Introduction
So are you trying to up-skill yourself to keep up with the market shift towards machine learning, data science, artificial intelligence? But you are still a beginner and trying hard to get a hold on this seemingly complicated field. In spite of all the hard work that you are putting in, are you unable to make much progress or get desired results?
You are definitely doing some mistakes which are very common within beginners in this field. So it is time to do a round of introspection.
Let us go through some of the most common mistakes which machine learning or data science beginners make and see if you are making one.
Mistake #1 – Caught up in the AI hype
Artificial Intelligence is supposed to steal our jobs, Data Science is the sexiest job of the 21st century – you all would have come across such statements in media. But these are far away from the truth and is more a hype !! This over hype is actually attracting so many people towards data science and machine learning.
The harsh truth is that this field is neither sexy nor will give you a god-like power to create artificial intelligent shown in movies. So if you are entering this field after making such wrong perception towards machine learning and data science then you are making a mistake and you will most likely be left disappointed.
So I will recommend you not to get carried away with this hype but first do a reality check of how this field works, what it can offer you and if this is really your cup of tea. And only then make a correct informed decision of entering this field otherwise it will be a waste of your time and effort.
Mistake #2 – No Theoretical Knowledge
This is the biggest mistake beginner make in my opinion. It is very common for beginners to learn certain Python or R machine learning libraries and then they start claiming that they have learned machine learning or data science. But ask them to interpret their machine learning model or explain their results they will fail to do so.
This happens because beginners tend to avoid theory part of machine learning and its other prerequisites like mathematics and statistics.
Of course the learning curve of machine learning and required mathematics, statistics is huge and it will require too much time and effort to acquire a thorough knowledge. But it is really essential to grab at least some important theoretical aspects. Without theory, you will just be working with black box ML libraries and tools. But this would not do any good for your career if you want to stay in this field for the long term.
Mistake #3 – Spending too much Time Only on Theory
This is completely opposite to what we discussed above. Sometimes beginners try to study the in-depth theory of each and every area of machine learning, mathematics, and statistics.
This is not an easy task, it might take months to get familiar with a particular subject. And here we have multiple subjects to work with. With this plan, beginners lose interest midway even before reaching a stage where they can apply machine learning practically.
Unless you are into academics or planning to do some research it is not really required to go extra deep into these topics. Between no theory at all and too much theory, a line has to be drawn midway. Otherwise, it will take months if not years to cover all these subjects in great detail. Assuming that you are looking for a faster career shift, this learning approach is a big mistake.
Mistake #4 – Sequential Learning Plan
This is somehow related to the previous mistake which we discussed. In this case, beginners try to plan their road map to machine learning in a sequential manner. For example a typical sequential learning plan might look like- linear algebra -> calculus -> probability -> statistics -> machine learning -> programming language -> practical application.
The plan looks perfect on paper but the reality is that with such a rigid plan the beginners get caught up so much with the prerequisites that even before reaching machine learning or its practical application they become overwhelmed and leave their journey midway.
My suggestion is to make a flexible learning plan. Identify a specific topic of machine learning for e.g. regression- spend time on its theory, learn the maths behind it, see how it can be applied practically using the programming language of your choice. Once you are comfortable with regression you can pick up the next topic, say classification. This type of learning approach will always keep the beginners interested.
Mistake #5 – No Learning Plan
In this case, the beginners don’t have a learning plan at all. Instead, they try to consume internet material like blogs, videos just randomly. They don’t even have a clue of what all topics are there in machine learning.
If you want to self learn, then to avoid such a messy situation you should, first of all, get hold of a machine learning book and try to understand what all topics are there for you to study. Structure your plan according to these topics and then start looking for online materials relevant to these topics one by one.
One step better would be to get enroll yourself in either free or paid courses. It will not only give you a systematic learning but will also help you to learn fast.
Mistake #6 – Relying too much on Courses and Certification
There are no dearth of machine learning, data science courses in the markets nowadays. Right from the free courses on MOOCs, to the premium paid courses offered by various platforms, institutes and educators – there was never so much choice of learning resources earlier !! Courses are a great way for beginners to get some hand-holding and initial push to enter the world of machine learning.
But the mistake which beginners make is that they think course completion certificates are enough to get them jobs in ML and Data Science. Unfortunately, reality is far from this. You are not alone doing such courses. There are thousands of others like you who are doing similar courses. So you need to do something extra to stand out from the rest.
So once you have got a hold on machine learning you should start participating in competitions, write blogs, be active on online forums, do your own projects and put them on Github. All these will not only give you a personal boost but will also give you something extra to put on your resume and show to your prospective employers.
Mistake #7 – Competition and Real World are not Same
So you have picked machine learning basics very well and have started participating in competitions. This is definitely a good way to test yourself and become more visible in the market. But competitions can become misleading in the long run for beginners.
In competition the data set presented to you on the plate is clean. Generally, in the industry, it is not so easy to get a clean data set in the first place. There are various data collection and preprocessing steps that are needed here. So beginners do not build the aptitude of data preprocessing which is really required in real-world projects.
Also in competition, the target is to reach the best accuracy possible even to 3rd or 4th decimal places. But in real-world your client or business would not be interested in such a fine precision of your model. For them an accuracy of 87.689 and 87.631 is same. They would only be interested in you to explain the decision-making behavior of your model.
So as a beginner you should try to look beyond competitions. You can try to get into some internship to get a real-life industry experience or build your own cool projects.
Mistake #8 – Giving up too soon
Machine learning and data science are multidisciplinary fields. The learning curve is steep, but at times this learning curve becomes too overwhelming for some beginners and they leave it midway.
There is indeed an entry barrier in this field, to be honest, so it is perfectly normal to feel overwhelmed as beginners. I do understand that at times you will feel completely clueless at these topics in beginning but just hold on to it and don’t give up.
Remember that even the machine learning model has to make hundreds and thousands of attempts (epochs) to reach a good accuracy, so you too should keep on trying it and you will definitely succeed in getting a hold on the nitty-gritty of machine learning.
- Also Read- Regression vs Classification – No More Confusion !!
- Also Read- Supervised vs Unsupervised Learning – No More Confusion !!
In The End…
The path of machine learning and data science is not easy, it is hard but it is not impossible. You are not the only beginner who is making mistakes but the trick is to identify these mistakes and take the correct path as quickly as possible.
There are many like you who had started as beginners and have successfully transitioned their careers into machine learning and data science. You too would be one of them soon. So good luck for your journey !!
