Today in this Article I will be explaining what are the top skills required to become a machine learning Expert.
Now, the skill sets can be broadly divided into hard skills and soft skills To become Machine Learning Expert. First, let’s talk about one of the hard skills that would be required to kind of become an Expert.
Hard Skill
Data science is such a big field that you can almost be at any part of your hard skills journey, and you can still be able to make it.
But good software engineering skills are sort of not essential, but would be definitely be helpful. If you have some background in coding.
1. First Hard Skill
Python And R that will be a real real benefit when you start out in your data science journey, If you Want to Expert in Machine learning So You have to learn Python programming to become An expert.
2. Second Hard Skill
The second hard skill that is sort of required is your maths, understanding of maths and statistics, I would say, if you have some background in statistics, I would definitely think that’s a really good place to kind of start To becoming machine learning Expert.
Soft Skill
So those are the sort of hard skills that would be required maths and sort of software engineering, to get you started in data science.
if you now look at that soft skills, I think those are more important when it comes to data science journey.
1. Learning Ability
Top most skill in data science is a learning ability, And this is purely because the field has been everyday changing since probably last five, six years.
Data science as a field every day, there’s new research that is being published, there’s new capabilities that are being used new software’s that come into existence.
So you have to be definitely be someone who’s good at learning, if you’re someone who’s just probably learning at one go, and then kind of forgetting about the entire thing over a longer period of time, probably data science might be a really, really tough place to make your career.
And so learning abilities, the most important thing that I would say, after that, I think apart from learning ability.
2. Scientifically Thinking
The second soft skill that is very important for a data scientist, or I would say machine learning Expert, is the ability to kind of think strategically or scientifically thinking, and why is this important?
If you’re solving a problem and your models don’t give good results, you don’t have high accuracies, then you need those kind of strategic thinking or analytical thinking to understand where your model is sort of not working, right?
And what are the next steps to do it. I mean, I have seen those kind of data scientists or machine learning guys who kind of throw data into sort of all sorts of algorithm, and then they’re just breaking their head over why’s that algorithm not working? Right?
So if you have to avoid that you need really a good analytical thinking mind. Because that’s something that is very critical.
You have to at every step, you think that okay, if this is a problem, what are the different ways to solve it? If these are the different ways to solve it, how can I prioritize each of them?
What is the effort required on all of that, so at every point in time, you need to be thinking very strategically and very analytically.
If that’s something that’s coming naturally to you, data science is definitely the place to be. So that’s the second skill.
3. Curiosity
Third soft skill is very important and crucial to the entire journey is curiosity. that’s mostly because, as I said that data science is sort of a field that is changing every day.
So you have new research papers coming out every other day. Now, if you are someone who’s not curious, you would not develop this habit of reading papers up every day.
For example, at one point in time, I was reading up to three research papers every day, and I had made a habit of kind of making sure that I read one paper every day.
So you really have to enter into those habits. And those sort of become more natural to if you are someone who’s curious, if you have this curiosity about learning,
you know, what is happening in some other field that also helps in kind of thinking cross domain cross domain is basically, when you have sort of reading,
if I’m solving a problem in medical imaging, I’m not necessarily always reading papers, which come out related to medical imaging in AI,
I’m reading up other places where AI is being used for some very different problems, and trying to understand how some of those techniques can be transferred into medical imaging.
And that’s exactly the point where you could start making some of the differences. And for example, I would not only be reading AI papers, I will also be reading up very old medical imaging papers, people who did some very different kind of processing, which was not even closely remote to AI.
And when you read all of these papers, that’s when you actually can innovate because innovation is not something that you see, you just take on your desk, you sit with a cup of coffee and say, Oh, let me innovate and that’s how you know but it’s never like that, right?
You need to have knowledge you need to have knowledge across various different domains, which are you know, very widely different, and that’s when you know all of that.
That’s the exact point where innovation starts happening. So and to do all of that, I think curiosity I would tend to think is a very, very important skill.
So if you’re able to kind of have all of those three soft skills, and then you have got your maths and software engineering part covered, I think you’re great to go and even if you don’t have your hard skills as much cover upgrade definitely has the courses that are available right now here you can go and check out the link below.
But soft skills you need to develop them to get very successful in machine learning and AI. And if you have any further skill set related question, you know, what are the different other kind of job related questions?
We will be more than happy to answer them, please write them down in the comments. And if you found this Article useful, like, share.
