When you start learning machine learning, you will soon discover that having a firm grip on fundamental frameworks isn’t as simple as it has been portrayed.

Without a working strategy, you can actually spend more time than necessary learning machine learning.

In this blog, I will share with you some basic learning techniques that will ultimately improve the way you learn, boost your concentration and increase your efficiency.

My learning philosophy is simple: ‘Anything worth learning, worth learning on time.’

There is a growing number of machine learning starters who remain ‘starters‘ even after years of starting.

Even though the internet is awash with different information and resources on how to learn machine learning, many people still find it difficult to master machine learning within the stipulated timeline they set.

Why this?

First, they simply don’t know why they are interested in machine learning. Hence, they are quick to sign up for random machine learning courses and even quick to abandon them with no tangible learning outcomes.

Second, they jump from one aspect of machine learning to another without structured format, timeline and set outcomes. They keep learning but never understand.

Note: In another article, I had explored different warning signs beginners need to watch out for before starting with machine learning.

If you have been finding it challenging to learn machine learning despite the enormous efforts and resources you have expended, the truth is you are not alone! You can check out this brilliant piece on costly mistakes to avoid in learning machine learning.

There are efficient and highly effective techniques of starting with machine learning that will ensure you don’t just learn but learn productively!

This is one of the best learning tips I always apply in my learning strategy.

When you approach machine learning with this mentality, you become more purposeful and goal-oriented.

More importantly, these techniques will help you improve the way you learn, boost your concentration, minimize distractions and increase your efficiency.

Now, the overall idea here is to ensure you optimise your time while learning machine learning. Ultimately, this will give you enough room to learn more and achieve your goal in the quickest time possible.

Here is the deal.

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The truth is, you don’t have all the time in the world to learn.

In life, nothing waits for you. As you are about to learn one thing, other new things are springing up.

And as the burden of learning increases, your motivation starts weighing down. You may eventually give up completely!

If you don’t develop smart learning techniques, you will soon discover it may take you forever to learn even the simplest thing.

But you can always be on top of your learning game. All it takes is the focus and clear strategy.

In learning machine learning, developing a purpose-driven mindset will help you frame your own learning strategy in a manner that ensures effective and efficient learning.

In this blog, we are going to look at 8 vital strategies and secrets to mastering your progress and start learning machine learning without stress. 

Let’s go there.

The ultimate techniques for learning machine learning faster and more productively.

1. Start with ‘Why’ (though not always).

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He who has a why can endure any how.

Frederick Nietzsche

Venturing into machine learning is trendy, but don’t just jump into it without starting with ”Why.”

You really don’t need to ‘rush’ into learning or developing a career in this ‘sexy’ field.

If you don’t have any motivation for or fundamental interest in machine learning, you will soon discover that nothing is ‘sexy’ in this line of career.

The most vital success factor in machine learning, especially as a beginner, is to learn with purpose.

Be steady. Be driven with “why.” Know what you are going into and why you are doing so. 

Yes, I know this is not always easy. But starting your learning journey with the understanding of the whole essence of venturing into machine learning will save you a whole lot of time and some headaches too.

I tell you, a good number of machine learning starters are merely being driven by impulse! This explains why they give up on their goal so soon.

Why are you interested in learning machine learning?

Is the reason worthy of your time, money and other resources?

If your answer is YES, then this article and many others in this blog will be of great resource for you. Read on.

However, if your answer is No, then you know what to do, don’t you?

No, I won’t tell you to quit. The truth is, some people find the reasons before they start. Others find theirs along the way.

Your being inquisitive about machine learning could actually signal your passion for and a deep interest in a machine learning career.

If you still want to have an idea of what machine learning is all about, in this case, maybe you are just an enthusiast. There is always another strategy for you.

Above all, you just have to be sure of your decision to venture into machine learning. Time is too short to waste on what you are not really interested in.

These questions might help you.

To help you move ahead, here are key questions you may have to answer to uncover your ‘Why’ in the quest of learning machine learning.

  1. There are many interesting and value-filled skills and knowledge base, why am I interested in learning machine learning?
  2. There are different reasons why people venture into machine learning career, which one is mine?
  3. What plan do I have with machine learning: just personal development? career development? just an enthusiast? etc.
  4. How ready am I in committing my time and other resources to this endeavour?

Your sincerity in answering the above questions will go a long way in putting your passion in a proper perspective.

Having said this, you need to know that on some occasions, asking ‘why’ could be self-sabotaging! You may not actually know why until you start.

You may find your ‘Why’ along the way.

Therefore, don’t be bogged down by the need to always know why.

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).

2. Learn one thing at a time (you can never go wrong with this).

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To do two things at once is to do neither.

Publilius Syrus

Things are changing now and then. New tools, new frameworks, new programming languages, new technologies, new libraries, etc.

Don’t be distracted by the constant drive of wanting to know everything at a time.

Your objectives of learning machine learning should be clear and simple.

If you don’t imbibe the learning culture of “one thing at a time,” you may fall into the common trap of “learn as it comes” that takes you nowhere.

The lack of a well-streamlined strategy will not only delay your progress but will also make it difficult for you to develop your skills and knowledge in learning machine learning.

Don’t ever feel pressed to want to jump the process you have developed for yourself.

Never allow the drive to want to learn every new thing in machine learning to distract you from the core objective you have developed for yourself.

At first, jumping from one topic to another might look interesting and cool, but later you will discover how you keep losing your momentum, passion and willingness to learn. 

Don’t get distracted at every instance of new developments in machine learning. When you have not laid a solid foundation for yourself, jumping from one course to another isn’t the best strategy for you.

If you want to maintain your level of concentration in learning machine learning, be focused. Learn one thing at a time. Deepen your understanding of core fundamentals.

The strategy here is FOCUS.

When you concentrate on what matters most in machine learning, your learning path will always be smooth.

So, how do you maintain your learning drive?

Develop a step-by-step approach to learning and make sure you stick to it.

Of course, there are some rooms for flexibility, but you shouldn’t lose your focus.

It may look like it’s taking a long time for you in going through this process. I tell you, at the end of the day you will discover that this is one of the best strategies for learning machine learning faster.

I know this is not as easy as it sounds. But you will always win with this approach.

Also, read this:

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

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

3. Start with practical projects first (and you will thank me later).

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Practice puts your brain in your muscles.

Samuel snead

Let me tell you this. I have discovered that it’s not always a good idea to start learning machine learning with the theory, except you have a very good prior foundation of some basic theoretical constructs.

Make sure you start with understanding the basic concepts, frameworks and categories of machine learning. Then jump into real-life practical applications and projects.

Start with basic machine learning projects out there.

The point is: You need to keep yourself excited about how machine learning tools are deployed in solving real-life problems.

You need motivation.

You will make a lot of progress soiling your hand with practical applications of the machine learning framework. The earlier you start this, the better.

Of course, this approach is effective as it exposes you to the nitty-gritty of machine learning.

Now, starting your learning journey with practical applications has several benefits. Starting with some excitements keeps your interest level high.

Imagine the feel of handling your data, running your code and evaluating your result!

Besides, starting with practical projects also helps you identify your interest and likely specializations.

In like manner, starting your machine learning journey with practical projects offers you the benefits of familiarising yourself with different machine learning frameworks, tools, programming languages and their libraries early in your learning process.

Also, read these:

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

A Complete Guide To Learning Machine Learning In Your Spare Time: An enthusiast’s approach

4. Document your progress and learning tips (you will always come back to them to re-learn).

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To learn machine learning quickly and more efficiently too, one of the secrets is proper learning documentation.

It may surprise you to know how quickly human memory can lose earlier stored information.

You need to deliberately and continuously store, refresh and restore the information in your memory to be able to recall it.

One of the best strategies of my early learning stage of machine learning is documentation. I never took any learning insights or knowledge for granted.

Your ability to effectively document your learning progress is key when you are after quality learning.

Many people are actually learning but the challenge is the rate at which they keep losing touch with what they had learnt.

Don’t just learn, learn deliberately.

You can always achieve a lot when you follow this step-by-step approach to learning.

Before you start a new topic even a project, ask yourself what do you want to achieve and how best should you document the learning outcomes.

Learning documentation reinforces what you have learnt and lays a good foundation for the next step in your learning ladder.

You don’t want to take this for granted.

Always remember this, it is not all about what you are learning, it is also about how you are storing the knowledge. This is where proper documentation comes in.

This is why documenting your learning progress matters:

One of the principles of efficient adaptation is learning. Learning precedes application. But the application is a function of an efficient recalling of learning outcomes.

Here are the most important benefits of learning documentation in machine learning: 

  1. Self-motivation and morale booster: Proper documentation of your learning progress helps you know how far you have gone in your learning journey and it’s also a boost for you to want to learn more.
  2. A vital ingredient for building your machine learning portfolio.
  3. Point of reference: Curating your learning achievements helps you in developing a point of reference you can own. When you need to recall how you started and the different tips you discovered along your learning path, proper documentation is the key. This does not only benefit you; it also serves as a great resource for others who are just coming up on the ladder you have climbed before. 
  4. A teaching tool. If you want to make the most use of your knowledge in impacting other starters, document your progress. It is your story. Others can learn from it. You don’t really need to be an expert to be a source of inspiration or reference point to others. When our learning outcomes are well documented, they can serve as resources to teach others either by starting your own blog, teaching online, producing an ebook (free or premium) or even developing a step-by-step tutorial on learning machine learning.

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.

5. Join machine learning competitions (this will drive your motivation and test your capability)

Participate in the challenge even if you are not sure you have what it takes to do so. Just do it!

Learning through competition? You can’t go wrong with this approach. And that is the truth!

To speed up your learning journey, one of the best strategies most beginners adopt is to be part of machine learning project competitions. Join Kaggle competitions. There are other machine learning competitions that can help you horn your skills.

Participate in the challenge even if you are not sure you have what it takes to do so. Just do it!

This approach has proven to be very effective in helping beginners learn machine learning faster.

The sense and feeling of being in a competition in itself will help you bring out the best in you.

All you need to do is get on board as soon as the competition is announced. Make it a point of duty to give it your very best.

You will also learn from the experience of others.

You should also understand that these competitions aren’t all about python codes, competition projects offer you the opportunity to apply your unique domain knowledge and experience too.

This is very important especially when it comes to the result interpretation and appropriate recommendations.

Why do you need to be part of the machine learning competition?

As a beginner, joining a machine learning competition has several benefits for your progress.

Being part of the competition naturally exposes you to different real-life applications of what you have been reading about.

Most importantly, you will learn a great deal about machine learning projects pipeline and documentation.

This experience will further broaden your horizon and build your confidence in handling practical machine learning use cases.

Also, the detailed explanation of the project, the presentation of the data and the step-by-step breakdown of the expected outcomes in terms of clients expectations are clear value adds you are sure to benefit from when you participate in this type of competition.

Your projects in these competitions can be featured in the portfolio as part of your practical project experience.

This is by far, one of the winning strategies in developing a machine learning portfolio an employer will love to check out.

6. Apply machine learning concepts, tools and frameworks in your job (the all-time best strategy anyone will recommend to you).

The best way to learn and build your capability is to apply machine learning tools in your current job.

If your goal is to transition into machine learning career, the best way to learn and build your capability is to apply machine learning concepts in solving your day-to-day problems in your current job.

Are you into data analysis? Do you apply some forecasting or predictive tools?

Do your current job responsibilities relate to finding relationships between data and deducting valuable insights for business growth?

If your answer is YES to at least one of these questions, then, you can start applying basic machine learning frameworks in these areas.

Don’t just apply machine learning tools in your current job just because they are the latest, ‘sexy’ and fanciful thing to do.

Of course, make sure the application of machine learning tools is adding valuable or superior value to your organization.

7. Teach and share what you have learnt (this is one of my favourites).

Sharing with others what you know has bidirectional impacts: People learn from you. You also learn from yourself!

Olusegun Omisakin

You can never go wrong with sharing your knowledge. One of the best ways to learn machine learning faster as a beginner is to share your knowledge with others.

If you are really committed to sharing your knowledge with other beginners, it will always pay off for you.

Teaching or sharing with others what you know has bidirectional impacts.

First, you are sharpening your machine learning skills and reinforcing/deepening what you have known.

Second, you are directly impacting others as they learn from strategies, approach and tips which have worked for you.

You see. It’s a win-win situation!

I can assure you, the more dedicated you are to adding value to others who interested in growing their skills and knowledge base, the broader you expand your skill and practical knowledge of machine learning

At this stage, people have trusted your brand, trustworthiness and readiness to add value.

How do I teach or share my machine learning knowledge even as a beginner, you may ask?

There are several channels through which other learners can benefit from your knowledge. For you to do this successfully, follow these steps:

  1. Be sure you understand what you are interested in teaching others, even though you don’t really need to be an expert.
  2. Be ready to learn from others too. It’s always a bidirectional relationship.
  3. Create your platform: start blogging, open your YouTube channel, explore your socials media accounts, etc.
  4. Write a free ebook detailing what and how you have learnt and share it with other beginners.
  5. Build followership and constantly deliver value.
  6. Have fun all the way.

8. Network with and follow machine learning experts and thought leaders (never lack this skill, never)!

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Networking is more about farming than it is about hunting.

Ivan Misner

No one knows it all.

It’s that simple.

Developing a strong and functional connection with other beginners and experts is the rule of the game.

It will always pay off.

This is one of the unwritten rules of knowledge and skill development:

if you want to grow faster, learn alone. if you want to grow longer and deeper, learn with others!

Networking with the Machine Learning community of experts and thought leaders is one of the soft skills you need to scale your learning progress.

Follow others.

Learn from them.

Promote their works.

Contribute to their success story.

Networking offers a lot of benefits to machine learning starters. Apart from opening up your horizon to bigger and wider opportunities, it helps you to keep abreast of the industry developments.

Functional networking can also connect you with those who are ready to assist you on your journey of machine learning career.

On a final note…

Learning machine learning effectively takes some efforts and strategies too.

It doesn’t matter how difficult it might look, if you approach it with a clear mindset you will always learn faster than you think!

As you challenge yourself to go deeper and move higher 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. Start Here With Machine Learning: A Beginner-Friendly Step-By-Step Procedure
  5. 7 Costly Mistakes Beginners Make When Starting A Machine Learning Career (plus tips on how to avoid them).
  6. A Complete Guide To Learning Machine Learning In Your Spare Time: An enthusiast’s approach
  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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