If there is anything we all must come to terms with, it is the fact that sometimes, all we want to do is learn a skill or two just out of curiosity. 

Building a career from such a skill might not be our immediate goal. And really, there is nothing wrong with that!

Have you always wanted to know what machine learning is all about without the immediate intent of building a career in it?

Making the best use of my time and resources in developing my potential has always been a rewarding exercise for me.

Some of us just want to enjoy learning new things. And whether that leads to starting a new career isn’t our immediate concern.

This mindset to life, for me, is really cool.

It is important, therefore, not to approach every opportunity to learn or develop our skill, as another moment to start a career.

In my day-to-day interaction with people, I discover there are brilliant and sharp guys and ladies out there who are really interested in upscaling their skills.

Let me ask you this question:

Have you always wanted to know what machine learning is all about without interest in starting a career in it?

You care about anything Artificial Intelligent tools.

You understand the need to prepare yourself for the job of the future and you really don’t want to be left behind.

But here is the thing, though. You are not just keen on starting a career in this field, yet.

If this describes your situation, you are not alone.

There are many people out there who desire to learn machine learning on their terms as enthusiasts.

Unfortunately, not many online resources about learning machine learning have these underlying assumptions. Those materials are mostly job-centred or career-focused.

You don’t have to feel left out.

I created this blog just for you.

So, let’s get to the point.

If you desire to learn machine learning without planning to start a career in it, you don’t have to be discouraged.

You will soon discover some interesting things about being a machine learning aficionado.

In this blog, I will walk you through all you need to do in order to learn machine learning at your own pace.

I will expose different learning strategies you need to apply that will help you learn machine learning in your spare time with fantastic results, all within a relatively short period.

Also, read these:

How To Start A Career In Machine Learning: A Complete Guide To Career Transition.

Reasons You Don’t Need to Learn Machine Learning (and what to do instead).

What exactly does it mean to be a machine learning enthusiast?

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As an enthusiast, you are simply interested in some machine learning concepts, principles or general ideas.

So, if you are satisfied with launching into machine learning without a career in mind, it’s safe to call you an enthusiast.

This is how to know you belong to this class of machine learning starters. These questions will determine to what degree you tend to be a machine learning enthusiast:

  • You are a generalist when it comes to knowledge acquisition and skill development.
  • You just love upscaling your knowledge flow as new things unfold.
  • You love the interconnectivity of every skill.
  • You love the challenge. The need to always contribute to the latest discussion appears to be the driving force behind your love for learning.
  • You are a tech and AI lover, but you don’t necessarily practise them on a career level.  
  • You love coding here and there, learning python or anything programing.

Does any of the above describe your approach to learning machine learning?

Also, read these:

10 Interesting Project Ideas to Prepare You for Machine Learning Role.

How to Learn Machine Learning The Self-Starter Way: Complete Guide.

Here are the key benefits of being a machine learning enthusiast

As I earlier stated, you don’t have to be defined by your career. Being a machine learning enthusiast offers several benefits that have different implications for career progress and development.

Among the benefits are:

  • Being a machine learning enthusiast is another way of broadening your capacity and reservoir of knowledge in artificial intelligence, data science and predictive analytics.
  • It allows you to understand your current job better. Your domain knowledge combines with the basic tools of machine learning will always be a great skill.
  • Being an enthusiast can pave the way for you to smoothly transition into a machine learning career role if and when you so desire. Who is well prepared for this role if not you?
  • It’s another way of building your passion into a full-fledged side hustle and freelance engagements.
  • Remember, some business owners today actually started with being an enthusiast and then migrated into developing their passion and their business model on what they used to be enthusiastic about.

This is how to learn machine learning in your spare time when you are just an enthusiast.

Tip 1: Set your learning target

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Don’t box yourself in those unrealistic learning goals out there.

The first step you need to take is to be clear on your learning target. Yes, you have to set measurable learning goals for yourself to be able to measure your progress and success.

Apart from allowing you to take ownership of your learning agenda, setting your learning targets also signals your readiness to move from being a passive to an active learner.

This helps you monitor your progress. It also makes you feel more motivated towards achieving your set target.

So, how do you draft the goals?

Well, there is no one-size-fits-all approach in drafting learning goals. All you need to do is make sure the design or structure accommodates your learning or personal preference and style.

Also, do make sure your learning goals are explicit enough to guide your focus and attention.  

Write down your goal. Make them definite, practical and meaningful for your overall purpose.

This will ensure you are evaluating the things you care most about machine learning.

Tip 2: Start with the basics

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Take it one step at a time.

Start from the basics and build it up.

Walk before you run. Take your time. Move at your phase.

Remember you are not under any pressure. This is the best mood you need to meaningfully learn machine learning.

Start with basic ideas, concepts and tools.

You are likely going to have a strong grip on machine learning when you start with understanding the basic concepts and principles. Start with the introduction. Don’t be tempted to jump the gun.

Learn one basic concept at a time. You will soon discover that the so-called basic knowledge will form the critical mass of your foundation as you learn along.

The better your understanding of the general foundation and framework of machine learning, the more meaningful and faster your progress will be.

One of the secrets to learning machine learning is to have a firm grip and sound understanding of its concepts and elementary mathematics.

Start with introductory machine learning concepts and topics such as multivariable calculus and linear algebra. You can also pick up some coding and programming experience in Python.

Again, one of the best ways to really appreciate the fundamentals of machine learning is to experiment with some simple and easy-to-handle use cases. It doesn’t need to be a complex project.

You can actually check on the internet and blogs where simple machine learning projects are discussed from the beginning to the end with some emphasis on the coding steps.

Also, read these

Start Here With Machine Learning: A Beginner-Friendly Step-By-Step Procedure

7 Costly Mistakes Beginners Make When Starting A Machine Learning Career (plus tips on how to avoid them).

Tip 3: Read a book or two

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For you to really make an impact in your machine learning field, you just have to be deep and stay updated! One of the ways you can achieve this is by developing a culture of reading machine learning books.

You can’t get it wrong reading your way to machine learning. Reading books is one of the best channels of learning machine learning as a beginner.

Don’t be discouraged by some not-too-easy-to-handle topics.

Study them for fun. You will discover you can learn wider and faster by reading books on machine learning.

In fact, most of the contents you see on blogs are a product of wide book reading on the part of the content creator.

The more books you read, the more knowledge and understanding of machine learning you will acquire.

The convenience of having machine books around you means you can always grow your knowledge in your leisure time.

Slow and steady wins the race, remember!

Also, read these:

5 Key Most In-Demand Soft Skill Sets You Need to Develop as a Machine Learning Career Starter

How To Build A Compelling And Winning Machine Learning Portfolio That Will Get You That Job.

Tip 4: Identify the role of machine learning in your current job and cash on it

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Won’t it be fun if you can actually apply some of the machine learning concepts and tools in your job?

Sure, a blend of your acquired domain knowledge and machine learning framework will always do the magic in your profession.

Yes, you aren’t necessarily transitioning into a full machine learning career, but being able to demonstrate or reflect some insights of machine learning in your day-to-day job responsibilities will always set you apart.

For example, the skills you have acquired so far has taught you the importance of data handling, processing and analysis. This skill will always be useful for you anytime.

You may not be a machine learning engineer, but you can always apply machine learning concepts and principles in your respective domain.

This means that being a machine learning enthusiast is an added advantage for your career development in the sense that you have now acquired relevant skills and capabilities.

Tip 5: Always document your learning process and progress

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Now, those earlier listed tips will become meaningful when you pay proper attention to learning progress documentation.

You will never know how far you have gone in your quest of learning machine learning without careful documentation of what you have learnt over time.

There is nothing as beautiful and encouraging as seeing how far you have gone in your learning endeavour.

Documenting your learning insights, challenges and achievements is one of the secrets to retaining what you have learnt.

Proper self-documentation is key. You will never know what you are capable of doing until you start documenting what you have done. This gives you the courage to keep going and achieving more.

As you start with learning the basics, reading the blogs and books, you have to keep documenting your learning outcomes. You will always fall back to them.

Understanding what you are reading is as important as documenting it in your own style.

Tip 6: Create avenues to teach or share your experience

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When you teach others what you know, it helps you grow deeper in your knowledge. Look for opportunities to share your experience so far in learning machine learning.

You don’t have to wait until you are a guru. You can start even now (I mean after reading this blog😁).

You can do this through your social media. Better still, if you are really set for this, you can start your blog where you document and teach others about your learning progress and skills.

This is one of the avenues through which you can announce your ‘arrival‘ into the machine learning tribe!

Starting your blog affords you the freedom to express your opinion and tell your story the way you want it.

Apart from the fact that it improves your visibility, sharing your learning story about machine learning can benefit other starter or enthusiast like you.

In conclusion

In this blog, you have learnt what it means to be a machine learning enthusiast and how you can learn machine learning in your spare time even when you are not interested in taking up a career immediately.

You are more likely to build a successful career in Machine Learning when your journey started from being an enthusiast!

As you challenge yourself to move higher in your interest in machine learning, I have carefully selected the following resources to help you learn better and faster:

  1. How To Start A Career In Machine Learning: A Complete Guide To Career Transition.
  2. Reasons You Don’t Need to Learn Machine Learning (and what to do instead).
  3. 10 Interesting Project Ideas to Prepare You for Machine Learning Role.
  4. How to Learn Machine Learning The Self-Starter Way: Complete Guide.
  5. Start Here With Machine Learning: A Beginner-Friendly Step-By-Step Procedure
  6. 7 Costly Mistakes Beginners Make When Starting A Machine Learning Career (plus tips on how to avoid them).
  7. 5 Key Most In-Demand Soft Skill Sets You Need to Develop as a Machine Learning Career Starter
  8. How To Build A Compelling And Winning Machine Learning Portfolio That Will Get You That Job.

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