Here are the most in-demand soft skill sets you need to develop and horn as a beginner to have a successful machine learning career.
Is this the new hard skill?
Let’s start with this.
Technical skills are non-human skills. You need to be proficient at using them to effectively relate with your machine. And they aren’t hard to possess.
You can learn them right on the job or from the outside.
Therefore, to be relevant and be on top of the game, you need to constantly update yourself. You either learn or rust!
However, with soft skills, the story is quite different and interesting, too.
Machine learning job isn’t all about data, graph and coding. Your daily task also requires interacting with humans.
This makes soft skills primarily human skills for human interactions. These skills relate to your mind, emotions, instincts, character, reputation and all forms of human-centric qualities.
You need them to relate better with fellow humans.
Unlike technical skills, soft skills have a high rate of being transferred from one project or job to the other.
Therefore, as crucial as technical skills are, machine learning career starters need to start developing their soft skills.
In addition to technical skill development, to build a lasting foundation in the machine learning profession, you need to dedicate your time to developing and acquiring key soft skills.
Here are the core soft skills you need to horn as a machine learning starter.
1. Communication Skills
Without a doubt, excellent communication skill is one of the most highly sought-after skills, especially in machine learning job.
Communication skill, when well demonstrated, makes machine learning job seekers instantly attractive to his/her prospective employer.
This skill includes the ability to communicate and present your idea or technical result effectively to others who, in most cases, aren’t interested in your technical jargons.
This skill is highly essential at every stage of machine learning development.
The better you are at demonstrating this skill, the more valuable you make yourself. And the earlier you learn this the better.
You just need to know how to communicate your reasoning or position in a clear, logical and easy-to-understand process.
What makes communication skill critical to the successful execution of machine learning projects is because communication channels are both within and outside your team.
First, developing strong communication skills will help you and your team to work seamlessly and have a common understanding of the project.
Also, machine learning projects don’t end when the technical stuff is perfected. You need to communicate the value-add to your client. And if you are the team lead, this is a must-have quality.
#1. If you don’t have a clear understanding of the project at hand, you can’t communicate it well. Worse still, If you don’t understand the project from the use-case perspective, your communication won’t connect well with your client.
#2. The best way to learn how to communicate well is to try and understand the ultimate goal of the project, especially from the client’s perspective. Break it into pieces and connect the intelligence and insights in-between the lines.
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2. Time Management Skills
Here comes one of the most desired of all qualities. Everything in life is time-bound.
Developing a machine learning project is literarily racing against time.
In a machine learning career, the need to efficiently manage your time cannot be overemphasized.
Everything must be delivered on time. The client can’t wait. Your team lead or coordinator is after timely delivery.
From the conception of the project idea to the final day of delivery, time will always be the enemy. There is a tight deadline every time. And everyone is expected to beat it!
For this, as a machine learning beginner, you need to be apt at prioritising tasks by assigning the right amount of time to every responsibility.
In doing this, don’t underestimate the time required to complete the project.
In most cases, you will need more time than earlier allocated.
#1. In the actual sense, no one can manage time. All we can do is try as much as possible to manage ourselves within the time framework.
#2. Get a fair understanding of the project objectives. Dimension the project and don’t underestimate the required effort and time.
#3. Work on the project bit by bit as scheduled. Don’t be distracted by the perceived complexity of the project.
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3. Team Work Skills
Needless to say, a machine learning career entails working as a team. You aren’t going to be a Lone Ranger. No one works in isolation.
Delivering a machine learning project or product takes a collective effort. At every stage of the production cycle, the seamless integration of efforts as a team is the most needed quality.
It’s a typical display of the division of labour towards achieving a common goal.
For this, you’ve got to be a good team player. You must be able to understand the whole essence of the project from the inception.
You must identify the linkage between your immediate role and the overall project.
How do you make your team’s work less complicated? How do you assist other members of the team who need your assistance? How do you keep the ultimate goal of the team’s project at heart while delivering your quota?
This is the only way you can make yourself valuable -and desirable too- as a member of a team.
In most cases, you must be ready to collaborate with other professionals within and outside your immediate team.
You are fulfilled not only when you complete your role, but real fulfilment comes when your client is satisfied with the overall project.
That is when you win.
#1. Always approach the machine learning project as a collative exercise. Don’t just do your part. Help others achieve theirs.
#2. It’s not only about what you can do alone. It’s about what the team can achieve together. This is the path of leadership.
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4. Leadership Skills
I know this might sound strange, but no one is too young or inexperienced to lead.
Leadership is of the heart and spirit.
The need for leadership spirit is best cultivated when you are starting out as a machine learning career beginner.
You have to cultivate leadership traits right from the beginning of your career. Being a leader starts with you. It’s all about you believing in yourself and your capability.
It also translates to believing, motivating, encouraging and guiding other members of your team.
Sometimes, a machine learning project doesn’t go as planned. Things happen. At this period, leadership is required. Somebody must stand up and restore courage and hope.
#1. The best time to learn the act of leadership is when you have no follower. Lead yourself first.
#2. You have to cultivate leadership traits right from the beginning of your career.
#3. Learn how to be a problem-solving and supporting follower. You don’t need any other quality to be a great leader.
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5. Critical Thinking and Creativity Skills
This skill isn’t just for the machine learning profession, it is one of the most needed skills virtually in all professions.
Critical thinking is a process of evaluating facts and considering likely solutions to the identified problem.
Exploring alternative perspectives or dimensions to an identified issue while asking questions that reveal salient positions and logic will ultimately improve your creativity and critical thinking.
Developing this skill will not only help you in solving a seemingly complex problem, but it will also position you as a key member of your team.
Now, this has nothing to do with being the leader. It is all about problem-solving.
While innovation is at the very heart of creativity, you don’t actually need to change the status quote or look beyond the box to be a creative person.
All you need is to bring a fresh perspective that helps solve the problem.
You can improve the existing product, explore a more time-efficient way of delivering the project or reduce the overall cost of the project. This is what innovation means!
#1. Always ask ‘why’ and ‘why not.’ You will never know how many ‘goods’ you are doing to yourself and your team.
#2. Subject your position to open criticism and harvest productive inputs and perspectives.
#3. While innovation is at the very heart of creativity, you don’t actually need to change the status quote or look beyond the box to be a creative person.
Lastly, go deeper into machine learning.
Again, you might find these under-listed articles resourceful:
- 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
- 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
- How To Build A Compelling And Winning Machine Learning Portfolio That Will Get You That Job.