Can I Learn Machine Learning On My Own?

Machine learning falls under the branch of artificial intelligence, and refers to the use of algorithms to teach machines how to function with minimal human intervention. It is a highly technical skill that can be learned through self-teaching, but is recommended as part of a formal curriculum. Beginner-level machine learning covers simplified concepts like basic terminology, programming languages, and algorithms. You can find content for these subjects online, though they may be behind a paywall. As you get into more advanced concepts like deep learning, neural networks, and reinforcement learning, finding efficient online resources can be much more difficult. There are advantages to picking up machine learning through self-learning, but there is also a list of drawbacks that should be considered. Depending on your learning style and educational goals, self-learning is a great place to start. In this article, we review the self-teaching tools available to you and the advantages, disadvantages, and alternatives for learning machine learning on your own. 

Advantages to Self-Teaching Machine Learning

Teaching yourself machine learning can be advantageous for several reasons. With self-teaching, you can directly tailor your education to your wants, needs, and interests. You can also learn at your own pace. Those who have obligations, non-traditional schedules, or find they learn better with more flexibility might find self-learning more appropriate. Similarly, those learners who are more autonomous, or have a high level of personal responsibility with their education would enjoy this method. Because self-learning students can make their own schedule, develop time management skills, and build problem-solving skills, they can prepare themselves for a professional position in machine learning. If you are considering self-teaching, it is helpful to know your level of self discipline, as well as your ability to focus and build an experience that fills gaps in your education. When choosing your resources, you’ll want to go with learning concepts, techniques, or principles that you are not fully confident in.

To be more specific, that means that if you’re struggling to understand the role of algorithms in machine learning, you can visit communities, download resources, or look up videos specifically about how algorithms are written, and how they work. If you’re new to data processing, it might be worthwhile to hang out in chat boards or on platforms that allow you to play around with data sets so that you can become more familiar with them. There are plenty of topics that lend themselves to self-studying in machine learning, including basic programming languages like Python, Java, of C++. Coding and programming are relevant to nearly every modern industry, and because of their popularity, there are also a range of free resources online for you to use.

On-demand classes are available in machine learning. They can provide you with a more straightforward and regimented approach to machine learning. Platforms like Coursera, Google AI, edX, and freeCodeCamp provide the resources needed to self-teach machine learning, and they can offer a solid curriculum or learning plan that takes the mystery out of whether the materials you’re using are high-quality and cross-checked by industry professionals. On-demand courses are geared towards students who have non-traditional schedules, or want the freedom to choose when they learn. They are paid for via subscription or up-front cost, and can be accessed at any time. Resources are often downloadable which makes them convenient for self-study even offline. On-demand courses also offer certificates of completion, which can help pad a resume or portfolio. Though usually not as effective as a traditional endorsement offered by a recognized institute, they show a commitment to learning that benefits you when you transition to the workforce.

Drawbacks to Learning Machine Learning on Your Own

While self-learning comes with many advantages, this may not be the ideal method for everyone. When learning a subject on your own, there is often a lack of guidance. Compared to live online classes, or in-person classes that come with a fully structured curriculum and an industry professional to refer to, students must compile and manage those things on their own when they are self-learning. Without a structured curriculum, it may be difficult to identify which topics must be prioritized and why. For example, without foundational knowledge of machine learning, students may try to start with more advanced topics like deep learning or neural networks, without first fully understanding the basics. If learned out of order, machine learning can be difficult and fragmented. 

Another drawback is the lack of immediate feedback, peer support, and community. Students who thrive on face-to-face feedback may struggle to keep themselves motivated. Self-discipline, time management, and organization are required to successfully learn such a complex skill. For those self-learning, the abundance of online resources can be a bit of a double-edged sword, and can be highly overwhelming. This is because free, cheap, or unmoderated resources are not always quality-controlled or up-to-date resources. With the field of machine learning rapidly evolving, materials must be closely considered so that you can distinguish between appropriate materials, and those which might not be helpful.

When learning on your own, it is also important to know who you can reach out to for help. If you have a strong network or a list of outside mentors that can step in when needed, it can prevent you from becoming confused. Learning intermediate and advanced topics requires a deeper understanding of the subject and a more in-depth learning plan that can most easily be met solely through self-learning methods. Much more research and background information is needed to ensure you are fully supported. Overall, by using structured resources, closely aligning the self-learning options with your educational goals, and knowing who to reach out to if you get stuck, you can learn many of the machine learning basics on your own.

Alternatives to Learning Machine Learning on Your Own

For those who want alternative options to machine learning courses, there are a few formats that might be more appropriate for you. Formal education programs like degrees and professional training courses are often offered on-site and in-person, which can give you the community and networking you need to better absorb the material. Some institutions offer exclusive access to certain facilities, resources, or technology that can also improve the learning process. In subjects like machine learning and artificial intelligence, specialist software can become expensive, so taking a course that provides those amenities is valuable. University courses or a degree track may be more expensive, but the curriculum is well-rounded and interdisciplinary. This means that you won’t just be learning about machine learning, but also computer science, data science, or other closely related fields.

Machine learning bootcamps and certification courses are also a great option for those who want a focused curriculum, but can’t commit to the duration of a degree. Live instructors are a one thing to keep an eye on, as they can be valuable for those who need consistent feedback or prefer the face-to-face aspect of a course. Instructors are industry professionals who are available to help you with any roadblocks or misunderstandings. They also act as a great point of contact for expanding your professional network. Specialized certifications gained through these boot camps and courses, like the IBM Data Science Professional Certificate or the AWS Certified Machine Learning certification, can greatly build up your credentials and make it easier to find professional opportunities down the line.

Workshops, seminars, and short courses are for those who may not have the financial or scheduled availability for a longer full-time course. They are much shorter and will cover fundamental or specialized topics in machine learning, but are unlikely to cover more advanced topics in a way that fully prepares you for your professional career. Though the duration of these courses is shorter and the content is less comprehensive, these classes are perfect for beginners and those who need to fill gaps in their knowledge. This might mean joining a machine learning workshop on algorithms, or attending a conference seminar on neural networks. Some of these class types may come with a certificate of completion that can be used on your portfolio or other professional pages. They can also help connect you to mentors, industry professionals, and others who can assist you as you move through your machine learning journey. These alternatives to self-teaching are certified ways to develop your confidence as a machine learning expert.