You can learn and apply machine learning

Hi, machine learning is not just for the academic elite.

Machine learning techniques are just another bag of tools that you can draw upon to solve specific complex problems.

Just like you can use sorting algorithms in libraries to order items in a list, you can use machine learning algorithms to develop predictive models and make predictions for new data. You don’t need a higher degree to apply machine learning, in fact, you may get to caught up in theory rather than getting results. You don’t need to know all the math and theory behind an algorithm, just enough practical knowledge to use it on your problem. You don’t need permission from a university or the universe before getting started, the tools and material are available for you now. Your boss doesn’t really care about some fancy degree, they want to know that you have the skills and confidence to deliver a predictive model for a specific problem at work.

Developers just like you are getting started in machine learning every single day.

They are starting small. Learning one tool, even one algorithm at a time. They are building up their bag of tools and portfolio of work to demonstrate they know their stuff.

You have started this journey, now we have to do the work.

Need a little more encouragement? Take a look at this post:

What is holding you back from your machine learning goals?

I’ll speak to you soon.


Identify and Tackle Your Self-Limiting Beliefs and Finally Make Progress

I get a lot of email from developers and students looking to get started in machine learning.

The first question I ask them is what is stopping them from getting started?

I try to get to the heart of what they are struggling with, and almost always it is a self-limiting belief that has halted their progress.

In this post, I want to touch on some self-limiting beliefs I see crop up in my email exchanges and discussions with coaching students.

Maybe you will see yourself in one or more of these beliefs. If so, I urge you to challenge your assumptions.

Self-Limiting Belief

A self-limiting belief is something that you assume to be true that is limiting your progress. You presuppose something about yourself or about the thing you want to achieve. The problem is you hold that belief to be true and you don’t question it.

Steve Pavlina lists 3 types of self-limiting beliefs in is post: Dissolving Limiting Beliefs:

If-then Beliefs: e.g. If I get started in machine learning, I will fail because I am not good enough. Universal Beliefs: e.g. All Data Scientists have a Ph.D. and are mathematics rock gods. Personal and Self-Esteem Beliefs: e.g. I’m not good enough to be a machine learner. You’re probably a logical and rational thinker. Apply those skills to your own beliefs about your goals and aspirations in machine learning and challenge them.

Waiting To Get Started

I think the biggest class of limiting belief I see is the belief that you cannot get started until you have some specific prior knowledge. The problem is that the prior knowledge you think you need is either not required or is so vast in scope that even experts in that subject don’t know it all.

For example: “I need to KNOW statistics“. See how ambiguous that belief is. How much statistics, what areas of statistics and why do you need to know them before you can start your investigation into machine learning?

Below are some of the more common self-limiting beliefs of skills or prior knowledge that must be obtained before you can get started in machine learning.

I can’t get into machine learning until…

…I get a degree or higher degree …I complete a course …I am good at linear algebra …I know statistics and probability theory …I have mastered the R programming language You can get started in machine learning today, right now. Run your first classifier in 5 minutes. You’re in. Now, start blocking out what it is from machine learning that you really want?

I have written about some of these before, for example:

Programmers can get into machine learning What if I’m not good at mathematics What if I don’t have a degree What if I’m not a good programmer Awaiting Perfect Conditions

Another class of self-limiting belief is where you are waiting for the perfect environment or conditions before taking the leap. Things will never be perfect, leap and make a mess, then leap again.

I can’t get started in machine learning because…

…I don’t have the time right now …I don’t have a fast CPU, GPU or a bazillion MB of RAM …I am just a student right now …I am not a good programmer at the moment …I am very busy at work right now It does take a lot of time and effort to get good at machine learning, but not all at once and not all at the beginning.

You can make good progress with a few hours a week, or tens of minutes per day. There are plenty of small snack-sized tasks you could take on to get started in machine learning. You can get started, it is just going to take some sacrifice, like all good things in life.

Struggling or Tried and Failed

The third class of limiting belief is that where you have made a small start but you are struggling or have failed to achieve your goal.

This is a tough one. Machine learning is hard but no harder than other technical skills like programming. It takes persistence and dedication. It’s applied and empirical and demands trial and error.

I can’t get into machine learning because…

…I feel overwhelmed …I don’t understand x …I will never be as good as y …I don’t know what to do next …I can’t get my program to work My advice is to cut scope or change direction. I advocate small projects as often as I can because the methodology has been so successful for me.

What is your self-limiting belief?

Do you have a self-limiting belief? Think about it. What are your goals and why do you think you are not there yet?

Do you have a goal to get into machine learning, to become a data scientist or a machine learning engineer but have not taken the first step?

Are you waiting to acquire some perfect set of skills before getting started? Are you waiting for the perfect conditions before getting started? Have you taken a first step and abandoned the trail? Where do you want to be and what are you struggling with?

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