How to Become a Machine Learning Engineer At Home

Becoming a Machine Learning Engineer at home is entirely possible with the right tools, mindset, and training method. From the comfort of your living room, you can explore machine learning in a new way and still gain practical experience through a live course. You can master the necessary skills such as coding languages like Python and Java or explore mathematical algorithms to build and deploy models. While there are benefits and challenges associated with learning at home, it is entirely feasible and sometimes preferable depending on your needs and professional goals.

What Machine Learning Engineer Training Method is Right for Me?

If you are pursuing a machine learning education, you may wonder what training method is best suited to you. There are myriad training methods (outside of traditional degree programs) such as certificate courses, bootcamps, on-demand classes, and other self-guided methods to consider. While each has its benefits and drawbacks, it may be worth considering how you prefer to learn, what level of depth you require, and what professional goals you ultimately wish to achieve.

What is a Machine Learning Engineer?

A Machine Learning Engineer is a technical role that requires knowledge of artificial intelligence, mathematics, and data science. They are tasked with building and deploying machine learning systems that can automate tasks to make business operations easier. In addition, they manage those systems and continually update and enhance their models to maximize their efficiency. There are multiple skills a machine learning expert would have, including mathematics, analytics, coding, and communication. 

On an average workday, a Machine Learning Engineer will analyze data, develop models, and improve their previous systems. They will typically use their keen analytics skills to determine actionable insights that help make better decisions for the business. They will also document their progress to determine what collaborative efforts should be taken. Machine Learning Engineers work closely with similar professionals such as Data Scientists and Software Engineers.

Machine Learning Engineers can work in a traditional office setting and work on projects with others on the team or individually. However, they can also work remotely or in a hybrid setting, which requires some in-house work, such as attending meetings and other collaborative sessions. In addition, they can find freelance work and offer their skills to numerous clients for a short-term period. This career is expected to become increasingly necessary in the coming years as the reliance on data and technology grows.

Can I Learn to be a Machine Learning Engineer from Home?

Machine learning engineering is an incredibly layered and complex subject that many assume would be difficult to learn from home. However, many choose to learn machine learning from home through different training methods and resources. You can explore several avenues to learn machine learning from home, including online tutorials, certificate classes, bootcamps, and other training methods. Although there are difficulties associated with learning outside of a neutral classroom environment, there are ample benefits that support the professional development of any budding Machine Learning Engineer.

What are the Advantages of Learning from Home?

Learning at home offers more flexibility to students who have busier schedules than most. The at-home format allows students to spend less time worried about making it to class and more time focused on learning the course material. In addition, this method is often more cost-effective, not only in comparison to degree programs but in-person options as well. In addition, it affords a more diverse range of lessons and connects you with others in the learning community from all around the world.

Additionally, learning from home via a professional training method still teaches students practical applications of the skills and emphasizes career development. There is an added benefit of comfort and convenience that makes students so keen on pursuing remote learning options. Overall, learning this skill from home empowers individuals to become experts in a highly desirable skillset that will be increasingly necessary in years to come

What are the Disadvantages of Learning from Home?

Still, some drawbacks come with learning from home. Among these include the potential distractions, either from outside or inside the home. In connection with this, you could experience technical issues that limit your ability to learn even more. Although live online courses try to mimic in-person instruction as much as possible, there is slightly less interaction due to the lack of social settings. Self-taught methods are considered ineffective for professional development altogether. Additionally, learning from home requires above-average discipline and motivation that may be difficult to maintain as the course progresses.

What Resources Are Available to Help Me Become a Machine Learning Engineer at Home?

If you are curious about studying machine learning engineering from home, you have many options. You can explore everything from free resources and on-demand classes to bootcamps and certificate classes. Each of these examples has benefits and detriments to consider, so take time to explore all the different facets of learning from home.

Free Resources

One of the first steps to learning any new skill is to conduct preliminary research. This may involve searching through libraries and career centers to learn about machine learning, but more than likely, you will explore free resources online. You can find blogs and websites from organizations within the field that clarify complex concepts. In addition, Machine Learning Engineers can start accounts on YouTube or TikTok to share their experience working in the field and answer questions for aspiring professionals. Likewise, you can join chatrooms and explore video tutorials that help you learn more about the field and specific tasks.

Although free resources can help you understand basic machine learning skills, it won’t be enough to provide you with a thorough education. Free resources are purely supplemental. This means that you can explore resources to answer questions when you’re outside of the classroom. A free resource is useful for sharpening or refreshing your mathematics skills before the next lesson. In addition, these resources can give you enough foundational knowledge to determine whether machine learning is the right path for you. However, you will need some form of professional training to become a Machine Learning Engineer.

On-Demand Classes

A step above free resources are on-demand classes. This learning method is entirely asynchronous, meaning students won’t participate in classroom activities and discussions. Instead, they will teach themselves the material through readings, video tutorials, and solo activities. Students can pick up and put down the material as needed, which is helpful for someone with an incredibly busy schedule. On-demand courses will occasionally have a deadline, but many are open after purchase. In addition, they will provide the learning materials that can typically still be accessed after the course ends.

On-demand classes seem like a great deal, but they can be far more challenging than expected. Because you have to teach yourself, you will likely spend more time researching questions and seeking clarification. Plus, on-demand classes have a lack of interaction between peers and an instructor. As such, you won’t receive feedback on your work or have other like-minded individuals to lean on for support. On-demand classes can help current professionals brush up on their skills, but may not be enough for a total beginner to gain professional-level skills.

Live Online Classes and Bootcamps

Live courses are the best option for someone who wants to turn machine learning engineering into a career. Most live classes, whether in-person or online, provide students with the most advanced and thorough curriculum that reflects their skill level. You can enroll in individual courses, which are typically shorter and more attuned to a certain skill level, or bootcamps and certificate classes, which have professional development and career advancement in mind. Students can access more benefits in live classes than in other training methods as well, such as instructor feedback, mentoring, course materials, direct troubleshooting assistance, and career-advancing activities such as resume assistance, mock interviews, and portfolio development.

The choice between in-person and online courses makes choosing live options a no-brainer. Students can learn whichever way suits their needs. If you are a busy individual who needs flexible learning options, an online course will provide you with the instruction and tools you need to advance in the field. You can still juggle your weekly tasks and responsibilities while learning new skills. However, some prefer in-person instruction where they can directly collaborate with peers and the instructor. Plus, this learning method is familiar to most, which makes the learning process more comfortable.

Live classes have drawbacks just as any other training method. First, choosing an in-person or online option can change the outcome. If you opt for an in-person class, you will have to attend regularly. This requires reliable transportation and extra time for a commute, which may not be reasonable for some. In addition, students in live online courses are heavily reliant on their technology and internet connection. Plus, you may encounter distractions or feel like you’re interacting less due to the lack of a neutral learning environment.

How to Find Machine Learning Engineer Work at Home

If you want to find a career that allows you to work from home as a Machine Learning Engineer, you have several options. First, you can search for remote job positions at companies not only in your general area but all over the country or world. Remote jobs are becoming more popular with the rise in technology. Another method is to work as a freelance Machine Learning Engineer. As a freelance professional, you can work from home for multiple clients, so you are continually diversifying and strengthening your skills.

Freelance Opportunities

You can find freelance work as a Machine Learning Engineer at numerous companies and from websites online. The first option is to explore company websites that display their job postings. Mahy will include their freelance opportunities. Similarly, you can explore job search websites such as Indeed or Glassdoor. However, some websites specialize in sharing freelance opportunities with freelancers, such as Upwork, Freelancer, and Guru. These are some of the best places to find freelance work as a Machine Learning Engineer.

As a freelancer, you’ll work independently and be responsible for your job responsibilities but also the business side of being a freelance professional. This may include negotiating terms, communicating with clients, managing your quarterly taxes, submitting invoices, and marketing your services. A freelancer can work with other collaborators on projects, but will frequently complete their work solo. They’ll attend meetings over the phone or video conferencing applications and stay in touch with other colleagues through platforms like Slack or Teams.

Finding Remote Jobs as a Machine Learning Engineer

You can find remote Machine Learning Engineer jobs similar to finding freelance opportunities. It’s possible to find job postings for remote positions on company websites as well as job search websites. Because remote opportunities can still be permanent job positions, you can also search LinkedIn and network through your connections. In addition, you can explore local career centers and join discussions and conferences about the field to learn more about the available positions in your region and beyond.

Remote work is similar to freelancing but with more stability. Most remote employees work on a part-time or full-time basis but are considered hired employees instead of contract workers. Remote workers will need exceptional communication skills and above-par technological abilities. These are necessary qualities to keep employers and clients updated on your progress and to handle any technical errors that arise.

How Will Remote Work as a Machine Learning Engineer Differ from On-site Work?

Remote work is similar to on-site positions, but there are considerable differences. With remote work, you can perform your duties from home, a cafe, the library, or any other space you see fit. On-site work, of course, requires being present at the office. These environments provide different outcomes to different workers. Some prefer having the flexibility to work from anywhere, whereas others get distracted and feel more comfortable in a designated, neutral office setting. Likewise, on-site employees can typically walk down the hall to ask questions or receive feedback and clarification whereas the remote worker will have to email or use another form of digital communication. 

In addition, a remote job may have less access to resources than an on-site position simply because they aren’t physically present in the office. Collaborative projects may feel more challenging, but working independently suits some people quite well. Both offer adequate professional development as it is impressive to have the self-discipline to work from home without distractions, but on-site work is traditionally effective and offers ample support. If you’re still considering whether remote work is the right fit, consider your preferences and what you need to feel successful.

Learn the Skills to Become a Machine Learning Engineer at Noble Desktop

If you are interested in becoming a Machine Learning Engineer, there are many ways to start your journey. Notably, you can explore certificate programs at reputable training centers such as Noble Desktop. Their Data Science Certificate covers all the necessary skills to become a data science professional, which is the field where machine learning practices are regularly applied. Specifically, students will learn relevant coding languages, automation, machine learning, data analysis, and predictive modeling skills. Throughout this 114-hour course, students will learn how to clean, balance, and apply machine learning algorithms to data sets with scikit-learn. In addition, they will practice analyzing data with Python libraries like NumPy and Pandas. By the end of the course, students will be able to automate everyday tasks and utilize their skills in the data science field. Noble Desktop provides students with additional benefits to support them through their learning journey. In this course, students can access 1-on-1 mentoring sessions, a free retake, and various payment plans. Upon completion, they will earn a verified digital certificate that displays their mastery of the material.

Likewise, Noble Desktop offers a Software Engineering Certificate where machine learning skills and relevant information will be discussed. In this certificate course, students will learn to use Python and Django for machine learning algorithms and task automation. In addition to Python, they will learn to build the front end of a webpage with HTML, CSS, and JavaScript, which all add interactive functionality. This course also gives students the exposure and experience they need to develop a professional portfolio with their completed projects. Over 510 total hours, students can access myriad benefits like a course retake, bonus mentoring sessions, and an additional elective course, all at no extra cost. Once the students complete the program, they will earn their verified certificate to show for all their accomplishments.

To gain more knowledge of artificial intelligence for business purposes, consider the Generative AI Certificate from Noble Desktop. In this 78-hour course, students will learn how to utilize artificial intelligence in different work settings and for varying reasons. For instance, students will learn how artificial intelligence can help automate workplace tasks or quickly analyze large data sets. The instructor will guide students through projects that leverage artificial intelligence for different marketing strategies and help create high-quality marketing designs. This course does not require any previous coding experience and is solely meant to delve deeper into the larger implications of artificial intelligence and its usefulness in the workplace. Through hands-on projects, students will gain the necessary experience to apply the skills in their field. Students can retake the course anytime within a year of their original enrollment at no additional cost. Plus, Noble Desktop will award students with a digital certificate once the course ends.