Are Machine Learning Classes Worth It?

Depending on your overall goals, taking machine learning classes can be a highly worthwhile investment. Machine learning has become an integral part of applications and services thanks to the advancements in technology in the past decade. Machine learning is a gateway to a promising career, in various industries including education, healthcare, and finance. What’s more, is that many job roles are directly associated with machine learning, such as that of a data scientist and artificial intelligence researcher. Research is recommended for anyone thinking of joining a machine learning class, and course length, content, and cost are at the center of that research. This article reviews the benefits of taking a class, as well as the most common considerations for choosing the best course. This will help you make an informed decision that best fits your needs.

Advantages of a Machine Learning Class

There are many advantages to taking a machine learning course. Students who are social learners can benefit from the social component of classes. In-person classes are highly interactive spaces with plenty of hands-on activities to help with learning. Even online courses generally have a social component in chat boards, forums, and group work. For those students who are more focused on the academic side of the experience, the curriculum and structured layout of the class will be a highlight, with assignments and projects providing ample opportunity to put what you learn into practice. Regardless of your learning style, interests, and goals, a formal class is a great way to learn the basic skills in machine learning, while also preparing yourself for the professional world of technology. Let’s take a closer look at some of those considerations below.

Access to Resources

Machine learning is a technology-heavy subject. Because of this, students often need access to tools, software, and other computing resources that can be quite expensive. When taking a course, you will have access to textbooks, software, instructors, and exclusive in-person or online resources that others don’t. This can help cut costs by allowing you to avoid subscription fees for applications and software, and large up-front purchases of new technology, especially if courses are held in a facility that already provides these amenities. Additionally, classes will provide the most recent and up-to-date versions of these resources, so that you can try them out before committing to the full cost of purchasing them yourself outside of a course. The resources are custom-picked for a class, so digging online for the appropriate books, activities, or information is unnecessary, saving you time.

Structured Learning Environment

One of the more noticeable perks of joining a class is the learning environment. The curriculum is designed by professionals who are dedicated to teaching you most efficiently and effectively, meaning that you won’t have to go hunting for tools that match your style of learning. Classes often have a regular schedule, so that you know you’re making consistent progress toward your goals. Classes also provide feedback and, depending on the format, more formal assessments to help track proficiency. The structure is a big selling point of formal courses, as free online resources and self-learning might be lacking in these areas.

Even if more flexibility is needed schedule-wise, there are options. Online classes can be offered asynchronously, which means that they are done through pre-recorded lectures, modules, and workbooks. This is self-paced, as students can look through their assignments at their leisure, however, the curriculum is still prepared by a professional, and students are guided through content logically and efficiently. Asynchronous courses are ideal for those who have other time-sensitive obligations but are looking for the same structure and support that formal classes can provide. 

Networking Opportunities

With in-person classes, meeting peers and making friends is part of the course. There are lots of opportunities to collaborate on projects and build up a community of like-minded people that you can reach out to, even after class. With the help of guest lectures, workshops, and seminars, you have a chance to shake hands with industry professionals and experts in machine learning. There are also general networking opportunities through alumni networks, for those considering university courses, and meet-ups for those who are taking bootcamps, workshops, or other private courses. No matter the affiliation, most courses have some form of career assistance for those who are interested in going the professional route. By checking the course description, it’s easier to tell whether a class offers portfolio building, resume help, or job placement assistance. Even if these resources aren’t offered, a course is a great way to meet people who can help you go further in your eventual career.

Portfolio Building

Assignments offered during your course can double as additions to your portfolio. Certain classes even focus on portfolio development as part of their curriculum. For example, bootcamps taken with Noble Desktop always offer a final portfolio project that can be tailored to your professional goals. Other institutions will provide a similar service. Showcasing these projects as part of your portfolio is an excellent way to demonstrate your skills in machine learning or related industries. A strong portfolio can work for you, enhancing your job prospects by highlighting direct projects that have been completed and showing your experience with machine learning.

Considerations for a Machine Learning Course

Even with all of the advantages that taking a machine learning course can bring, it’s important to consider certain aspects of your education before taking the plunge. Knowing things like your budget, proficiency, or preferred learning style upfront can help you find that ideal course. Jumping straight into a course is not advised, and by carefully considering these factors, it’s easier to meet your goals, as well as your personal and professional needs, while also saving yourself from the stress of taking a course that doesn’t work for you. 

Course Cost

Course costs vary widely by institution, proficiency level, course type, and even scheduling format. For example, if you take a university course in machine learning, tuition may be a combination of admission fees, facility fees, and textbook costs in addition to the baseline cost. These can be lowered with the help of scholarships or financial aid, but might still carry a heavier price tag than their bootcamp, workshop, or one-day class counterparts. Similarly, with bootcamps, workshops, or other private courses, there are subscription or software fees that must be considered. Hardware equipment can be expensive if you don’t have access to the appropriate tools. University courses tend to be more expensive, especially if you go the degree route, which can cost several thousand dollars. Bootcamps can range from hundreds to thousands of dollars. Workshops and one-day courses are a more budget-friendly option but do not offer the same scope in curriculum.

When reviewing whether a course cost is appropriate, it’s worth paying attention to the things you get in return for your participation. Paying close attention to amenities or offered resources is a great way to see if the bang is worth the buck. The standard perks of paying for a course are increasing your earning potential in the professional field and growing your network, but other advantages might be access to resources, facilities, and high-quality assignments. Those students who are serious about joining machine learning as a professional track are advised to take a more intensive course. It is normal for those courses to cost a few hundred to a few thousand dollars.

Course Difficulty

Proficiency level is an important part of choosing the right course and plays a large role in whether a class is worthwhile. If a class is too easy and you are not intending to review content that you know, you run the risk of wasting time and money without learning new content. If a class is too difficult, you run a similar risk of not learning any new content, the reason being that you’re unable to understand it. Before you join a course, it is recommended that you outline the content you are already comfortable with. In this way, when reviewing a curriculum for a potential course, you can make sure that it aligns with your educational level and overall goals. Many courses in machine learning will summarize who the course is intended for, and the skill level required to comfortably complete the assignments. Since beginner, intermediate, and advanced skill levels are not a firm line, double-checking course content is useful in making the best decision.

Course Type

The final consideration is the course type. Different formats can help you learn machine learning differently. For example, workshops are short courses that focus primarily on one aspect of machine learning. Though skill level plays a role in workshops, it is not usually the focus. They are hands-on and highly interactive, making them a perfect choice for those who have other time commitments and can’t take a full course. However, they do not offer a comprehensive curriculum. Rather, their teaching strategy is geared towards short project-based opportunities that can teach you specific skills. Seminars are similar. For those looking for more comprehensive formats, a university course or a bootcamp may be more appropriate. University courses are longer and typically include interdisciplinary teaching, which loops in related subjects to give you a more well-rounded view of machine learning. Bootcamps are similarly intensive but are often more focused on portfolio development and the career path forward after the class.

What Will You Learn in a Machine Learning Course

Machine learning is a branch of artificial intelligence that helps minimize the human interaction needed for a machine to advance. Essentially, it’s the process of getting a machine to learn on its own. This is done with algorithms, statistical models, and data. When a machine can recognize and make sense of patterns, it can begin to use them to make its own choices so that it can develop further. In a beginner machine learning course, you are likely to learn the history and modern use of machine learning. You’ll pick up basic programming skills, typically in Python, and learn more about machine learning libraries such as sci-kitlearn. Algorithms and data processing are highly important too, so learning how to classify algorithms and handle data are beginner-friendly concepts that your course will likely cover.

At the intermediate and advanced levels, machine learning can become much more complex. You’ll not only need to learn about algorithms but need to know how to apply them so that the machine can learn from them. In the higher levels, you’ll begin working with classification, neural networks, distributed learning, and feature engineering. Deep learning is one of the larger concepts you’ll be learning at this stage, and it refers to the use of a neural network to model complex patterns in your data. With deep learning, advanced applications can be developed, and this shows up in everything from recommendation systems to autonomous vehicles. Mastering these topics is vital to tackling more complex machine-learning problems.

These technical skills work alongside soft skills that are necessary for success in the field of machine learning. Project management is useful in overseeing machine learning projects, and ensuring that assignments and classwork are done efficiently and successfully. Being able to collaborate with your peers and communicate ideas is vital. Logic and critical thinking skills are expected of data scientists and those who work with artificial intelligence. Additionally, given the rapid development of this specific field, adapting and keeping updated information is crucial. When combined, all of these skills will enable you to contribute to the ever-growing field of machine learning and artificial intelligence.

Who will Benefit from a Machine Learning Course

Machine learning is a relevant industry for anyone interested in technology. In particular, data enthusiasts, tech-savvy individuals, researchers, and STEM students are encouraged to look closer at machine learning. A beginner or a short course is a recommended option in this case. Once you’ve decided if machine learning is in your interests, there is the additional recommendation of checking out university degrees or classes. For professionals who already work in tech, a longer, more advanced course is ideal. This includes bootcamps that provide intensive, hands-on training and focus on real-world applications, which can help you advance your career. Machine learning is a highly relevant and very technical topic, but it is worth looking into for anyone with an interest in modern technology. Using the above considerations, you can find one that matches your level of interest, overall goals, and proficiency. 

Learning Machine Learning with Noble Desktop

Noble Desktop offers several classes related to machine learning. Their general Python Data Science & Machine Learning Bootcamp teaches Python as it relates to data analysis, machine learning, and automation. Python is a common programming language in artificial intelligence, making it a valuable asset even outside of this specific context. The curriculum includes 2 Python bootcamps. It also reviews Python for automation, data visualization, and interactive dashboards. This comprehensive course will build your confidence in programming so that you’re not only ready for entry-level positions in data science and Python engineering, but capable of creating predictive models from data, and automating everyday tasks. Upon completion of the course, you will receive a digital certificate of completion that you can add to your digital profiles. 

For those who are more interested in the Data Science aspect of machine learning, Noble Desktop also has a Data Science Certificate. This course reviews Python, SQL, automation, and machine learning. The difference is that this course is geared more toward data scientists. This course includes 3 bootcamps, including the Python for Data Science bootcamp, Python Machine Learning Bootcamp, and SQL bootcamp. There are plenty of opportunities to practice Python in this course, as well as prepare for a career in any programming-related field. Perhaps the most attractive component of this course for tech-savvy individuals is the opportunity to create machine-learning models with data collected during the class.

There is also the option of taking Noble Desktop’s Generative AI with ChatGPT course. This class covers AI in a fun and exciting way. During the course, you can see machine learning in action, and contribute to one of this decade’s more popular artificial helpers: ChatGPT. Through this course, you’ll learn how to write prompts in a way that generates helpful responses and explore how the world is currency using ChatGPT to improve technology while assisting individuals with their day-to-day lives.