How to Get Better in AI: 6 Suggestions for Beginners

It is hard to get better in AI for newcomers. I shared a post for newcomers about AI in Healthcare Roadmap for Physicians. These five steps look easy to complete, but it is not.

Get Better in AI with 6 Tips

It is important to understand and apply tutorials as reading as. People can tackle some points during learning. So, I want to mention some important points to keep your learning journey efficient and funny and also get better in AI in healthcare.

These suggestions are especially for artificial intelligence, machine learning, and deep learning fields but are not limited to them. You may apply all of these in all technical fields like software development.

Don’t Bother Yourself with Technical Details

This was the first thing I said in the previous post I mentioned above. Be aware of your skills. It would be best if you focused on clinical problems with the clinician’s eyes.

If you can’t understand some technical detail in these topics though you are trying hard, it can be a small unnecessary detail. It won’t change anything in your life even if you understand it clearly. If you need to work on a project that requires highly technical knowledge, you should probably do interdisciplinary work with a technical person.

Read Others’ Codes & Repositories

Reading others’ code is as important as writing your own code. Don’t hesitate to review other developers’ or open source codes. You learn a lot from others. You see better code styles, better implementations, better annotations, and better documentation. Find a role model for yourself and follow their work and style. In the meantime, you create your own style.

Practice with Your Own Project

Learning from books and reading others’ codes are good, but it is not enough. You should develop your own projects. Find a problem in your field. It shouldn’t be a big problem. Try to solve this problem with AI. Collect your own data, annotate your data, train your model and evaluate the results. Do yourself all these steps. Even though your model can’t solve a big problem, you learn all phases of AI project development. You will get better in AI as you work on projects.

Follow Latest Researches

You should follow the latest research and trends in your field. Researches show you the current situation and give an idea about the upcoming days. Also, you can find interesting ideas and research topics. For example, If I hadn’t followed the research, I wouldn’t know we could predict patients’ gender from their ECG records. Yes, we can but not too successful.

How can you do it? You can follow specific journals, newsletters, or blogs. One of them is this PyMed blog. You can subscribe to our email list to catch the latest trends and news in AI in healthcare.

Subscribe PyMed Research Newsletter

Additionally, you can follow Stanford AIMI, NVIDIA Healthcare, MONAI, and PyMed.

Join Kaggle Competitions

Kaggle is a clubhouse for data enthusiasts. It has endless resources for AI researchers like datasets, notebooks, and award-winning solutions. Also, Kaggle hosts competitions with prizes. It is a handy platform for proofing your knowledge in AI and learning AI from there. Don’t hesitate to join competitions. If you win an award in a competition that will be great.

Don’t Overrate Certificates

Get Better in AI

There are a lot of AI courses that give a certificate. People can think that these certificates are enough to get a job or be successful, but they are not. They are important but not as much as you think. Because many employers look at your projects and concrete output, not your certificates. As I mentioned, creating your project end-to-end is more important than showing your certificate.

Conclusion

I have been actively learning and working on AI for years. According to my experiences, I mentioned six tips to get better in AI. Briefly, don’t deep dive into technical details; read others’ code, do your own projects and follow the latest trends.

If you have any additional suggestions, you can share them in the comment section below.

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