28 October 2020 /

Top Skills Any Aspiring Machine Learning Engineer Needs To Know

According to TopDev’s 2019 Vietnam Developer Report, Machine Learning/ Artificial Intelligence (AI) is currently the technology that draws the most attention from developers. With Machine Learning, you can easily snatch a high paid position in any industry, such as Technology, Finance, Healthcare, Retail and Customer Service, to name just a few!

But have you ever wondered what it takes exactly to achieve a Machine Learning Engineer position? Well, read on a few lines and we’ll uncover the top skills you need to be qualified in this growing field.

1. Computer Science Fundamentals and Programming

One of the first and foremost skills for anyone wanting to work in Machine Learning is computer science fundamentals and programming. More specifically, this includes data structures, algorithms, computability and complexity and computer architecture.

In order to achieve this, you can seek practice in building personal projects, or join competitions and hackathons.

2. Probability and Statistics

The job of a Machine Learning Engineer is quite similar to that of a Data Scientist, in the sense that both roles involve working with vast volumes of data, and this pretty much explains the next two skills.

In order to provide a clearer picture of the real world, a Machine Learning Engineer needs to have profound knowledge in both probability and statistics and provides various measures (mean, median, variance, etc.), distributions (uniform, normal, binomial, Poisson, etc.) and analysis methods (ANOVA, hypothesis testing, etc.) that are necessary for building and validating models from observed data.

3. Data Modeling and Evaluation

Data modelling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns and/or predicting properties of previously unseen instances. A key part of this estimation process is continually evaluating how good a given model is.

Depending on the task at hand, you will need to choose an appropriate accuracy/error measure, an evaluation strategy. Iterative learning algorithms often directly utilize resulting errors to tweak the model (e.g. backpropagation for neural networks), so understanding these measures is very important even for just applying standard algorithms.

4. Applying Machine Learning Algorithms and Libraries

Even though you can find common implementations of Machine Learning algorithms on libraries, packages or APIs, in order to utilize them to the fullest extent, you need to not only find a suitable model, but also an appropriate procedure to fit the data.

But that’s not all, you also need to be aware of the relative advantages and disadvantages of different approaches, and of course, watch out potential booby-traps (bias and variance, overfitting and underfitting, missing data, data leakage, etc.).

5. Software Engineering and System Design

To be honest, the outcome of any typical Machine Learning Engineer is software, which is often just a small gear of a much larger ecosystem of products and services. That’s why you need to be able to know how to make multiple gears fit together and cooperate with each other perfectly.

As the data volume will eventually scale up, you need to design the system carefully to avoid going back and forth fixing your own algorithms. Software engineering best practices are invaluable for productivity, collaboration, quality and maintainability.

And that’s the 5 skills you need in order to acquire a Machine Learning position! If you want to unlock your coding potentials and achieve your Machine Learning dream this year, then CoderSchool would be more than happy to support you in this journey!

Check out our beginner-specialized curriculum right here.