What if I Don’t Have a Degree

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.

What if I Don't Have a Degree

What if I Don’t Have a Degree
Photo by Alexander Kachkaev, some rights reserved

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.

16 Responses to What if I Don’t Have a Degree

  1. Bojan Miletic January 16, 2014 at 9:13 am #

    Thank you for this post.

    I used to think that having a degree was the most important thing in a carrier. I still think that it is important, but far less.

    As you mention having previous experience and projects that you can show, makes lot more difference. This is based on my experience as a programmer, but I think that same goes for almost any other job.

    • jasonb January 17, 2014 at 8:37 am #

      I totally agree Bojan.

      Even during the degree, mastery was a matter of doing extra work way above and beyond the coursework.

      Formal education is changing. We live in very interesting times.

  2. Shantnu Tiwari January 17, 2014 at 4:50 am #

    I agree, degrees are overrated. They are like the Bose sound systems- good, but not worth the price being charged (I say this as some with a Masters, though not in Computer Science, thank SpongeBob Squarepants).

    I like your point about degrees being for the average- in my own degree, I constantly topped my class, but when I got a job, I found I knew nothing.

    I think degrees are a form of hazing: The thinking is that “We wasted four years and 50-60,000 dollars getting a piece of paper, so you damn better too. Otherwise, we will end up looking like idiots.”

    Especially with so much info on the internet, you can learn more stuff, quickly and cheaply in your home, rather than spending a fortune.

    • jasonb January 17, 2014 at 8:40 am #

      Spot on Shantnu.

      Something I think a lot about while writing content and guides for this site is how hard it is to self-study. You can get the material or put your own course together, but it takes a lot of discipline to see it through. Perhaps there is value in having a highly structured environment like a university just to boost completion.

      Once you know how to learn (how YOU learn), you can put stuff in your head a lot more efficiently than a generic course. I guess I struggle to come up with a way a person can get to that point on their own – there is very little taught on learning how to learn or teaching yourself how to teach yourself 🙂

      • neerthigan April 12, 2014 at 12:08 am #

        You are right Jason. We are not taught how to learn or taught how to learn by oneself, we are force feed the information.

        I have taken up a goal to find books or website that can help one to learn. The best book I have came across is called “The 5 Elements of Effective Thinking” by Edward B. Burger and Michael Starbird. I was also able to find a list of books similar to the one I recommended above:


  3. K.Y.Park February 18, 2015 at 4:36 pm #

    thanks for this posting on self teaching & learning of machine learning.
    as a programmer who identifies oneself as an independent AI researcher & developer,
    your opinion encourages me to keep endeavoring after my lifetime goal and dream.
    thank you.

  4. JZQuant August 17, 2015 at 7:33 pm #

    The part of world where i come from we don’t have degrees for most of these things, nor do i have finances to travel abroad. so while doing a distance masters in math i realized i can do the same for Machine learning albeit with out a genuine degree. but who wants a degree any way

    Go to any of your dream university website,download their curriculum , syllabus and ask for reference books on quora or stack exchange.
    Stick to a schedule and finish targets through self discipline and it should work. i usually follow this site for ML: http://cs.nyu.edu/~dsontag/courses/ .I have done this for a masters in math , i believe i can do it one more time .
    This is a limited approach often it takes lot more time than just finishing the books to completely get the big picture or the historic development of a subject which you can easily acquire in a classroom driven experience . But your learning is going to be lot more rigorous and symbolic as you are forced to learn that way ,unlike class room learning which is more intuitive and can sometimes be damaging at-least in mathematics.

    But I am not sure if this techniques will benefit in research.I guess research demands a lot of awareness and group learning.

  5. Chris February 15, 2016 at 12:10 pm #

    I’m currently studying for a degree in Business Systems, not because I think I will learn something useful I don’t already know (I’m 40), but simply to get past the first stage in HR, when they weed out all of the applications that do not have a degree.

    My personal thoughts on a degree is it’s used to differentiate poor people from rich people. If you put the time in, a degree is not hard, but many poor people cannot afford to go to higher education.

    That website posted by JZQuant looks interesting, thanks for that

  6. Nader March 15, 2016 at 7:02 am #

    Great post !

    I completely agree with Jason.

    Now I am on a quest to learn not through academia where great minds go to die, but through the unlimited amount of information and knowledge available at our finger tips.

  7. Azim May 25, 2016 at 5:37 pm #

    The real challenge is with the job listings in job portals. Almost all job listings ask for MS in computer science or P.hd. For a quant developer or actuary position you can pursue CFA or Actuary course along with your job and get it done. Unfortunately for machine learning there isn’t any such industry recognized certification which can be pursued along with job. That makes it difficult for those who do not have a masters degree, although you might be one of the brightest in your field.

  8. Joao Pires July 8, 2016 at 7:19 pm #

    Well, my opinion is … the best thing is to have booth … this means … a Degree and be a self-study. Mostly to be critical and rational.
    About the jobs … well this is a area where it’s always difficult to understood. There are so many variables, so isn’t a strait forward way.

    • Jason Brownlee July 9, 2016 at 7:43 am #

      The job market is fickle.

      It comes down to personal relationships (or relationships you build rapidly in an interview) and indicators you can used show you can deliver results.

    • Tom Anderson August 22, 2016 at 5:27 pm #

      @Joaoa Pires – Yes, I was about to say the same thing. The degrees can’t keep up. For example when I began my PhD in 2009 there was not even such a thing as “deep learning”. And nobody else is doing the exact same research study as me. To keep abreast of the field, it is important to do self-directed study.

      • Tom Anderson August 22, 2016 at 5:30 pm #

        P.S. What I mean to say by “not even such a thing”, actually deep learning has been around since the 80s and even before, but only in the last few years has it become state-of-the-art to solve problems like speech recognition. When I started, everyone was talking about HMM and GMM for these problems.

        • Jason Brownlee August 23, 2016 at 6:03 am #

          Yeah, I remember all the hubbub about RBM in 2006 around the time of the Netfix prize, and then later about DBM. It was impressive to see the field of “deep learning” coalesce around 2010-2011.

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