Machine learning engineers are responsible for all the smart features we get in technology nowadays. They are on the front side of everything related to AI so you can only imagine how exciting it is to be one of them.
If you want to become a machine learning engineer you need a set of skills and qualities, ones that define you as a person who’s ready to take on all the challenges in the complex world of technology.
In today’s article, we’ll tell you exactly what skills you need so you can work on them before you apply for the job. Doing so will increase your chances of landing that job and hopefully help you become the best machine learning engineer you always wanted to be. Let’s take a look.
1. The basics of computer science and programming languages
To become a machine learning engineer and land a sweet job at a reputable company, you need to be familiar with data structures (graphs, trees, queues, and stacks are just a few of the examples), algorithms (sorting, optimizing, searching, and dynamic programming), computer architecture (memory, deadlocks, distributed processing, and bandwidth), as well as computability & complexity. These fundamentals need to be applied while you are programming. You also need to be able to address them and adapt them depending on the task.
If you want to practice the right way, local competitions and coding fests are the best way to do so because they are closest to the real thing. Wondering where to find free datasets? Feel free to learn more here.
2. A good amount of knowledge in the probability and statistics field
The foundation for many machine learning algorithms is probability, likelihood, Hidden Markov Models, and Markov Decision Processes. Along with probability, you need to be familiar with the statistics field.
As a machine learning engineer, you will be using these things to deal with the uncertainty of real world situations. For example, if you are working for the financial department, you will put your knowledge to use to reduce the risk of transactions and investments, as well as other business operations. You should read more about statistical machine learning if you are not yet familiar with it.
3. Experience with System Design
Even though there’s a lot more going on in the background, the final goal for a machine learning engineer is to deliver a piece of software. Your system design needs to be intuitive and it shouldn’t be the limiting factor for when the data starts increasing in volume. You should know how to work with version control, you should know how to properly test before delivering, how to read and understand documentation as well as deliver it to your clients when you ship your product, and much more. These things are especially important when you are collaborating with multiple people on the same project.
4. Knowledge of Python and R – At least these two languages
When you need to do some data analysis, the best way to do so is by learning Python and R, two different languages that are amazing for machine learning. R specializes a bit more in statistics, while Python is known to be a really easy language to learn syntax-wise and is great for general-purpose coding.
Both of these are great and they can be used one with another, so we suggest learning them both since most companies will consider this as an advantage, meaning you’ll find a job easier and have an advantage over other competitors and employees. But, if you are looking to start with one, we suggest R if you are looking to build beautiful graphs. Setup R studio and you’re good to go.
Python has so many functionalities and most engineers are already familiar with it before they apply for a job. But, if you are not, we suggest adding it to your list and learning it as soon as possible.
5. Applying Machine Learning Algorithms
To properly apply a machine learning algorithm, you must choose the right library, package, or API, but that’s not the only thing. You also need to choose a suitable model, a learning procedure that can fit the data, and have a deep understanding of how the hyperparameters will affect the learning curve. Your approach also needs to be something you are familiar with because you will be facing a lot of obstacles along the way. And, while we are at obstacles, the final skill is nerves of steel.
6. Patience and nerves of steel
Being patient is important for a machine learning engineer. This job is filled with room for error and now and then you will be facing things that can set back your progress for entire days, sometimes even weeks. Version control errors or someone messing up the project are just a few of the negatives you’ll be facing. It’s sometimes a stressful job but if you don’t give up during the beginning phase which is always the hardest, you’ll make it in the industry.
Conclusion
Our world is nothing without technology and the IT field is constantly changing at a really fast pace, meaning that our world is in huge need of machine learning engineers and programmers now more than ever.
Machine learning engineers are the ones building the complex systems that make our lives so much easier. However, becoming one is not easy. In fact, according to a few surveys, it’s one of the most difficult professions nowadays, but also one that pays incredibly well.
If you are looking to become a machine learning engineer shortly, now’s the right time to start practicing and honing your skills. Above you can find the six most important ones you need to become one.
