Machine learning is a stairway to success. There is no doubt in that. However, the method of using this stairway is often shaky and unstable.
Just to start or people just out of college start their machine learning career by making all the wrong decisions. From trying to cram every algorithm to giving less importance to strategical, analytical skills, every mistake just leads to wasted time and low output. Hence, in this article, we will discuss the common mistakes made by beginners and methods to correct them.
7 Common Mistakes That Amateur Make in Machine Learning
1. It Is All Theory
Most of us make the mistake of starting by learning theory, machine learning aspects, math, and complex algorithms. But, the right approach is to just start and then move your way up. You don’t have to know all the textbook and algebra first to become a machine learning professional. Yes, you may have to do a Ph.D. for future growth, but this can wait.
Just take a step back and flip the model. Understand how predictions are made, what goes behind model problems, and how future trends are analyzed. Practically absorb theory you are trying to learn.
2. Being A Know-It-All
Undoubtedly, machine learning is a huge field with various domains such as deep learning, artificial intelligence, and neural networks. The field is so massive that you could spend years understanding everything and you still won’t know it all. Hence, just stop wanting to be a know-it-all and pick an area. Take deep learning, for example. Then, know everything there is to know in this domain. Keep narrowing your area for dedicated learning.
3. Cramming Algorithms
Algorithms are not simple – these are, in fact, a full ecosystem. Every algorithm in machine learning is complicated, has hyperparameters, and a certain set of outcomes. But, you don’t have to get lost in algorithms. These algorithms are just a means of getting results. So, start by processing and understanding how an algorithm works to produce the results it gives. Then, proceed towards understanding the complicated aspects.
4. Changing Tools
Don’t dive in and start learning a new tool or language in every 5 days. This is closely related to how you don’t have to be a know-it-all. Similarly, here don’t try to understand every tool and language in one go. Proceed with one tool or language, and maybe later you can learn other things. But initially, focus on a few things only.
5. Running After Degree
While getting a dedicated degree in the field is imperative, most diploma courses cut-short information. So, you can get a certification, degree, or diploma. But, don’t lose focus on the core aspects of machine learning. You should be more inclined towards learning as this is the one thing that will help you penetrate the market. You can become a machine learning specialist even without a degree. But, having less knowledge with a degree doesn’t work in the same manner.
6. No Problem-Solving Skills
The problem-solving skills help you achieve a solution in a structured manner. Here’s how it goes:
- Breaking the problem into multiple logical parts that can be solved one-by-one.
- Evaluating the problem from a wider perspective and knowing its larger impact. This helps in finding a strategic approach.
If you don’t learn problem-solving skills, most of the times your work will be a total haphazard. Organizing the process and flow of finding issues is essential in machine learning.
7. Irregular Approach
Learning specific machine learning aspects for one month and then easily getting distracted to enter other domain of machine learning is not how you will achieve success. Machine learning is a sensitive study which requires persistent efforts and regular learning. You need to stay focused on one domain and learn it fully before entering anything else.
There are various other mistakes that we all make in our career. However, knowing these common loopholes only help you to overcome these challenges.