1-hour studying.

  • 30-minute break.

  • 1-hour studying.

If 25-minutes is too long, start with 10 and make your way up.

Why is doing this valuable?

Because you can’t control whether or not you solve every problem which arises before the end of a days work.

But you can control how much time and effort you put in.

Can control: 4-hours of focused work in a day.

Can’t control: solving every assignment question in a day.

Sleep your way to better learning

We’ve talked about focused and diffused thinking. Sleep is perhaps the best way to engage unconscious diffused thinking.

Being in focused mode is to brain cells what lifting weights is to muscles. You’re breaking them down.

Sleep provides an opportunity for them to repaired and for new connections to be formed.

Dr. Terrence Sejnowski talks about how new synapses (connections) are formed on the dendrites during sleep.

Dr. Terrence Sejnowski talks about how new synapses (connections) are formed on the dendrites during sleep.

There’s a reason famous thinkers like Einstein and Salvatore Dali would sleep for 10-hours at a time and tap multiple naps a day.

They knew it was vital for their brain to clear out toxins built up during the day which prevented engaging the focused mind.

The next time you feel like pulling an all-nighter and studying, you’d probably be better off getting a good night sleep and resuming the next day.

Spaced repetition, a little every day

Jerry Seinfeld writes jokes every day. He has a calendar on his wall and every day he writes jokes, he marks an X on it. Because he writes them every day if you looked the calendar you’d see a chain of X’s.

Once the chain has started, all he has to do is keep it going.

‘Don’t break the chain.’

This technique is not only good for writing jokes. It can be used for learning too.

In the Learning How to Learn Course, they refer to a similar concept called spaced repetition.

Spaced repetition involves practicing something in small timeframes and as you get better at it, increasing the amount of time between each timeframe.

For example, when starting to learn Chinese, you might practice a single word every day for a week until you’re good at it. Then after the first week, you practice it twice a week. Then twice a month. Then once every two months. Eventually, it will be cemented in your mind.

The best learning happens when you combine these two concepts. Don’t break the chain by practicing a little every day and incorporate spaced repetition by going over the difficult stuff more often.

To begin with, you could set yourself a goal of one Pomodoro a day on a given topic. After a week, you would’ve spent 3-hours on it. And after a year, you’ll have amassed over 150-hours. Not bad for only 25-minutes per day.

Bonus: A great tool for spaced repetition is the flash card software Anki and for not breaking the chain, I recommend the Don’t Break the Chain poster from the writers store.

Part 2 — Chunking

Part 2 of the course introduces chunking. Multiple neurons firing together are considered a chunk. And chunking is the process of calling upon these regions in a way which they work together.

Why is this helpful?

Because when multiple chunks of neurons fire together, the brain can work more efficiently.

How do you form a chunk?

A chunk is formed by first grasping an understanding of a major concept and then figuring out where to use it.

For example, if you were starting to learn programming, it would be unwise to try and learn an entire language off by heart. Instead, you might start with a single concept, let’s say loops. You don’t need to understand the language inside and out to know where to use loops. Instead, when you come across a problem which requires a loop, you can call upon the loops chunk in your brain and fill in the other pieces of the puzzle as you need.

Deliberate practice: do the hard thing

Forming a chunk is hard. First of all, what are the important concepts to learn? Second, where should you apply them?

This is exactly why you should spend time and effort trying to create them. Instead of learning every intricate detail, seek out what the major concepts are. Figure out how to apply them by testing yourself. Work through example problems.

The process of doing the hard thing is called deliberate practice. Spending more time on the things you find more difficult is how an average mind becomes a great mind.

Einstellung — don’t be held back by old thoughts

Dr. Barbara Oakley introduces Einstellung as a German word for mindset.

But the meaning is deeper than a single word translation.

Every year you upgrade your smartphone’s software. A whole set of new features arrive along with several performance improvements.

When was the last time your way of thinking had a software upgrade?

Einstellung’s deeper meaning describes an older way of thinking holding back a newer, better way of thinking.

The danger of becoming an expert in something is losing the ability to think like an amateur. You get so good at the way that’s always worked, you become blind to the new.

If you’re learning something new, especially if it’s the first time in a while, it’s important to be mindful of Einstellung. Have an open mind and don’t be afraid of asking the stupid questions. After all, the only stupid question is the one that doesn’t get asked.

Recall — what did you just learn?

Out of what you’ve read so far, what has stood out the most?

Don’t scroll back up. Put the article down and think for a second.

How would you describe it to someone else?

It doesn’t have to be perfect, do it in your own words.

Doing this is called recall. Bringing the information you’ve just learned back to your mind without looking back at it.

You can do it with any topic. Reading a book? When you’re finished, put it down and describe your favourite parts in a few sentences.

Finished an online course? Write an article about your favourite topics without going back through the course. Sound familiar?

Practicing recall is valuable because it avoids the illusion of competence. Rereading the same thing over and over again can give you an illusion of understanding it. But recalling it and reproducing the information in your own words is a way to figure out which parts you know and which parts you don’t.

Part 3 — Procrastination & Memory

The Habit Zombie

How hard do you have to think about making coffee in the morning at your house?

No very much. So little, your half asleep zombie mode body can fumble around in the kitchen with boiling water and still manage to not get burned.


Because you’ve done it enough it’s become a habit. It’s the same with getting dressed or brushing your teeth. These things you can do on autopilot.

Where do habits come into to learning?

The thing about habits is that almost anything can be turned into a habit. Including procrastination.

Above we talked about combating procrastination with a timer. But how do you approach from a habit standpoint?

Part 3 of the Learning How to Learn course breaks habits into four parts.

  1. The cue — an event which triggers the next three steps. We’ll use the example of your phone going off.

  2. The routine — what happens when you’re triggered by the cue. In the phone example, you check your phone.

  3. The reward — the good feeling you get for following the routine. Checking your phone, you see the message from a friend, this feels good.

  4. The belief — the thoughts which reinforce the habit. You realise you checked your phone, now you think to yourself, ‘I’m a person who easily gets distracted.’

How could you fix this?

You only have to remove one of the four steps for the rest to crumble.

Can you figure out what it is?

The cue.

What would happen if your phone was in another room? Or turned off?

The cue would never happen, neither would the subsequent steps.

The technique of removing the cue doesn’t only work for procrastination. It can work for other habits too. It also works in reverse. If you want to create a good habit. Consider the four steps.

To make a good habit, create a cue, make a routine around it, give yourself a reward if you follow through and you’ll start forming a belief about you being the type of person who has the good habit.

The dictionary isn’t the only place product comes after process

Thinking about the outcome of your learning is the quickest way to get discouraged about it.


Because there is no end. Learning is a lifelong journey.

No one in history has ever said, ‘I’ve learned enough.’

And if they have, they were lying.

I’ve been speaking English since I was young. Even after 25-years of speech, I still make mistakes, daily. But would getting upset at where my level of English is at be helpful? No.

What could I do?

I could accept that knowing everything about speech and the English language is impossible. And instead, focus on the process of speaking.

This principle can be related to anything you’re trying to learn or create.

If you want to get better at writing, the end product could be a bestselling book. But if I told you to go and write a bestselling book, what would you write?

Worrying about what a bestselling book would have in it would consume you. It’s far more useful to focus on the process, to write something every day.

Free up your working memory and set a task list the night before

Dr. Oakley says we’ve got space for about four things in working memory. But if you’re like me, it’s probably closer to one.

Some of the people I work with have three monitors with things happening on all of them, I’m not sure how they do it. I stick to one and push it to two when a task requires it.

If I had a third monitor it would be the A5 notepad I carry around everywhere. It’s my personal assistant. Every morning, I write down a list of half a dozen or so things I want to get done during the day. Sometimes I write the same list on the whiteboard in my room to really clear out my brain.

Even when I’m in the middle of a focused session, 12 minutes into a Pomodoro, things still come out of no where. Rather than stop the Pomodoro, the thought gets trapped on the paper. Working memory free'd up.

The course recommends creating a list of things the night before but I’m fan of first thing in the morning as well. Putting things down means they’re out of your head and you can devote all of your brain to power to focused thinking rather than worrying about what it was you had to do later.

Don’t forget to add a finishing time. The time of day you’ll call it quits.


Because having a cutoff time means you’ve got a set timeframe to complete the tasks in. A set timeframe creates another reason to avoid procrastination.

And having a cutoff time for focused work means you’ll be giving your brain time to switch to diffused thinking. Who knows. That problem you couldn’t solve at 4:37 may solve itself whilst you’re in the shower at 8:13.

You’ve only got a limited number of slots in your working memory. If using a task list helps to free up one of the slots, it’s worth it.

You’ve only got a limited number of slots in your working memory. If using a task list helps to free up one of the slots, it’s worth it.

Part 4 — Renaissance learning & unlocking your potential

Learning doesn’t happen in a straight line

You could study all weekend and go back to work on Monday and no one would know.

You could work on a problem all week and by the end of the week feel like you’re worse off than you started.

Dr. Sejnowski knows this. And emphasises learning doesn’t happen linearly.

Learning tough skills doesn’t happen over the course of days or weeks or months. Years is the right timeframe for most things.

I wanted to know how quickly how quickly I could learn all of the math concepts behind machine learning: calculus, linear algebra, probability. I found a question on Quora about it.

A person who had been studying machine learning for 30-years replied with, ‘Decades.’ And then explained how he was still discovering new things.

I was impatient. I was focusing on product rather than process.

Learning looks more like a broken staircase than a straight line.

Learning looks more like a broken staircase than a straight line.

Charles Darwin was a college dropout

How do you go from medical school dropout to discovering the theory of evolution?

Easy. You be Charles Darwin.

But what if you’re not Charles Darwin?

Not to worry, not everyone is Charles Darwin.

There will never be another Charles Darwin. History repeats, but never perfectly. It’s better to think of it as a rhyme. History rhymes with the future.

Charles Darwin dropped out of medical school much to the disappointment of his family of physicians. He then travelled the world as a naturist.

Years went on but one thing never changed, he kept looking at things in nature with a child-like mind.

He’d take walks multiple times per day in between periods of study. Focused mind and diffused mind.

Take any introduction to biology course and you’ll know what happened next.

I’ve condensed a story of decades into sentences but the point is everyone has to start somewhere.

The first year you learn something new you suck. The second year you suck even more because you realise how much you don’t know.

There’s no need to envy those who seem to know what they’re doing. Every genius starts somewhere.

‘He who says he can and he who says he can’t are usually right’

The course credits Henry Ford as saying the above. But the internet is telling me it’s from Confucius.

It doesn’t matter. Whoever said it was also right.

If you believe you can’t learn something, you’re right.

If you believe you can learn something, you’re right.

Dr. Oakley was a linguist during her twenties. She didn’t like math. So how did she become an engineering professor?

By changing her belief in what she could learn.

I used to tell myself, ‘I’ll never be the best engineer.’

I said it to someone at a meetup. They replied, ‘not with that attitude.’ A simple yet profound statement. All the best ones are.

Whatever it is you decide to learn, it all starts with the story you tell yourself. Pretend you’re the hero in the story of your own learning journey. Challenges will arise. It’s inevitable. But how does the hero deal with them? You decide.

Learning: the ultimate meta-skill

Taking responsibility for your learning is one of the most important undertakings you can manage.

Learning is the ultimate meta-skill as it can be applied to any other skill. So if you want to improve your ability to do anything, learning how to learn is something you should dedicate time to.

The things I’ve mentioned in this article are only scratching the surface of what’s available in the Learning How to Learn course. I’ve left out exercise, learning with others, studying in different locations. But you can imagine the benefits these have.

If you want to dig deeper, I highly recommend checking out the full Learning How to Learn course. I paid for the certificate but it’s not needed. You can access all the materials for free. However, I find I take things more serious when I pay for them. And the information in this course is worth paying for.

“If you’re not embarrassed by who you were last year, you’re not learning enough.” - Alain de Botton

Keep learning.

PS there’s a video version of this article available on my channel. I’m a visual learner so I like to watch things and read them if I can.


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.


How to write a good resume

I haven't had to update my resume for a while. When I started at Max Kelsen, I never sent in a resume.

And when I applied for a teaching role at DataCamp, instead of attaching a resume form, I typed a few sentences about my recent and relevant experiences.

I've never been a fan of resumes. I found them hard to write and always wanted to put more than was necessary.

And more importantly, an A4 sheet of paper is hardly the best way to evaluate someone's abilities.

But for some roles, like the one my brother is applying for, they're required.

So how do you make a good one?

1. Keep it short

No more than one page. Respect the time of the person who's going to be reading it. Anything more than a page is overkill.

2. Tailor it for the role

If you've got plenty of experience, cut out what's not related to the role you're applying for. Keep it short.

If you don't have much experience, list other projects you've worked on.

If you haven't worked on other projects, start working on some other projects or be honest about where you're at in a custom cover letter.

'I don't have any completed projects as of yet but am applying to this role to show my interest.'

'Over the next X months, I'll be working on Z project. Once I've completed it, I'll report back with my progress.'

3. Be honest

This is obvious. Don't list anything you wouldn't be able to talk about in length during an interview.

If you haven't got the relevant experience, address it, address how you would handle it, address what you're going to do about it (see the bottom of 2).

4. Be specific

Not good: Worked with customers every day.

Good: Served an average of 3000 customers per quarter with an 88 NPS score.

Not good: Worked on data science projects.

Good: Built a data science pipeline which saved a clients business an average of $10,000 per month in 6-weeks.

What have you worked on?

How much?

How many?

How often?

Include the details. 2-3 dot points per major experience.

5. What are you interested in?

Some are on the fence about this. But I'm for it. You can make your own decision.

Add a little human to it.

What's been getting you excited lately?

What's something non-role related you've been enjoying?

After all, if you're successful, there's a chance you will have to spend time with the people who are reading your resume.

Give to a reason to want to know more. This could be under a hobbies/interest section. Or in a custom cover letter.

6. No spelling mistakes. Ever.

Modern resume filters will discard anything with a mistake.

When you think it's ready to send off, read it again. Show it to a friend to read. Read it out loud. Does it make sense?

Use a tool like Grammarly to make sure your words are spelled correctly and are in order.

7. Have a little more

'Where can I go to see more?'

Have a presence online. A website, a blog, a portfolio, a GitHub account, a LinkedIn, a place to see that project you've worked on. You don't need them all but at least 2 would be good.

I don't plan on ever having to send another resume to someone, I've got a full LinkedIn for that. Or if they want to know a little more, I'm not hard to find online.

In a resume filtering world, having the little bit extra is what will set you apart.

My resume is below. You can copy it if you like. It's out of date but it hits the points above.

If you're looking for a guide on how to fill a LinkedIn profile, you can copy mine too. It's not the best but it's full of information.

Looking for a place to make a resume? If I had to do mine again, I'd go to, I haven't used it but it looks slick. Otherwise, there's a free template on Google Docs, Josh an I used that to make his.

All the best with the next application!

My resume for April 2018.

My resume for April 2018.