How To Start A Career In Machine Learning: A Complete Guide To Career Transition.
You are here because you are interested in starting a career in machine learning. Well, that’s my guess. Maybe you are planning to transition from your current job into a full machine learning role but not fully convinced you are making the right decision.
You know what? You are never alone!
The truth is: Launching into machine learning career takes planning and adequate preparation. But you don’t need to be a pro before you actualize this dream.
With a strategy, you can learn machine learning faster and actualize your dream of transitioning into a machine learning role with ease.
This blog will teach you all you need to achieve your dream of a machine learning career.
What does it take to take a leap into machine learning career from your current job in 2021?
Yes, you can make that career change with ease and achieve your dream.
I know this might sound simple, but the truth is, you can! And why must it be complex, anyway?
Let’s start with this.
If at least one of these scenarios describes your current career situation, then you are in the right place on this blog!
# Scenario 1: You are not currently in the machine learning field but you actually want to start learning the tool.
# Scenario 2: You are really in love with machine learning and ready to leave your current job and pursue your career in the machine learning field.
# Scenario 3: You have learnt machine learning frameworks and gained some level of expertise in the application of machine learning. And all you need now is to get a job in this field.
Why are machine learning jobs so popular?
According to the IDC Report (2020), global spending on Artificial Intelligence will double in four years, reaching $110 billion in 2024.
The expected implications of this especially on sales/marketing and manufacturing/supply chain industries cannot be overemphasised.
In fact, the machine learning career path has witnessed a great turn-around to the extent that industries are now experiencing a shortage of qualified experts.
Much more than what we are experiencing currently, applications of Artificial Intelligence and machine learning are projected to witness a tremendous transformation in years to come. Thanks to the Fifth Industrial Revolution (5IR).
So, you can relate to why the rush for artificial intelligence and, specifically, machine learning among young career starters.
When you are convinced it’s time to move, no stereotyping can hold you back.
Do you know that more and more resources are popping up on the internet daily about how to learn and start a machine learning career?
I know at one point in time you also would have contemplated the possibility of starting a career in this field.
“But, is machine learning career for me?“, you might ask.
When you are truly convinced it’s time you move, you will discover that this step isn’t as difficult as it looks.
Here is the thing.
People view the ambition of being a machine learning developer as a difficult dream which is only meant for some special ones.
They believe starting a career in or using machine learning frameworks in your field is a no-go area for the ‘ordinary’ guy.
I’m sure you know this isn’t true.
Let’s look at it this way.
When intending starters talk about a machine learning career, here are the few assumptions and stereotypes that fuel their orientations:
- I am not an expert. what do I know about mathematics/statistics?
- I know I’m not very good at coding or programming languages
- Well, only data scientists can succeed in the field
- I need a PhD or at least a masters degree in machine learning
- Machine learning career is meant for young career starters. I’m too old for that stuff.
Let me ask you, are you also a proponent of this highly erroneous ideology?
Indeed, some of the assumptions highlighted above may help start with machine learning.
But I tell you, you don’t actually need to have possessed these qualities before you build a successful career in machine learning.
To successfully launch into the machine learning field, all you need is YOU.
If you set your heart at achieving success, you would most likely be successful, and machine learning isn’t an exception.
First of all, a machine learning career isn’t meant for only some ‘IT guru and ‘coding-anointed’ fellows.
So, let’s set the record straight.
ANYONE willing, determined and dedicated enough to learn can build a career in machine learning!
What is machine learning in the first instance?
Isn’t it a tool, a framework or methodology that can be deployed by anyone towards solving the already identified problem?
The truth is, achieving a successful and seamless transition into a machine learning career isn’t as straightforward as starting online machine learning courses.
So, you will do yourself a great favour by approaching a machine learning career with this mindset.
This does not mean only the selected few have what it takes to make a move to a machine learning career.
The truth is, ANYONE can learn and build a career in machine learning. All it takes is passion, commitment, dedication, strategy and focus.
Beyond just learning machine learning, Arpan Chakraborty believes there is a need for you to start working in the field.
Also, to help beginners gain some perspectives on starting a machine learning career, Ram Tavva underscores different machine learning types, career paths and job roles.
Madhukar Jha, the founder of Blue Footed Ideas, in his “How I Became a Machine Learning Expert in 10 Months,” highlights different steps to starting a career in machine learning.
In reinforcing the required dedication and focus, he posits that “The transformation process was not easy and demanded hard work, lots of time, dedication and required plenty of help along the way.”
But, what does career transition actually mean? And how can you transition from your current field to a full-blown machine learning role?
Career transition actually connotes career movement and this can happen horizontally or vertically.
# Horizontal career transition occurs when you change the core of your job responsibilities by moving completely away from your current field or career to another. This movement might also affect your job title.
When this happens, your job responsibilities, your domain knowledge and your career field assume a new direction.
This, in turn, may require the acquisition of a new set of skills and capabilities on your part.
This is typically referred to as change of career.
In this respect, you are likely in one of these cases:
- You are most likely to be a career starter with sufficient time to build a new career in machine learning.
- You have a strong background in artificial intelligence, statistics, coding, programming and you’re not currently applying these skills in your current job. So, you want to move to a new career path!
# Vertical career transition signals more or less a career deepening rather than a complete change. It is a movement along the same industry, domain or career.
This is generally referred to as career progress.
This might happen because you are now specializing in a different niche or you are now adopting new methodologies to solve the old problems or maybe you are now focusing more on finding solutions to the new challenges in your field or industry.
By implication, you aren’t leaving your domain, you are simply changing your approach, tools and, maybe, even title. So, your cumulative experience overtime is all related and relevant to the new role.
Vertical career transition might also come with a change in job title and/or responsibilities in some instances.
Whatever the case might be, the fact is you are not entirely leaving your current career field.
In all, your domain in the current job remains. You are still in the same career field even though you might be exploring other niches or applying newer frameworks or methodologies in the same industry.
In this case, here are the reasons why the vertical career transitioning option might be best for you.
- You have extensive experience in your current job responsibilities.
- The application of machine learning tools is already disrupting the conventional approaches or methodologies in your current field. So, you want to plug into this.
- There are new business cases/opportunities which are best approached by the application of machine learning frameworks.
As you can see, the best approach actually depends on what works for you.
Horizontal or Vertical, here is a word of caution for you.
Career transition, if badly planned, can end in a disaster!😳😳
To start with, make sure you are not contemplating a machine learning career simply because of some flimsy reasons.
To achieve a successful career transition, you need a strategy. You must develop a plan. You need to have a clear vision of your strategy.
You can’t leave anything for chance. Every of the action, decision and step you take must be well informed towards a clear strategy.
You don’t just wake up and decide you want to transition from your current job.
It doesn’t work that way.
You need to plan. Of course, this plan doesn’t need to be overly detailed or complex.
All you need to do is to develop a SMART strategy.
So, why do you need a strategy?
When you plan, you can foresee challenges to prepare for and opportunities to explore.
Give yourself some time to perfect the core skills needed to make the transition smooth.
There is no point in resigning from your current job with the hope of landing a new job when you are not well prepared for the change.
You have to subject your drive, desire and motive to some planning.
Do you want to make a smooth career transition to a machine learning role? Develop your plan, understand what it takes and stick to it.
Questions to ask yourself before you make that career move.
Before you decide to make your move or consider making a vertical or horizontal career transition from your current job into the machine learning field, you need to ask yourself some critical questions.
When you carefully consider these questions, you will see the bigger picture and make a well-informed move.
Asking and answering these questions will also guide you in making sure that you are making optimal decisions towards the overall progress in your career.
Here are the questions:
- Why am I considering career transition at my current career level now?
- Is this the best time for me to embark on such a move?
- What approach is best suited for me in my current situation?
- What strategy do I have to adapt to optimise the outcome of this transition?
As you know, any action you take or decision you make in the course of your career will always have some consequences.
It all boils down to how well thought-out your decisions process are.
When you can answer these questions critically, you then can have a clear part as to the next decision to take whether you move vertically or horizontally.
Also, read these:
This is how to plan your transition from your current career to the machine learning field.
Here, you start adopting the machine learning tools in your current job without moving to another career. I called this vertical career transition earlier.
How do you go about this?
First of all, learn machine learning.
Secondly, niche down to your industry domain: Take a critical look at your domain. Are there new challenges, business cases or opportunities begging for a new solution or approach? This could, in fact, make you a sought-after solution provider in your area.
You can champion the application of machine learning in your domain and become a thought leader in your industry. This is another innovative way of distinguishing yourself in your industry.
Lastly, start applying machine learning tools and frameworks in your job. Identify the gap and see how machine learning frameworks can fill it.
Specifically, you can be a team leader in new data science or machine learning unit in your organization.
You can even advocate for this by demonstrating how the adoption of machine learning methodologies can foster the achievement of the organization’s mission.
This approach points to one fact: You don’t necessarily need to change your career before you achieve your dream of applying machine learning tools and methodologies to solving problems.
Also, read these:
This is a horizontal progression. You move away from your current job and career to a new full-blown career in machine learning.
Here is my advice for you.
Many starters, in an attempt to transition into the machine learning field, find it difficult to actually achieve their goal.
One of the reasons is the fact that they fail to plan and strategise.
Your strategy should include the following steps:
Firstly, learn machine learning.
Secondly, specialise in your desired area.
Thirdly, start early with practical use cases and projects.
Lastly, build your portfolio and launch out.
It’s not just enough to go out there and start learning python, R, coding, etc., you need to develop a strategy.
What does a machine learning career skill acquisition strategy mean for you?
First of all, you need to start with a clear learning strategy. In fact, this should be an offshoot of your planning.
It boils down to satisfactorily answering the questions below.
In helping you out, I have also projected the plausible cases for each of the questions.
Each case points to different questions that will drive your overall strategy:
Question 1: Why do I need to learn machine learning?
- I am just being enthusiastic about artificial intelligence and machine learning and nothing more.
- I need machine learning tools to up-skill and be more productive in my current job given the increasing role of big data and analytics in my organisation.
- I want to make a complete career change and start a new career, especially in the areas of big data analytics, predictive modelling and machine learning.
Question 2: What career transition model works best for me?
- I need a vertical career transition/change.
- I need a horizontal career movement.
Question 3: What category, type or specialization of machine learning do I concentrate on based on my ultimate career change goal?
- I want to start machine learning broadly first and then specialize later when I would have understood its scope and dimensions.
- My current job requires I learn and specialize in supervised, unsupervised, deep learning, etc.
Question 4: How exactly do I go about learning it? What channels of learning are best for my current situation?
- My schedules are busy so I am going for self-phased online courses and resources.
- I don’t have enough resources to dedicate to machine learning, so I don’t mind being a self-starter.
- I am available for a Bootcamp hands-on training learning environment.
In addition to the above, a carefully mapped-out strategy will reveal to you the critical learning paths, timeline and indicators needed to benchmark your learning outcomes.
Now it’s time to rebrand yourself towards your new career path
After you have equipped yourself with the necessary tools of machine learning and gained some level of expertise, it’s time to rebrand your professional image towards machine learning.
You don’t have to wait until you actually know everything about machine learning before you start building your professional goodwill.
Start branding yourself.
1. Do you need to change your job title?
Even though this might not really have major changes in your quest for career branding, if it’s what you are comfortable with, do it!
Go ahead and change your job title as long as it reflects your job responsibilities and skills. It doesn’t matter which industry you are in.
Do it as long as your daily job activities involve the use of machine learning tools and methodologies in providing solutions.
2. Update your job responsibilities
This is very important. You may have to describe what you do in a way that matches the core job responsibilities in the machine learning field.
The language, terms or jargons might change. You may have to look at this from the viewpoint of value you are now adding to your organization.
Are you applying machine learning tools on the same job? Are you solving the usual problems with new methodologies? Then reconstruct your job responsibilities and let them define your career in the light of machine learning.
Please, note this. Your job responsibilities don’t have to change in the sense of broad job expectation and deliverables.
What you might watch out for here is the approach you now adopt in carrying out these responsibilities.
Has your approach or methodology to the usual job significantly transitioned from the conventional to the AI-driven or machine learning-conscious tools?
Then redefine yourself in the light of this development.
It’s time to start rebranding your existing portfolio and building new ones. Remember, nobody will take you seriously just because of your job title or even responsibilities.
You will only be reckoned with when the potential employers or your new career network contacts start seeing the output of your work.
One of the best ways to achieve this is to document your product, reports, codes, projects, etc. This is where a great and relevant portfolio comes in.
Apart from showcasing your skills and other qualities, building a winning portfolio also establishes your authenticity and authority.
4. Other professional identities
Touch your CV and resume to reflect your new job roles and responsibilities.
Look at the industry CV format and adjust your profile appropriately. Emphasise how your career progression has assumed a transition into machine learning applications, roles and responsibilities.
Ultimately, make sure you are guided and lead by sincerity and openness.
# Social media:
Even though this isn’t the first place to go when you haven’t made a significant improvement in your career transition goal, it is the most interesting part of the ‘show.’
This is where your efforts, so far, at making a career transition begin to be rewarded, if well executed.
Tell your friends, followers, colleagues and other networks. Let people know you are genuinely available for a machine learning career role.
Start networking like crazy: join the move. Attend conferences, seminars and career meetings related to your new role.
Contribute to discussions on social media and other related platforms. This is where and how to establish your ‘arrival’ and authenticity. Join social media career groups.
You have explored necessary information and strategies you can’t undermine if you are interested in changing your career to a machine learning role.
It is safe, therefore, to conclude with the following hints:
First, you need to decide which approach suits you best: vertical or horizontal transition.
Second, ask yourself some critical questions to ascertain which route is best for you.
Third, develop a workable plan and clear strategy.
Lastly, don’t wait until you master everything in machine learning. You can start rebranding yourself as soon as you have gained some level of competence.
- 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
- 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
- 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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