When the competition is fierce and there are dozens (if not hundreds) of people applying for the same jobs, how do you stand out and catch a recruiter’s eye?
The key is a strong portfolio. A strong portfolio gives hiring managers a sense of who you are and what you can do, with tangible examples of your skills and expertise. Ahead, we’ll go over what you’ll want to include in a Machine Learning Engineer portfolio and how to build projects that’ll help you land a job.
What to include in a Machine Learning Engineer portfolio
A Machine Learning Engineer’s portfolio is similar to any other technical portfolio. You’ll want to include some information about you and your history in the machine learning field. Here are the basics:
This section allows you to tell recruiters more about who you are — the type of information that may not fit in a technical resume. Consider highlighting your passion for machine learning and explain why you got into the field. You can also include details about your current projects, your tech stack, and where you see yourself in the future.
You’ll also want to let hiring managers know how to reach you, so don’t forget to add your name, email address, and phone number at the top of your portfolio. You can also add links to your professional social media accounts — including your LinkedIn, which is one of the primary tools for tech recruiters.
Adding a GitHub link is another good choice. As far as other social media accounts go, that depends. If your Twitter or other social media accounts are machine-learning related, then include them. If they’re more about your personal life, don’t.
When it comes to your skills, emphasize those that apply to the type of job you want, but don’t go overboard and make it a huge list. The focus of your portfolio is the projects you include, but listing your skills provides added context.
Some of the top skills for Machine Learning Engineers include:
- Data visualization
- Algorithms and data structures
- Statistical modeling
- Quantitative analysis
- Cloud computing
The type of machine learning projects to include in your portfolio
The type of projects you should include in your portfolio depends on the companies and industries you’re applying for. Try looking through Machine Learning Engineer postings on sites like LinkedIn and Glassdoor. Make a list of a few companies you’d like to work for, and read through their job descriptions to see which skills and technologies they’re looking for in potential candidates.
Then, you can use these insights to create projects that will stand out. It helps to pick a few diverse ones so you can add more variety to your portfolio. You don’t want every project to use the same type of data set or make your skillset look more narrow than it actually is.
Use the job descriptions and your interests to build projects you’re curious about. Then, when you’re designing your projects, keep in mind the following tips:
- Keep it relatively small. Your portfolio should include several projects, so try not to spend too much time on a single one.
- Make it a complete project. You want recruiters to know that you can finish what you start. Don’t include any unfinished projects in your portfolio.
- Choose an interesting topic. Remember, your portfolio also helps show hiring teams who you are as a person, so you’ll want to include projects that exemplify the types of problems you like to solve.
- Use publicly available data sets. Using publicly available data sets is also great for coming up with project ideas. They give people a point of reference for your work, and it’s always better to use real data over toy data when showcasing your skills.
You can also build your portfolio as you learn. Many of our machine learning and data science courses teach you the skills you need while you build projects suitable for a Machine Learning Engineer portfolio. For example:
- Get Started with Machine Learning includes handwriting recognition, breast cancer classification, and sports vector machine projects.
- Build Deep Learning Models with TensorFlow includes deep learning regression, galaxy classification, and COVID-19 and pneumonia classification projects.
- Feature Engineering includes principal component analysis, logical regression, and feature creation projects.
- Build a Recommender System shows you how to create the algorithms that power streaming services and e-commerce platforms, and enable them to provide users with personalized suggestions.
Where to host your machine learning portfolio
If you already have some web development skills, your own domain, and a hosting provider, then building and hosting your own portfolio site is a great option. But if you don’t, no worries. Most Machine Learning Engineer roles don’t require web dev knowledge.
The next best place to host your machine learning portfolio is GitHub. You can host all the code from your projects there. If you use Jupyter notebooks in your project, Github will automatically render the results of your notebooks, including charts and graphs. And for your bio and other details, you can create a simple ReadMe file in markdown and use Github Pages to turn it into a beautiful web page.
Build your portfolio with us
So now you know you need a portfolio if you plan to have a career in data science, and you know how to build a portfolio that’ll catch a recruiter’s eye. If you’re just getting started in machine learning and haven’t yet finished enough projects to flesh out your portfolio, we can help you out.
Our Data Scientist: Machine Learning Specialist career path will teach you all the skills you’ll need to kick off your career in machine learning. You’ll learn how to use Python and SQL to build machine learning models and manipulate data, and as you complete the course, you’ll build unique projects that you can include in your portfolio.