When you’re new to a profession, you don’t have a lot of domain expertise, which might lead to blunders. If you’re new to machine learning, the same thing will happen.
Machine learning is a large field. It’s doubtful that you’ll know all there is to know about machine learning on day one, and that’s fine.
So, today, we’ll go through the top five machine learning mistakes a machine learning beginner makes and how to avoid as a newbie.
Mistake 1: Not Understanding Machine Learning Fundamentals:
In our opinion, this is the most common error made by newcomers.
Beginners frequently study certain Python or R machine learning libraries and then claim to have learnt machine learning or data science.
However, when asked to analyse their machine learning model or explain their results, they will fail.
This occurs because novices prefer to disregard theory as a component of machine learning, as well as its other requirements such as mathematics and statistics.
It is critical to grasp at least some key theoretical concepts. You will only be dealing with black box ML libraries and tools if you do not understand theory.
However, if you want to continue in this sector in the long run, this will not help your career.
Mistake 2: Beginning with Poor Data:
One thing to bear in mind is that data is quite important. Understanding the methods and principles of machine learning is insufficient.
The majority of the effort in developing a model is done at the data and feature levels. It is preferable to focus on data rather than method since your data and its properties will ultimately shape your model.
The quality of your final model will be entirely determined by the data rather than the algorithm.
This is one of the most common mistakes made by machine learning beginners.
While upgrading algorithms is typically portrayed as the glamorous side of machine learning, the harsh reality is that the vast majority of effort is spent preparing data and dealing with quality concerns. The accuracy of your models is dependent on the quality of your data.
A whopping 80% of the time is spent on the project’s early phase, which comprises data cleansing, segregation, and so on.
Mistake 3: Too Much Time Is Spent on Theory:
Yes, this is also an issue.
You can’t expect to achieve a decent body by memorizing exercises and never going to the gym.
You can’t expect to become a master of the English language by memorizing the dictionary.
You can’t merely learn math formulae and ideas and expect to be able to answer any problem.
You get the picture.
Many students spend far too much time on theory and far too little time on practice. This is why many students become discouraged when they are unable to solve a challenge.
The amount of time you should spend on theory vs. practice is a delicate balance. This will differ from person to person, but you must strike this balance. You might start by maintaining this 50-50 and then alter it based on your personal tastes.
Mistake 4: There is no proper learning plan.
This is a significant disadvantage, particularly for someone who intends to become a machine learning specialist entirely by self-study.
We’re not saying it’s impossible, far from it; it’s just a little more complex and time-consuming.
When learning something new, you must have a solid structure/game plan/learning plan in place. Without this, there is no correct framework to the learning process, and you will forget half of what you learned.
If you intend to self-study, you need conduct a lot more research online to locate a framework that looks appropriate to you. We live in an information era, and you can readily locate free or paid materials to help you learn quicker.
Mistake 5: Giving Up Too Early:
Machine learning and data science have a high learning curve, and not everyone is able to persevere.
But that’s why we’re here: to advise you that you’ll have a few issues at first, and that’s perfectly normal.
Even persons who are now “Industry Experts” had difficulties when they first started out.
It’s all part of the procedure.
To be honest, there is an entry hurdle in this industry, therefore feeling overwhelmed as a beginning is very acceptable. So, if you’re about to give up on machine learning, remember this quote and you’ll be much more inspired.
Thus, this are the 5 common mistakes as a machine learning beginner and the ways to avoid them.
