How to become a Machine Learning Expert

No, I’m not going to tell you to do personal projects

Jul 8 · 6 min read

As an educator in the Machine Learning space, I often see lots of people give out advice on how beginners can break into Machine Learning and do well there. Typically this advice follows something along these lines:

  1. Take a combination of Math Classes. Typically involving Probability and Statistics, Calculus, Linear Algebra, and Logic.
  2. Find one or two personal projects that interest you. Really good advice will suggest building an end-to-end pipeline starting from Data Collection all the way to analysis and report generation.
  3. Look at some tutorials/documentation online for learning the implementations.
  4. Enjoy your ML expertise.

Some might recommend ML courses online. But this is the gist of what they suggest. Now none of this strictly bad per se. It is however incomplete. Such advice will work for you, BUT it will be a grind to get to an advanced level. If you’re someone who is trying to transition into ML from other fields (or has any other time obligations) you don’t have the time for this kind of bottom-up approach. So how can someone like that compete with people like me, who work ML full time? Or people who spend hours learning the ins and outs of these systems in classes. It took 3 years of Math for me to get to Convolutions, Laplace Transformations, and Multivariate Optimisation. I’m still learning a lot more (as a student I have that luxury). You don’t have three years. So what should you do? This article will go into that.

Why should you listen to me? Good question. This is what someone I worked with had to say:

You can see this recommendation on my LinkedIn

In this project, I was brought in as a Machine Learning Expert. I was responsible for providing a Machine Learning perspective in trying to figure out ways we could improve safety. The work I did there will take a while to explain, but my contributions were notable enough for a public recommendation given to me. Here is a patent for my work in developing a proprietary Machine Learning algorithm in Parkinson’s Disease Detection. The algorithm was actually commercialized in 2019. You can see the offer right here

I have other proof of credibility (some of which I will share later in the article, all of which are available in the Public domain). Now that we agree on my credentials, let’s get back to the article. How can you break into higher ranks of Machine Learning? What can you add to the training that gurus recommend? Simple. Read Research in ML. Take papers that seem interesting and read them. Follow YouTubers such as myself, Yannich Kilcher, Two Minute Papers that break down research in ways that people can understand.

But I don’t have much expertise. Won’t papers be too complex?

Yes and no. By itself, reading papers (or watching someone explain them) won’t be immediately useful. They will have lots of keywords that you don’t know. You won’t know half the techniques they use. But it will get you in the habit of searching up and learning new things. It will teach you to get familiar with complex problems. And that is huge. And as time builds up, you will find yourself understanding the papers better. You will start to see why the authors did a particular thing. You might question why they chose a particular data augmentation technique over another. Paper by paper, you will find your knowledge improving. And this will help you address the problem you would get by focusing on projects. It takes care of the flaw of that approach.

What flaws?

The project heavy approach is exceptional for learning the technical aspects. It will teach you the hows of the experience very well. And there is no better way to learn than through first-hand experience. In terms of the how of machine learning, there is no better way. But it overlooks one thing. In ML, the what is often as important as the how. You need to learn how to identify different aspects of the problem. You need to be able to understand different steps in the pipeline. Tutorials and courses are very structured. They provide guidance, but will often not give you an idea of all the ways your solution could have been built. Reading about different teams and research that tackle similar problems will not have this flaw. You will be exposed to different ideas and perspectives. For example, reading about all the Computer Vision Research (and research into using Machine Learning in Malware Detection) birthed my Deepfakes Detection Idea (which I am researching right now).

It will also give you access to other people involved in the community. You can discuss the research with experienced professionals. They will share their experiences, adding to the discourse. You can see an example, where I discuss SimCLR (a visual learning architecture) with a reader of my work. They told me about the potential problems it has with imbalanced datasets. This was new to me. This is an overlooked advantage of getting involved in research communities. You can learn from experts.

In a nutshell

Exposing yourself to research will open up your world to how many ways there are to solve a problem. It will expose you to different ideas and concepts. You might even come across novel implementations (such as how you can Classify Malware by turning the binaries into images). In simple words, it’s a hack. It will allow you to get involved in ML the way people involved in it full-time are involved. It will help you learn the same way people learn about ML on the job (coming across and testing new ideas) without spending all those hours of trial and error yourself.

This holds true for even more advanced people. I came across TSNE fairly recently into my ML journey (July 2020). This was an interesting idea to me, and something I’ve gone on to use for great results. I came across this idea by reading research papers. To learn more about this concept, be sure to check out the video below.

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How to Proceed

I will go into details about how to read research papers on your own (for varying levels of Math Comfort). So if you’re into that, be sure to follow my work on different platforms. However for a quick overview. I would suggest something like this. Spend about 2–4 hours a week coming across new research in ML (through one of the channels, or just following publications). Make sure you understand the papers to the point where you can explain what they do at a high level. Even at a low end, 2 papers/week is 8 papers/month. This will add up exponentially(compounding returns do amazing things). Interact with people using the internet. It’s a great thing, and you can maximize it.

For the technical side, build a simple version of the cool project. This is just to get you familiar with the tech stack and the implementation. Make sure you learn the technologies well. And use the learnings from your research to play around with new additions to this project. And soon you too will transition smoothly into ML Mastery and have recommendations like this. See you at the top.

Another LinkedIn Recommendation

Reach out to me

If the article (or the recommendations) got you interested in reaching out to me, then this section is for you. You can reach out to me on any of the platforms, or check out any of my other content. If you’d like to discuss tutoring, text me on LinkedIn, IG, or Twitter. I help people with Machine Learning, AI, Math, Computer Science, and Coding Interviews.

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