5 Most-Common Machine Learning Mistakes to Avoid as a Beginner

When you’re new to any field, you don’t have a ton of domain knowledge & which might lead you to make some mistakes. The same will happen if you’re a machine learning beginner.

Machine learning is a very vast domain. It’s highly unlikely that you’ll know everything about machine learning on day 1, and that’s okay.

So today, we’ll be discussing the 5 most common machine learning mistakes to avoid as a beginner.

1. Not Understanding the Basics of Machine Learning

This is the biggest mistake a beginner makes in our 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 when you 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 as part of machine learning and its other prerequisites like mathematics and statistics.

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.

2. Starting With Bad Data

One thing to keep in mind is that data plays a very crucial role. Grasping the algorithms and concepts of machine learning is just not enough.

When building a model, most of the work is done at the data and features level. It is better to concentrate a little more on data than on the algorithm because your data and its features will shape your model in the end.

The quality of your final model will completely depend on the data than the algorithm.

This is one of the most overlooked errors that machine learning beginners make.

While improving algorithms is often seen as the glamorous side of machine learning, the ugly truth is that a majority of time is spent preparing data and dealing with quality issues. Data quality is essential to getting accurate results from your models.

A remarkable total of 80% of the time is spent in the initial phase of the project - which includes cleaning the data, segregating it, etc.

3. Spending Too Much Time On Theory

Yes, this is a problem as well.

You can’t just memorize exercises and expect to get a good body, without ever visiting the gym.

You can’t memorize the dictionary and expect to suddenly be a master of the English language.

You can’t just study the formulas and theories for maths and expect that you’ll be able to solve every problem.

You get the idea.

A lot of students spend too much time on theory, and not enough time practicing that theory. This is why a lot of students feel disheartened when they aren’t able to solve a problem.

There’s a fine balance between the amount of time that you should spend on theory vs. practice. This will vary from person to person, but you need to find this balance. You can begin by keeping this 50-50 and then adjust it according to your own preferences.

4. No Proper Learning Plan

This is a major downfall, especially for someone who’s planning to become a machine learning expert completely through self-study.

We’re not saying that it’s impossible to do that, far from it, it’s just a bit more difficult & time-consuming.

When you’re learning anything new, you need a proper structure/game plan/learning plan of how you’re going to learn. Without this, there’s no proper structure to the learning process and half of the time you won’t even remember what you learned.

If you’re planning to self-study, you should definitely be doing a lot more research online to find a structure that seems suitable to you. We’re living in an age of information & you’ll be easily able to find free or paid resources that will help you learn faster.

You can check this out, it’s a lot cheaper than most of the paid courses out there (and a lot more valuable too): Machine Learning and Artificial Intelligence Certification Course

5. Giving Up Too Soon

Machine learning & Data science have a very steep learning curve and not everyone is able to stick through with it either.

But that’s what we’re here for, to tell you that you’ll face a few problems at the start, and that’s completely fine.

Even the people who are “Industry Experts”  now, faced problems when they were starting out.

It’s part of the process.

There is indeed an entry barrier in this field, to be honest, so it is perfectly normal to feel overwhelmed as a beginner. So, whenever you feel like giving up on machine learning, just remember this quote and you’ll feel a lot more motivated.

Most people overestimate what they can do in one year and underestimate what they can do in ten years. -Bill Gates

Mistakes are an integral part of the learning process — we should embrace them as they are the engine that drives us onwards and upwards.

Good luck!