How to become a Machine Learning Engineer

Machine learning has a wide range of business uses and promises to provide even more opportunities in the future, making it a booming job field. In its “Future of Jobs Report 2023,” the World Economic Forum finds that Machine Learning Specialist is the fastest-growing of all occupations. It reports, “Demand for AI and Machine Learning Specialists is expected to grow by 40% or 1 million jobs, as the usage of AI and machine learning drives continued industry transformation.” Working in this field can also be very lucrative; Indeed reports that a Machine Learning Engineer has an average salary of $164,000. Further, machine learning roles provide opportunities for working with cutting-edge technology, which can be both exciting and fulfilling. Overall, there are a lot of reasons to become a Machine Learning Engineer.

Machine learning is a technical career, and most people need to take formal classes to gain all the skills and knowledge that will prepare them to work as a Machine Learning Engineer. While every person learns differently, many experts say that you should plan on spending at least six months to learn the skills you’ll need and a year or more to master everything.

Recommended: Data Analytics Certificate
Recommended: Best data science classes in NYC
Recommended: Best data analytics classes near me
Recommended: Best AI classes and certificates in NYC

What you’ll need to learn to become a Machine Learning Engineer

While every training program will have its own guidelines and curriculum, there are some fundamental skills you’ll need to learn to break into this field. These include but are not limited to:

Technical skills

• Mathematics: To work in machine learning, you’ll need strong mathematics skills, including statistics and probability so that you understand and work with machine learning algorithms.
• Coding: You’ll need advanced coding skills, too, since you’ll be working with code often. Python and R are two of the most popular machine-learning languages. Other languages you might need are C++ and JavaScript.
• Software engineering: To create machine learning systems, you’ll need an understanding of software engineering.
• Machine learning algorithms and frameworks: Algorithms and frameworks are tools that you’ll use for creating and training machine learning models.
• How to work with data: Machine Learning Engineers work with a lot of data, and you’ll need to learn how to clean, prepare, and use that data. This includes understanding how to use SQL databases.
• Cloud services: Machine Learning Engineers use cloud services to create, train, and manage their models. The scalability of these services ensures that solutions can adapt.

Non-technical skills

• Communication: Most Machine Learning Engineers work as part of a team, so they need to be good at communicating. 
• Problem-solving: As a Machine Learning Engineer, you’ll be continually working to find solutions to a variety of different problems, so problem-solving skills are critical.
• Adaptability: Technology in this field is changing constantly, and you’ll need to be able to adapt to these changes.

How to learn machine learning skills

College degree programs

Some Machine Learning Engineers gain their skills by pursuing a college degree. Popular degrees include data science, statistics, and computer science. A bachelor’s degree in one of these areas will take about four years to earn, and an advanced degree will take even longer. Tuition costs for each of those years of study vary widely depending on whether you attend a public or private school and whether you’re an in-state or out-of-state student; however, according to the Education Data Initiative, the average cost of tuition at a four-year school is more than $17,000 per year. It says that over the 21st century, the cost of attending college has become more than double what it once was. As a result, a college degree has become unaffordable for many people.

Professional training centers

You can also gain machine learning skills by taking a class at a professional training center like Noble Desktop, General Assembly, or Fullstack Academy. These schools offer accelerated programs that are designed to cover a comprehensive set of skills in a relatively short amount of time. They typically cost a lot less than college. In addition, many of these programs have a strong focus on getting students career-ready, so they can be a good option if you want to get a job as a Machine Learning Engineer after you graduate. In addition to extensive hands‑on practice designed to prepare you for real‑world projects, many programs provide career‑support services such as portfolio and résumé assistance.

When you take a class at a professional training center, you’ll often be able to choose between more than one learning format. Some schools offer both in-person and live online classes, allowing more flexibility for learners. There are also self-paced machine learning courses, which are even more flexible since they are asynchronous.

Putting your skills into practice

It’s one thing to take a machine learning class and another to use your skills in the real world. Experience is an essential part of becoming a strong Machine Learning Engineer. As you put your skills into practice, you’ll gain an understanding of how to apply them to real-world situations and how to deal with the many challenges that you’re likely to face. Most employers want employees who have both a strong skill set and solid experience. 

Build a professional portfolio

It is one thing to be able to say that you have machine learning training and another to be able to demonstrate that training with practical examples of your work. If you want to become a professional machine learning engineer, you’ll want to build your own professional portfolio that contains machine learning work you’ve done and gives your prospective employers a glimpse into the kind of programmer you are. These portfolios often include fully functioning algorithms you have developed or contributed to, serving as evidence of your capacity to begin working on machine‑learning tasks immediately.

The importance of staying up to date with new technology

Machine learning technology changes constantly and rapidly, and if you want to be successful and competitive in this field, you can’t just go to school once and then stop learning. Instead, most experts insist that you’ll need to embrace a mindset of constant learning, keeping yourself up to date on new technology and practices in this field.

Pursuing a data analytics certification or other type of certification relevant to your field can be a great way to stay up to date with machine learning practices. Popular certifications for Machine Learning Engineers include the Google Professional Machine Learning Engineer Certification and the AWS Certified Machine Learning - Specialty Certification. In addition to keeping you relevant, certifications are an effective way to display your knowledge and dedication to employers. A number of studies have found that employees with certifications are likely to earn more and also have a better chance of career advancement.