How to Become a Machine Learning Engineer: A Beginner’s Guide

Machine learning is a critical component of artificial intelligence systems, with which AI is more effective today. Many big companies utilize machine learning to predict outcomes in their business, develop new and improved products, and automate processes.

Therefore, enterprises that deploy AI solutions need machine learning engineers to make the most of these systems. So, how do you start a career in this exciting field? Here’s how to become a machine learning engineer.

Who Are Machine Learning Engineers, and What Do They Do?

A machine learning engineer is an IT professional who develops algorithms that enable AI systems to learn from input data and make predictions automatically using the data. Machine learning engineers work alongside data scientists to transform data into workable information (algorithms) for AI systems.

In an organizational setting, the ultimate responsibility of machine-learning engineers is to design and implement machine-learning models and improve on already existing ones. They analyze and organize large quantities of data, verify data quality, select appropriate data sets and representation methods, and transform data into machine-learning algorithms.

Furthermore, machine learning engineers design and test various systems, research and implement machine learning algorithms and tools, and execute the training and retraining of systems. Additionally, they extend ML libraries and develop machine-learning applications according to client needs.

How to Become a Machine Learning Engineer

Like any other profession, you must meet certain criteria before becoming a machine learning engineer. With sheer determination, you can become a sought-after machine learning engineer; all you need to do is follow these steps.

1. Learn Coding With Python

Machine learning involves a great deal of programming. Therefore, you should be fluent in major programming languages like Python, C++, and Java. However, you can start with one programming language, so you keep your brain clear of jargon. Python is a fairly easy programming language to start with.

Furthermore, must-know machine learning algorithms are used to develop machine learning systems, like deep learning algorithms. Hence, a good background in machine learning algorithms will serve you as a machine learning engineer.

2. Take Data Science and Software Engineering Courses

Like any other tech profession, you must learn the job’s technicalities and the required technical skills to become a machine learning engineer. Therefore, you must have a solid educational background.

Since machine learning engineers typically work with data and software programs, a degree in software engineering or data science is a great place to start. Additionally, machine learning engineers must possess advanced math and statistics skills as it helps them analyze data and develop machine learning algorithms, reiterating the need for a college degree.

Furthermore, you can take online courses on machine learning to get specific knowledge in the field. Here are some of the best online courses on machine learning you can start with:

3. Enroll in Bootcamps and Work on Real-World Projects

Springboard ML Bootcamp Screenshot

Bootcamps are a great way to gain practical experience in a field quickly. By enrolling in bootcamps, you get to work on projects allowing you to build field experience, which is of tremendous importance to employers.

Moreover, bootcamps are especially important because they help you learn the required technical skills and knowledge while gaining hands-on experience simultaneously. Furthermore, a bootcamp is a great avenue to build a good portfolio.

Likewise, you can take up personal projects and develop machine-learning solutions to real-life situations. Here are some machine learning project ideas for beginners. Here are some of the best bootcamps to solidify your grasp of essential machine learning concepts and practices.

4. Get Professional Certifications

Obtaining professional certifications is a great way to boost your career as a machine learning engineer. Moreover, it is easier for top companies to consider you for jobs when you possess certificates from leading institutions, as it shows your technical background and competency in the field.

Therefore, increase your value in the industry by acquiring professional certificates. You can get professional certificates when you complete a certification program, training, or professional exam organized in the field. To become a certified machine learning professional, look at these programs.

5. Join Professional Communities and Build Your Network

LinkedIn Text

Machine learning engineers work in teams of data scientists, software engineers, and other tech professionals. Hence, networking with other professionals in the industry is critical to your success as a machine learning engineer.

The benefits of joining a professional association and networking within the industry are far-reaching. For one, it helps you stay updated on the latest industry trends and find career development opportunities. Therefore, find a body of professionals in the industry to join and network with.

LinkedIn, MeetUp, and Jobcase are great places to seek these connections. Building professional relationships on a platform like LinkedIn is fairly straightforward, although employing these LinkedIn features can help you grow your presence faster.

6. Apply for Machine Learning Engineering Roles

The final step to becoming a machine-learning engineer is to land machine-learning jobs. And to achieve this, you must craft a technical CV highlighting your field experience or display a captivating portfolio. Writing a resume can be overwhelming, but this guide on how to write a tech resume will make it easier for you.

As a newbie machine learning engineer, you can start with junior roles to gain industrial experience. Then, grow through the ranks as you build confidence to take on bigger projects and roles.

Build a Career as a Machine Learning Engineer Now

Machine learning applications are on a growth trajectory as many companies seek to harness their potential for business development and competitive advantage. Leading companies like Google and Netflix have machine learning integral to their operations.

This increase in machine-learning utilization spells an increase in demand for machine-learning engineers. Therefore, now is an opportunity to start a career in machine learning by following the steps highlighted in this article.

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