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.

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.

Reading.

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.

Source: https://www.quora.com/What-are-some-ways-t...

"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.

Why?

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.

Aghhhhhhh.

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.

Another.

Forgotten.

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.

Source: https://qr.ae/TUfk19

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.

Source: https://qr.ae/TUrDLv

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.

Why?

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.

Why?

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?

Flying?

Invisibility?

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.

How?

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.

Why?

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.

Why?

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.


Source: https://link.medium.com/nB4U2kbs3T

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.

‘Pipettes.’

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.

‘Biomed.’

‘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 deeplearning.ai, the Udacity AI Nanodegree, fast.ai 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.

‘Definitely.’

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.

Source: https://qr.ae/TUvTBk

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 resume.io, 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.

The Five C's of Online Learning

This post originally appeared on Quora as my answer to 'Udacity or Coursera for AI machine learning and data science courses?'

P1000829.jpg

Tea or coffee?

Burger or sandwich?

Rain or sunshine?

Pushups or pull-ups?

Can you see the pattern?

Similar but different. It’s the same with Udacity and Coursera.

I used both of them for my self-created AI Masters Degree. And they both offer incredibly high-quality content.

The short answer: both.

Keep scrolling for a longer version.

Let’s go through the five C’s of online learning.

If you’ve seen my work, you know I’m a big fan of digging your own path and online platforms like Udacity and Coursera are the perfect shovel. But doing this right requires thought around five pillars.


Curiosity

When you imagine the best version of yourself 3–5 years in the future, what are they doing?

Does it align with what’s being offered by Udacity or Coursera?

Is the future you a machine learning engineer at a technology company?

Or have you decided to take the leap on your latest idea and go full startup mode?

It doesn’t matter what the goal is. All of them are valid. Mine is different to yours and yours will be different to the other students in your cohort.

The important part is an insatiable curiosity. In Japanese, this curiosity is referred to as ikigai or your reason for getting up in the morning.

Day to day, you won’t be bounding out of bed running to the laptop to get into the latest class or complete the assignment you’re stuck on.

There will be days where everything else except studying seems like a better option.

Don’t beat yourself up over it. It happens. Take a break. Rest.

Even with all the drive in the world, you still need gas.


Contrast

Sam was telling me about a book he read over the holidays.

‘There were some things I agreed with but some things I didn’t.’

My insatiable curiosity kicked in.

‘What did you disagree with?’

I was more interested in that. He said it was a good book. What were the things he didn’t like?

Why didn’t he like those things?

The contrast is where you learn the most.

When someone agrees with you, you don’t have to back up your argument. You don’t have to explain why.

But have you ever heard two smart people argue?

I want to hear more of those conversations.

When two smart people argue, you’ve got an opportunity to learn the most.

If they're both smart, why do they disagree?

What are their reasons for disagreeing?

Take this philosophy and apply it to learning online through Udacity or Coursera.

If they’re like tea and coffee, where's the difference?

When I did the Deep Learning Nanodegree on Udacity, I felt like I had a wide (but shallow) introduction to deep learning.

Then when I did Andrew Ng’s deeplearning.ai after, I could feel the knowledge compounding.

Andrew Ng’s teachings didn’t disagree with Udacity’s, they offered a different point of view.

The value is in the contrast.


Content

Both partner with world-leading organisations.

Both have world class quality teachers.

Both have state of the art learning platforms.

When it comes to content, you won’t be disappointed by either.

I’ve done multiple courses on both platforms and I rate them among the best courses I’ve ever done. And I went to university for 5-years.

Udacity Nanodegrees tend to go for longer than Coursera.

For example, the Artificial Intelligence Nanodegree is two terms both about 3–4 months long.

Whereas Coursera Specializations (although at times a similar length), you can dip in and out of.

For example, complete part 1 of a Specialization, take a break and return to the next part when you’re ready. I’m doing this for the Applied Data Science with Python Specialization.

If content is at the top of your decision-making criteria, make a plan of what it is you hope to learn. Then experiment with each of the platforms to see which better suits your learning style.


Cost

Udacity has a pay upfront pricing model.

Coursera has a month-to-month pricing model.

There have been times I completed an entire Specialization on Coursera within the first month of signing up, hence only paying for one month.

Whereas, all the Udacity Nanodegree’s I’ve done, I’ve paid the total up front and finished on (or after) the deadline.

This could be Parkinson’s Law at play: things take up as much time as you allow them.

Both platforms offer scholarships as well as financial support services, however, I haven’t had any experience with these.

I drove Uber on weekends for a year to pay for my studies.

I’m a big believer in paying for things.

Especially education.

When I pay for something, I take it more seriously.

Paying for something is a way of saying to yourself, I’m investing my money (and time spent earning it), I better invest my time into too.

All the courses I’ve completed on both platforms have been worth more than the money I spent on them.


Continuation

You’ve decided on a learning platform.

You’ve decided on a course.

You work through it.

You enjoy it.

Now what do you do?

Do you start the next course?

Do you start applying for jobs?

Does the platform offer any help with getting into the industry?

Udacity has a service which partners students who have completed a Nanodegree with a careers counsellor to help you get a role.

I’ve never got a chance to use this because I was hired through LinkedIn.

What can you do?

Don’t be focused on completing all the courses.

Completing courses is the same as completing tasks. Rewarding. But more tasks don’t necessarily move the needle.

Focus on learning skills.

Once you’ve learned some skills. Practice communicating those skills.

How?

Share your work.

Have a nice GitHub repository with things you’ve built. Stack out your LinkedIn profile. Build a website where people can find you. Talk to people in your industry and ask for their advice.

Why?

Because a few digital certificates isn’t a reason to hire someone.

Done all that?

Good. Now remember, the learning never stops. There is no finish line.

This isn’t scary. It’s exciting.

You stop learning when your heart stops beating.


Let’s wrap it up

Both platforms offer some of the highest quality education available.

And I plan on continuing to use them both to learn machine learning, data science and many other things.

But if you can online choose one, remember the five C’s.

  1. Curiosity — Stay curious. Remember it when learning gets tough.

  2. Contrast — Remix different learning resources. All the value in life is at the combination of great things.

  3. Content — What content matches your curiosity? Follow that.

  4. Cost — Cost restrictions are real. But when used right, your education is worth it.

  5. Continuation — Learn skills, apply them, share them, repeat.

More

I’ve written and made videos about these topics in the past. You might find some of the resources below valuable.

Source: https://qr.ae/TUnFZB

When does it all start to make sense?

I read a post on LinkedIn the other day which talked about how someone had been coding Python for 10-years but still looks up some basic functions every day.

I've been a machine learning engineer for 8-months and I do the same.

If you could see my Stack Overflow history, you’d find a bunch of things which you'd expect to find in the first chapter of a book on Pandas.

It's my own fault. I could take the time and learn all the functions off by heart. Then I wouldn't have to look them up every time.

But what happens when they change? Or if the library gets updated?

It's hard to change a way of doing things if it’s the way you've always done it. So learning a programming language off by heart may be helpful but it could lead to problems down the road.

I'm a fan of learning what you need to learn when you need to learn it and not being deterred by your previous learnings.

This kind of firey curiosity is extinguished in school. Instead of crossing knowledge roadblocks when they come, the curriculum tries to prepare you for every single one.

What's more important than knowing what to do in every situation is knowing how to figure out what to do. Knowing how to ask questions, knowing how (and being willing) to be wrong.

So when does it all start to make sense?

Someone asked me this the other day. They had been coding Python and working on a few projects but still running into a few struggle points.

I replied back with my experience of things still not making sense at times and an answer similar to the above.

I prefer things not to make sense every so often. If everything made sense, the world would be a pretty boring place.

I'm not a fan of boring. And I know you aren't either.

Creating and deploying data science/machine learning pipelines on the cloud still doesn’t make sense to me. But I'm getting there. The Data Engineering on Google Cloud Specialization on Coursera has been helping. Part 4 was all about streaming data. I talk about it more in my latest video.

Some thoughts on university versus learning online for data science

Zac emailed me asking a question.

Keep on working and keep looking for new opportunities in the field…
OR 
Go back to uni and finish the last 18 months of my degree.

He just finished an internship and has about 18-months left at university before he finishes his computer science degree.

It’s a tough choice.

I sat and thought about it for a while. Then replied to the email with some unedited thoughts.

And I’m sharing them here, also unedited. Bear in mind, I’ve never been to university to study computer science.

Zac,

Here’s how I see it, I’m gonna write a few thoughts out loud.

Where do you want to be/see yourself in 3-5 years? 

It sounds like you’re pretty switched on to where your skillset lies (aka, teaching yourself, working on things which interest you).

Might be worth having a think about which one better suits the ideal version of you in 3-5 years.

Does that ideal version of you require a university degree? Or could that version of you get by without one?

Which one is the most uncomfortable in the short term?

I’m very long term focused (I have to remind myself of this every day). So whenever I come up to a hard decision, I ask myself, ‘Which one is hardest in the short term?’

I treat short term as anything under 2-3 years (the starting era of the ideal version of yourself).

18-months isn’t really the longest time

How much of a rush are you in?

Could you stick out the 18-months, share your work online through an online portfolio, upskill yourself through various other courses (and jump ahead of others) and come out with a degree AND some extra skills.

Get after it

This is countering the above point.

If you think you have the balls to chase after it (sounds like you already do), why do you need university to be a gatekeeper?

Sure, not having an official degree may shut you off from some companies, but to me, a piece a paper never really meant much. Especially when the best quality materials in world are available online.

I have a colleague doing a data science masters at UQ and he said he has learned way more since working with Max Kelsen than at university.

Put it this way, I was driving Uber this time last year. But I followed through with my curriculum, shared my work online and got found by an awesome company.

Share your work

Whichever path you choose, I can’t emphasis this enough. Make sure people can find you online.

If you’re not going to get a degree. Be the person who’s name comes up on others LinkedIn feeds for data science posts. Have some good Medium articles, share what you’ve been doing.

It’ll feel weird in the start. Trust me. But then you’ll realise the potential of it.

All of sudden, you can become an expert in your field by being the one to communicate the skills you’re learning.

How did I do?

What would you do in Zac’s situation? Learn online and look for more work experience? Or stick out the 18-months of computer science?

'What's the one thing you'd do over again?'

I get asked often what's the one machine learning course I'd take over again.

'What's the one fitness habit you'd do again?'

'What’s the one thing I can do….?'

There is no one thing.

Even if there was, there would be no point doing it because everyone else would be doing it.

You're capable of making the change you want to see in the world.

Whatever it is. Bettering yourself, learning a new skill, travelling to that place.

The only real gatekeeper is the one in your head. The one who decides to listen to the naysayers.

But the catch is, you're also your biggest naysayer.

Now you know this. You don't have to be. Not anymore.

The one thing I'd do over again is starting sooner. Starting my own learning journey. Starting my own creative studio. Starting to share my work. All sooner.