7 Costly Mistakes Beginners Make When Starting A Machine Learning Career (plus tips on how to avoid them).
Let’s face it, starting a career in machine learning comes with strong dedication, commitment and a clear strategy.
Deciding to start a career in machine learning is a great choice. But how you start also matters. And this is what most starters don’t pay attention to.
Not paying attention to proper ways of transitioning into a machine learning career has cost many beginners. They end up wasting time, money and other valuable resources.
As a result of these challenges, most beginners believe that building a career in machine learning is a difficult task.
Though this experience can’t be entirely discarded as untrue, there are better ways and approaches to starting a career in machine learning.
The truth is, there are pitfalls you must avoid if you want to build a successful career in machine learning. And that is what I want to discuss with you in this article.
In this article, I am going to share with you 7 mistakes beginners make when kickstarting their career in machine learning.
I will also share some actionable hints to help you avoid mistakes and start your career strong!
Here Are 7 Deadly Sins Beginners Must Avoid When Starting Machine Learning Career.
Mistake 1: When you are not even starting at all.
Needless to say, not following your mind to start what you believe in is a glaring mistake.
Maybe you love to start one day, but then you always wait for a perfect time. You see, that perfect time? Hmmm, it would never come!
Were you excited about the opportunity to learn machine learning some months or years ago, but now you are developing cold feet?
The fact is: You won’t always have a perfect knowledge of why you need to start, but you have to start anyway.
While meaningful and strategic preparation is crucial, the fact of life is you can never achieve any success by dreaming alone.
Success comes through taking the very first step!
Not starting is never a winning strategy.
So, you’ve got to start!
You have to transit from being an enthusiast to a professional. Stop the plan and never-ending preparation. Just start.
It’s never a crime to start. It’s the core principle of life.
You won’t always believe you can really start, but always remember it’s not about what you believe. It’s about the action you take.
You’ve got to start before you become!
I know how procrastination hindered my desire to venture into machine learning a long time ago. I was always waiting for the perfect time.
Yes, I read blogs and other learning resources with a great desire to sit down and start, but I couldn’t just start!
Personally, I see this as the most common hindrances to achieving success, especially among beginners.
You may not know exactly how, where or when to start but you just have to start something from somewhere!
You won’t always be equipped with all the resources and tools needed to start. The fact is you don’t actually need to know everything before starting.
The reason you want to start is that you want to know what you don’t know.
Remember, slow and steady wins the race. So, start from somewhere.
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Mistake 2: When you are underestimating the time factor.
One of the most important factors in learning or starting a career in machine learning is time. And the term we use is “fast”. We want to achieve the goal…fast! We want to learn… fast!
Worst still, the ‘productivity hacks‘ industry seems to have created a bigger productivity challenge in this regards.
What you need to learn must be learnt. And learning in all context is a global function of time.
Even the productivity hack gurus understand it takes time to learn the art of hacking.
My point is, don’t ever approach learning machine learning with the mindset of ‘hacking’ your way through.
There is no short cut. You’ve got to sit down and do the learning.
While you can optimize your learning process (the time it takes you to achieve your goal), the fact is you can’t achieve meaningful value without commitment and dedication which are all a function of time.
When it comes to starting a career in machine learning, it is a decision that takes a reasonable time investment to achieve.
Don’t start it if you can’t commit to it.
Learning is about progression.
Anything worth learning shouldn’t be approached with levity and unrealistic assumptions.
Instead of targeting the time, why don’t you approach your learning path through a value-addition mindset?
So, you ask yourself, what knowledge am I interested in acquiring this week, …this month, … this year?
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Mistake 3: When you are relying solely on passion as a recipe for success.
While passion is a good thing in any endeavour, passion alone doesn’t deliver.
The fact is, you don’t necessarily need passion to succeed.
In the midst of that unmotivated and uninspired feeling of yours, all you need to know is this: while your feelings are great, you don’t actually need them to succeed.
You need to be committed to achieving success.
It takes action, not just passion, to achieve a goal!
Action should be the most emphasized ingredients of success. Without it, passion becomes a deadly attribute.
Stop being just passionate. Passion without action will lead to persistent frustration. Passion isn’t a sufficient requirement for achievement. Action is!
Start being ‘actionate!‘
You are more likely to succeed in any endeavour when you take action, much more than when you are just being passionate.
What to do instead: Be committed to your goal and be action-driven enough to succeed. Prioritise commitment above feelings.
Passion is great. But only your commitment can translate to success.
Also, read these:
Mistake 4: When you always bite off more than you can chew.
Knowledge is infinite. No one can learn all therein at once. I tell you this is one of the most challenging problems you will face while trying to learn machine learning.
You will get more than you bargained for. On one side, it’s a good thing. But if not well managed, it might turn out to be a really bad idea!.
I would say, don’t go to the internet with an open mind, because you will come back heartbroken and disappointed!
The internet is awash with information on almost all subjects. This might look cool for you as you are setting out to learn.
But if care is not taken the availability of information online may become detrimental to your learning success.
You may have all answers at your disposal, but you may never know what the question is!
Not all information on the internet is there to inform, some are there to deform you. You need to move back and ask yourself, what exactly do you want?
Cool blog design or website functionality should not be your primary concern. Your concern should be whether the blog provides a solution to your earlier identified challenges/issue/goal.
Have a plan. digest the information so you can be creative and get a new perspective.
Develop a clear learning strategy that works the best for you and stick to it.
Remember, slow and steady wins the race.
Mistake 5: When you are learning inconsistently.
Let me tell you this.
One of the greatest pains you can inflict on yourself is learning machine learning without a clear strategy! In this situation, everything becomes learnable, but not everything is profitable.
Don’t spread yourself thin learning every piece of information about machine learning you see online. An unstructured learning approach will make your journey rough, long and uninteresting.
With this approach, you will put in more effort and achieve little if anything at all. Obviously, this isn’t a working strategy.
In some instances, some pieces of information may be worth exploring, but this should be within the time structure and outcomes you have developed for yourself.
Learn with purpose and enjoy the process.
Know exactly what you want, why you want it and when you need it.
Be led by purpose.
If you follow a systematic learning framework, you will learn even faster with clear impact tracking and value addition assessment.
Also, read these:
Mistake 6: When you are developing a tool-focused mindset at the expense of domain knowledge.
I have seen huge projects fail because of a lack of proper framing of the problem statement, weak domain knowledge, inadequate clients engagement and lack of business-case clarity.
Start with your domain knowledge. Start with where you are: your day-to-day role, your interest, your organization.
The domain knowledge of market/industry trends, developments and outlook will always be the most cherished skill of machine learning starters.
The problem statement isn’t just about the objective, it’s much more about identifying and dimensioning the problem even before you start the project.
Beyond your ability to gather data, analyse and evaluate a model with good accuracy, having domain knowledge of the variables and relationship among them is key.
You risk the core ability to interact with your organization clients and offer real business solutions if all you know is Python!
Of course, this is not to deemphasise the need for solid technical knowledge and background in machine learning tools and libraries.
You should attempt to learn and develop your understanding of the inherent usefulness of machine learning tools, libraries and other resources in addressing business problems and challenges.
Mistake 7: When you always learn in isolation
Learning in isolation isn’t the best strategy to learn machine learning.
At the very beginning of your learning stage, this might be encouraged but as you move on, you have to reach out. Remember if you want to go fast, go alone. If you want to go far you go in a group.
The machine learning production cycle is all about teamwork. So learn how to learn from others. Interact more.
Learn to interact with others on different online and offline platforms such as Kaggle, Github, etc. Follow people on social media. Attend conferences and summits even if you don’t understand everything.
There are different resourceful communities of machine learning online and around you. Community creates an awareness of collective purpose, direction and growth in you.
Apply what you have learnt.
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:
Other related articles:
- 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).
- 10 Interesting Project Ideas to Prepare You for Machine Learning Role.
- How to Learn Machine Learning The Self-Starter Way: Complete Guide.
- Start Here With Machine Learning: A Beginner-Friendly Step-By-Step Procedure.
- A Complete Guide To Learning Machine Learning In Your Spare Time: An enthusiast’s approach
- 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.
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Hi, there! I’m Olusegun.
I help ambitious machine learning starters develop a working strategy to learn, build projects, develop a winning portfolio and start a career in machine learning with ease. Dreaming of building a successful career in machine learning? Let's do it together.
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