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I have seen people that think that they need to get a degree in machine learning.
I am all for degrees, I just don’t think they are for everyone. I also know that you can get started in machine learning and go far without a degree.
In this post I will convince you that you do not need to get a degree in machine learning to get started or make progress in the field of machine learning.
Machine Learning Degree
You may believe that you need a degree in machine learning, and maybe you do.
Some of the reasons you believe you need a degree are:
- To learn machine learning properly. Getting a machine learning degree will teach you machine learning in a structured way. Degree programs are designed by academics that are experienced in the subject matter and in how to educate. The degree programs are targeted and clearly define what is expected of a student before they join the program and what they will capable of after the program.
- To get a job. Getting a higher degree in machine learning will give you the opportunity to apply for machine learning jobs. Organizations advertising jobs that require specific skill sets and select prerequisites that allow them to efficiently filter applicants. Advertisements for machine learning jobs typically require a degree or higher degree in machine learning or a closely related field.
- To practice machine learning research. Getting a higher degree in machine learning will give you the opportunity to practice machine learning research. The vast majority of machine learning research is produced by research labs at universities and in industry. The competition in such labs is fierce and the prerequisites for advertised positions are specific undergraduate degrees and honors programs.
Degrees Have Limitations
If you can complete a degree in machine learning it does not guarantee the outcome you seek. It may increase your chances but success is not assured. Degrees are great, I have a few myself, but keep in mind that they are just one path that like any path have their own set of limitations.
Taking on and completing a formal degree is a big undertaking. Some points to help you deeply consider this approach are listed below.
- A degree is expensive. A degree program can cost tens of thousands of dollars or more and you are sacrificing any income you may have been able to earn during that time with the hope that you will have a greater earning potential in the future. Granted, you may able to offset those costs with a scholarship and you may be able to defer those costs into the future.
- A degree is a symbol for others. There is prestige with earning a degree, especially a higher degree. The completion of a degree is a symbol for others to evaluate you by. It is a filter used by employers to make their hiring process more efficient.
- A degree takes a long time. A degree takes years and a higher degree can take many years, even the best part of decade. That is a very long time to wait if you are interested in applying or using machine learning today.
- A degree is for the average student. A degree is designed by a committee for an average student with an average performance and prerequisites. It does not take into consideration your specific interests or skills.
- A degree teachers older information. A degree is designed before you purchase access to the program. At undergraduate level, this can mean that the material is many years out of date at a minimum.
Skip the Degree
Can you skip the degree and still have the opportunity to get what you want? I argue that you can and that there are multiple paths available to you.
For example, I was implementing machine learning algorithms, writing articles on AI and winning competitions associated with conferences while working full time as a programmer. Some of the best rated competitors on Kaggle (a website for machine learning competitions) do not have higher degrees or if they do, they are in totally different fields of study.
Learn Machine Learning Properly
You can complete a formal training in machine learning at your own pace, at home. Three options for formal training alternatives include:
- Complete an online course on machine learning. Watch the lectures, do the homework and interact with other students.
- Read a book on machine learning, cover to cover. Take notes, complete the exercises, and implement what you learn.
- Design and execute your own course. Draw upon high quality free and paid materials on the subjects that interest you most and design the course and add the formalities you require.
Get a Job
You can create symbols that indicate to potential employers that you are skilled in machine learning. It will require initiative and marketing on your behalf. Three examples of symbols you can create:
- Complete a course or read a book and track your progress and findings in a public blog as you go.
- Compete in machine learning competitions and work to earn a modest ranking such as within the top n% for a competition. Partner with skilled practitioners to acquire skills faster and achieve better results.
- Complete small projects in machine learning, advertise the results on a blog and social media and release the code on public revision control systems. Build up a collection of completed projects you can refer to, draw from and discuss.
Practice Machine Learning Research
If you are obsessed with a particular concept or machine learning method, you can design your own research program.
Higher degrees are really an apprenticeship in research and research methods as well as induction into the deeper parts of the field, and that is hard to replicate independently.
Nevertheless, if you can practice machine learning research outside of an institution. Three examples include:
- Reproduce results from applied research papers. This will likely require communication with the researches involved to learn the details of the methods and data. Reproduction of results is a pillar of the scientific method and demonstration that results can or cannot be reproduced is publishable research in and of itself. You could start by blogging your experiences and marketing your findings to interested researchers.
- Self-publish your own treatments on your subject. This may be in the form of white papers, essays or ebook monographs. Do your best work and have the confidence to reach out to the research community for comment and review.
- Contribute and collaborate by putting out excellent work and showing interest in others work. Build and maintain connections with researchers in the field. Like any relationship, start slow and build trust.
Anyone can read and internalize research papers, write down their own ideas and design and execute their own experiments. Start small and be honest. Academics love to pick holes in everything, savor and learn from feedback in whatever form.
Do not let you perceived need for a degree stop you from getting started in machine learning or thinking that you can make significant progress.
In this post you learned that you can get started in the field of machine learning and make the progress you seek without a degree or higher degree.
You learned that there are multiple paths available and a degree is but one path that can consume a lot of time and resources. You also learned about alternatives to the structured learning of a degree and for the research apprenticeship for a higher degree program.
Formal education is a contentious issue, I’m keen to hear your opinion on this post. Please leave a comment and let me know what you think.