How to Build a Successful Machine Learning Portfolio

Learn how to complete machine learning projects to make your portfolio stand out.

This article is the last in the “Becoming a Machine Learning Engineer” series. I started writing with the goal of improving my writing and learning through creating. My biggest fear was everything I wrote would go unnoticed. Lucky for me the exact opposite happened. People engaged, commented, clapped, shared, and followed me and my writing. I’m humbled by this success and have decided to keep writing in hopes that I can help a new generation of machine learning engineers on their journey of learning. This article helps you learn how to build a successful Machine Learning Portfolio that will get you hired.

Becoming a machine learning engineer is not a trivial task. It takes lots of hard work and patience to go from nothing to building systems that learn from data. If you have gotten this far in the series then congratulations, you are where I was a few months ago, but if you are like me, then you are not quite sure how to showcase the skills you have built up over many months of dedicated practice.

The single most productive thing you can do to showcase your skills is to build a machine learning portfolio of your work. A high-quality ML portfolio can showcase:

  • Ability to communicate
  • Technical competence
  • Ability to reason through problems
  • Motivations and ability to take initiative

These are all things that employers want to see when they are decided who to hire. Unfortunately, not many people put together a winning Machine Learning portfolio is a way that will showcase this. When putting together your first machine learning portfolio, there are five things to keep in mind. These five things are guidelines that will ensure your portfolio will give you the biggest bang for your buck.

Keep Projects Small

Size does matter in your portfolio, while a significant project is flashy it comes with risks and costs. A small project likely won’t take longer than 20–40 hours to complete. And if it doesn’t work out then your loss is much less than a multi-month time sink that some large projects can become when building a successful ML portfolio.

Complete Projects

The only thing worse than having no portfolio is having a portfolio filled with half-done projects. It screams to the world that you are not capable of finishing what you started and should be avoided at all costs. Ensure you complete projects if you want to build a successful machine learning portfolio that will make an impact.

Independent Projects

Machine learning can be applied to nearly every field. You should showcase this in your portfolio by completing projects that can stand on their own and not extensions of previous work. Ignore this if you know exactly what field you want to get good at and are looking to showcase your expertise.

Novel Projects

Many students in my classes make the same mistake. They have followed many tutorials and feel like they should be able to do anything. Then they try to complete my take-home projects and fail.

Following online tutorials is not a way to learn well, completing novel projects from start to finish is how you learn well. If I see a portfolio of many tutorial type projects in it, I thought out the whole resume.
Need novel project ideas? Check out my previous article Becoming a Machine Learning Engineer | Step 4: Practice, Practice, Practice

Easily Accessible

Make your portfolio available online for all to see. The more people find, read, and comment on your work the better. Not only will it be a vector for future employers to find you but you might also get great feedback on your projects.

The easiest way to do this is just to host everything in a git repository with a substantial ReadMe.

If you’re like me and learn best from example then take a look at this data science portfolio.

Take away

Having a portfolio of your work is quickly becoming an essential part of hiring for new Machine Learning engineers. It has been a part of the software engineers’ hiring process for a while now. Get started now, dig up old projects or build new ones, put them together, and write a few reports on what you learned from the projects.

Also read: The Best AI and Machine Learning Books

Thanks for reading 🙂 If you enjoyed it, drop me a comment and share the article with anyone you think needs it. Let’s also connect on TwitterLinkedIn, or follow me on Medium