{"id":22916,"date":"2023-01-27T03:16:29","date_gmt":"2023-01-27T11:16:29","guid":{"rendered":"https:\/\/www.springboard.com\/blog\/?p=22916"},"modified":"2023-08-04T23:20:00","modified_gmt":"2023-08-05T06:20:00","slug":"machine-learning-portfolio","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/machine-learning-portfolio\/","title":{"rendered":"How To Build a Machine Learning Portfolio (+ Examples)"},"content":{"rendered":"\n

The applications of machine learning are numerous, and the field is only growing. From self-driving cars to early cancer detection, machine learning\u2019s capacity to learn new information based on existing patterns or data is changing the way that we live.<\/p>\n\n\n\n

Because of this, machine learning engineers are in demand. And while educational requirements and experience are important, most hiring managers will tell you that an application\u2019s portfolio is one of the most important parts of their package.  <\/p>\n\n\n\n

So if you\u2019re looking to join this exciting field, and want to build out a compelling portfolio, then you\u2019re in the right place. Below, we\u2019ll show you how to build a machine learning portfolio, and offer up some winning examples, so that you\u2019re one step closer to landing the machine learning job of your dreams. <\/p>\n\n\n\n

What Is a Machine Learning Portfolio?<\/h2>\n\n\n\n

A machine learning portfolio is a collection of personal projects and learning experiences demonstrating your ability to apply machine learning principles to real-world problems. It also gives employers an idea of what you’re capable of in terms of implementation and design.<\/p>\n\n\n\n

Why Do You Need a Machine Learning Portfolio?<\/h2>\n\n\n\n

Machine learning is a highly technical field, and it can be challenging to show off your skills in a way that’s easy for an employer to understand. A machine learning portfolio is a perfect solution for this problem because it demonstrates your expertise and experience in one place.<\/p>\n\n\n\n

Elements of a Winning Machine Learning Portfolio (Format and Content)<\/h2>\n\n\n\n
\"Elements<\/figure>\n\n\n\n

When building a machine learning portfolio, it’s important to remember that you’re not just building a project\u2014you’re demonstrating your reputation. Your portfolio is your calling card and should reflect everything you want potential employers or clients to know about you: your skills, interests, and experience.<\/p>\n\n\n\n

In this section, we\u2019ll discuss the elements of a winning machine learning portfolio.<\/p>\n\n\n\n

A Short Description Highlighting You, Your Work, Your Domain Expertise, and Your Related Interests<\/h3>\n\n\n\n

Your short description should be no longer than two paragraphs. First, introduce yourself, including your name and details about your background. Then describe your work experience in machine learning, especially recently completed personal projects. Finally, mention related interests or passions.<\/p>\n\n\n\n

Skillsets and Tools You Know<\/h3>\n\n\n\n

This is where you should list all of the languages<\/a> and frameworks<\/a> you know well enough to use in production environments. Mention all the skills<\/a> you have acquired throughout your experience using tools such as TensorFlow<\/a>, Scikit-learn<\/a>, or Keras<\/a>. You will also want to include any machine learning algorithms <\/a>you have mastered over time and use regularly.<\/p>\n\n\n\n

Your Best Projects<\/h3>\n\n\n\n

Your portfolio should include links to some of your best projects so clients can see exactly what work you do best.<\/p>\n\n\n\n

Key Topics To Include for Each Project<\/h4>\n\n\n\n

Here are the details you should include for each of your projects:<\/p>\n\n\n\n

General Details<\/h5>\n\n\n\n

The general details of the machine learning project should be included in the first part of your post. This will help potential employers understand what you’re trying to achieve and why it’s important.<\/p>\n\n\n\n

Tools and Methods Used<\/h5>\n\n\n\n

You should include a short list of tools you used for this project and any methods or processes you followed. This gives readers a better idea of how you built your project and why you chose those particular tools.<\/p>\n\n\n\n

Results<\/h5>\n\n\n\n

Highlight the results you got from your model or system and why you think this happened. Were there any outliers or anomalies in your data that affected the results? Can you explain why these occurred, even if they weren’t expected?<\/p>\n\n\n\n

Learnings and Takeaways<\/h5>\n\n\n\n

Finally, you\u2019ll want to leave the readers with some takeaways. Did anything surprising happen during this process that changed how you think about machine learning at large? Did any personal growth occur as a result of working on this project? <\/p>\n\n\n\n

Achievements and Social Proof<\/h3>\n\n\n\n

Make sure to include any relevant achievements or awards from previous jobs. This could be anything from a list of awards you’ve won, data science courses<\/a> you completed, a list of companies you\u2019ve worked with, or testimonials from previous managers, colleagues, and friends.<\/p>\n\n\n\n

GitHub Profile Link<\/h3>\n\n\n\n

If you’re looking for a job in machine learning and still need to gain experience, this might be the most important part of your portfolio. The only thing worse than having zero experience is having no way to show off your skills when companies look at your resume. An excellent way to avoid this problem is to show off some projects on GitHub (and then link them from there). <\/p>\n\n\n\n

An Easy Way To Reach You<\/h3>\n\n\n\n

If an employer wants more information about one of your projects, having a straightforward way for them to reach you is essential. Include your email address, phone number, and relevant social media handles.<\/p>\n\n\n\n

Add-Ons (as Needed)<\/h3>\n\n\n\n

Here are other topics worth mentioning that can further impress potential employers. <\/p>\n\n\n\n

Volunteer Work<\/h4>\n\n\n\n

You want to show that you can be a team player and work on projects that are bigger than yourself. If you don\u2019t have any volunteering experience yet, ask around. Most companies have a volunteer program that accepts applications from people who want to help with specific machine learning projects<\/a>.<\/p>\n\n\n\n

Participation in Hackathons and Competitions<\/h4>\n\n\n\n

Another great way to show off your skills is through competitions like hackathons or coding competitions. These will allow you to work with a team, which employers look for in potential employees. <\/p>\n\n\n\n

Reports and Articles<\/h4>\n\n\n\n

If you’ve written any reports or articles related to machine learning, ensure they’re included in your portfolio. It’s good practice to publish research results online so that other researchers can see what kind of work you’ve done and how it compares with other researchers’ findings on similar topics.<\/p>\n\n\n\n

Any Conferences Where You\u2019ve Presented Your Work<\/h4>\n\n\n\n

If you\u2019ve ever presented your work at a conference, include the details in your portfolio. This is considered an outstanding achievement and can give you a massive advantage over other candidates.<\/p>\n\n\n\n

Tips for Building an Effective Machine Learning Portfolio<\/h2>\n\n\n\n
\"Tips<\/figure>\n\n\n\n

Here are some of the best ways to build a compelling portfolio that showcases your machine learning skills and helps you land the job you want.<\/p>\n\n\n\n

How To Make a Great First Impression<\/h3>\n\n\n\n

Here are six tips on how to make your portfolio stand out from the crowd:<\/p>\n\n\n\n

Show, Don’t Tell<\/h4>\n\n\n\n

You can’t just tell someone that you have some knowledge of machine learning\u2014you have to show them. The best way to go about this is by providing examples and demonstrations of your work rather than just writing about your projects. Showcase the original code, synthesize the techniques you used, and quantify your results.<\/p>\n\n\n\n

Ensure Your Portfolio Loads Quickly and Is Easily Navigable<\/h4>\n\n\n\n

Your portfolio should load quickly and be easy to navigate so the hiring manager can get the complete picture of what you’ve been working on without spending too much time clicking around. <\/p>\n\n\n\n

Try Adding a Separate Page or Section for Each Project To Go Into Better Detail<\/h4>\n\n\n\n

Adding separate pages for each project lets the hiring manager get a more in-depth look at what you’ve accomplished with each project and keeps your portfolio more organized and clean.<\/p>\n\n\n\n

Always Keep Your Portfolio Up to Date<\/h4>\n\n\n\n

It’s essential to always keep your portfolio up to date because machine learning is an ever-changing field, and you need to keep track of the new technology. Not only does this help others see how much progress you’re making, but it also shows them that you’re dedicated to your work. <\/p>\n\n\n\n

Link to Your GitHub and LinkedIn Profiles<\/h4>\n\n\n\n

If you have projects hosted on GitHub or other similar sites, make sure there’s a link somewhere on your website or blog so employers can easily find them. It’s also helpful if you include links to any articles about these projects, so hiring managers can read more about what went into making the project successful.<\/p>\n\n\n\n

Aesthetics and Readability Matter<\/h4>\n\n\n\n

You want people to navigate through your content easily, so make sure your portfolio is clearly laid out and easy to read.<\/p>\n\n\n\n

Seek Feedback From a Mentor Before Publishing Your Portfolio<\/h4>\n\n\n\n

Before publishing a machine learning portfolio, seek feedback from a mentor<\/a> or peer. Not only will this help you refine your message, but it will also provide valuable insight into how others perceive you and your work.<\/p>\n\n\n

Get To Know Other\tData Science Students<\/h4>
\"Jonathan<\/a>

Jonathan King<\/p>

Sr. Healthcare Analyst at IBM<\/p><\/div>

<\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Leoman<\/a>

Leoman Momoh<\/p>

Senior Data Engineer at Enterprise Products<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Bryan<\/a>

Bryan Dickinson<\/p>

Senior Marketing Analyst at REI<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n

What To Avoid<\/h3>\n\n\n\n

You’ve got many options for what you can showcase in your portfolio, but what should you avoid? <\/p>\n\n\n\n

Don’t Copy Someone Else’s Portfolio<\/h4>\n\n\n\n

It’s tempting to look at another person\u2019s portfolio and think, “I want a portfolio just like that.” But don’t copy someone else’s work. You want to show off the best of your abilities, so make sure your portfolio is yours uniquely.<\/p>\n\n\n\n

Show Your Best Work<\/h4>\n\n\n\n

If there are projects that you’re not particularly proud of, consider leaving them out of your portfolio entirely. When building out your portfolio, including everything that might impress hiring managers may be tempting. However, only focus on what you think is essential and make sure that’s reflected in what you include on your resume or online application form.<\/p>\n\n\n\n

Final Checklist for Your Machine Learning Portfolio<\/h2>\n\n\n\n
\"Final<\/figure>\n\n\n\n

To ensure your portfolio is as strong as possible, we’ve put together a checklist of things to consider before sending off your application.<\/p>\n\n\n\n

Are These Your Best Machine Learning Projects?<\/h3>\n\n\n\n

The first question to ask yourself is whether or not your portfolio is your very best work. If it’s not, then you need to go back and refine it before you send it out.<\/p>\n\n\n\n

Does Your Portfolio Showcase a Well-Rounded Set of Machine-Learning Skills?<\/h3>\n\n\n\n

If you have time, look through your past projects and make sure that they showcase a well-rounded set of machine-learning skills. If there are gaps in what you’ve done, try to fill them in by learning new algorithms or programming languages.<\/p>\n\n\n\n

Do the Projects in Your Machine Learning Portfolio Match the Type of Job You\u2019re Applying For?<\/h3>\n\n\n\n

The next thing to consider is whether the projects in your portfolio match the job you’re applying for. Make sure that any particular skills you want to highlight are present in at least one of the projects included in your portfolio.<\/p>\n\n\n\n

Is Your Portfolio to the Point, Accessible, Readable, and Complete?<\/h3>\n\n\n\n

Make sure your portfolio is complete before sending it off. Ask yourself these questions:<\/p>\n\n\n\n