How to Build a Winning Machine Learning Portfolio that’ll Get You Hired?

Sakshi GuptaSakshi Gupta | 7 minute read | June 10, 2020
Machine Learning Portfolio

Applying for machine learning jobs without a machine learning portfolio is like boarding a plane without a passport. It is the most important asset because it is the first point of contact with a potential employer that helps one establish themselves as a brand and sell their machine learning skills. The most frequent question our mentors at Springboard get from machine learning beginners is – “Are we really going to build a machine learning portfolio? What can we possibly include in the portfolio when we’ve never worked for any company on machine learning projects?” And it’s a great question! If you want to start getting those machine learning interview calls, it’s a simple fact that you ought to show your machine learning skills through diverse projects – but the good news is, that doesn’t have to mean paid work. You can absolutely mention about the various projects that show the technical competency of your machine learning skills. In fact, even experienced machine learning practitioners build and update their machine learning portfolio to keep up and stay relevant to their machine learning skills.

Machine Learning Portfolio – Why do you need one?

“Proof of your machine learning skills matter than what you state.”

Here are the key pointers explaining why one needs a machine learning portfolio.

  • Machine learning portfolio is your secret sauce to landing the first machine learning job interview
  • Gets you internships and freelance projects.
  • Helps stay relevant in your machine learning career.

A big part of a machine learning engineer’s career is spent in communicating the insights to other members of the team. However, to make a career in machine learning, one type of communication that a machine learning engineer has to master is the ability to showcase their machine learning skills to the employers who are likely to hire them. To achieve this, every machine learning engineer should develop a machine learning portfolio consisting of a collection of projects they have worked on and how each of these machine learning projects demonstrates the machine learning skills they have developed over time. A compelling machine learning portfolio is the secret for a successful machine learning career. Putting a machine learning portfolio together is an intensive process, but the beauty of having a well-thought-out machine learning portfolio is that it gives the recruiter a proof of your machine learning skills, as well as rewards you with your dream machine learning job. Without much ado, let’s get started on how to build an eye-grabbing machine learning portfolio.

1. Machine Learning Portfolio –  The Format

A machine learning portfolio may come in one of two main flavours -GitHub or a personal website or blog. A personal blog or GitHub profile is a powerful indicator that you are a competent machine learning engineer. Having an active GitHub account is important to showcase the machine learning projects you’ve worked on.  Also, having some kind of a personal website or blog can be highly beneficial because a lot of machine learning is about communicating the findings and presenting the data. Writing a blog about the machine learning projects you’ve worked on with carefully-constructed project presentations or sharing your experience working with a machine learning tool on the blog is of great value and a great form of advertising your machine learning skills to the employer. You can also use Medium for blogging to host your machine learning portfolio along with a link to your GitHub account as it helps reach a greater audience and gets a good amount of visibility in the data science and machine learning community .

If using GitHub or any other code repository as your portfolio, make sure it is always supported with a readme file for each project which contains the purpose and findings of the project along with graphs, visuals, videos, and reference links, if any. Also, make it easy for others to re-run the project by providing clear instructions on how to download the project and reproduce the results.

Regardless of the presentation format of your portfolio, a golden rule to remember is: “Machine learning portfolios are all about process, not just the end result.” To clarify, this implies that your portfolio is not a mere bunch of machine learning projects that you worked on. Each of the machine learning projects in the portfolio must be accompanied by a clear explanation that draws the interviewer’s attention on what went behind the scene to produce the model. In short, the projects in the portfolio should clearly narrate the story (right from data collection to summarizing your findings)  behind the machine learning model you developed. 

2. Machine Learning Portfolio: The Content

A great machine learning portfolio is a collection of independent machine learning projects that prove a candidate has all the required machine learning skills for the job he/she is applying for. However, do not just pick some random projects to work on and add them to your portfolio. One cannot be an expert in all the domains, so leverage your domain expertise and work on the most relevant machine learning projects or projects that relate to the companies that you are interested in working for.   Tailoring the machine learning projects to the specific machine learning jobs you’re applying for can be of added advantage in acing the interview.

3. Machine Learning Portfolio: Type of Projects To Include

Do not work on generic run-off-the-mill machine learning projects like Intrusion detection or Spam detection. Always try to pick up innovative projects to build a portfolio that excites the interviewer making him want to know more about the project.  For instance, if you are a final engineering student who knows about CNN and Deep Learning, you can build an automated attendance system that the interviewer would definitely be excited to know more about like – how did you do the face recognition, how much data was required, and more. To summarize, the secret to a great machine learning project for the portfolio is to choose a project that has an interesting application and also requires effort to collect data. 

The workflow of a machine learning project involves various steps like – data preparation, data pre-processing , data visualization and storytelling. Make sure that the machine learning portfolio has at least one project in each of these categories demonstrating your well-rounded set of machine learning skills to the prospective employer along with at least one end-to-end machine learning project implementation right from conceptual understanding to a real-world model evaluation. 

Tips to Build a Good End-to-End Machine Learning Project

  • Always choose an interesting topic that has a publicly accessible dataset.
  • Work with as much data as you are comfortable by importing and parsing multiple datasets. Do not limit yourself.
  • Have a clear understanding of the various predictions you want to make using the dataset before data preparation. 
  • Always clean up the code by splitting it into multiple files and document it before uploading it to GitHub or any other code repository.

Having said that, each project in the portfolio should explore –

  • The various features of a popular machine learning library or a tool.
  • The behaviour of the machine learning algorithm under various conditions.
  • Should have an end-to-end implementation of the algorithm in any of your favourite programming languages.

4. Machine Learning Portfolio – The Add-Ons

While having a list of machine learning projects is the key to having great machine learning portfolio, adding additional components like – referring to a few trending quora questions about machine learning that you have answered, mentions about your participation in data science competitions or hackathons on Kaggle or other websites, and talks you presented at meetups on interesting machine learning concepts or projects are some nice secondary add-ons to make your portfolio stand out.

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Checklist For an Effective and Impressive Machine Learning Portfolio 

These are the questions you need to ask yourself while preparing a machine learning portfolio –

  • Is this your very best machine learning project presented in the best possible way ?
  • Does the portfolio showcase a well-rounded set of machine learning skills?
  • Do the projects included in the machine learning portfolio match with the type of machine learning job I am applying for?
  • Is it small, accessible, readable,  and complete?

If you have answered YES to all these questions, you definitely have a great machine learning portfolio to land some of the best machine learning job opportunities.

Related Read: Data Scientist Job Description

Over To You…Get Started Now! 

There you have it! With a few simple steps on how to build a successful machine learning portfolio, you no longer have any excuses or reasons to keep putting off building your own portfolio.  Dig up your machine learning projects and weave a story around them such that it shows your passion, interest and skills in machine learning. A pro tip to make your machine learning portfolio from “meh” to “impressive” is to devote enough time creating it such that it shows off all your machine learning skills and increases your employability as a machine learning engineer. Keep on building and adding machine learning projects to your portfolio, and you will have a machine learning job in no time!

At Springboard, our mentor-led Artificial Intelligence and Machine Learning program have 14 guided real-world projects on NLP, Image Processing, and Deep Learning that are designed to help you start with building an effective machine learning portfolio to showcase your machine learning skills to prospective employers and land a top gig as a machine learning engineer.

Since you’re here…Are you a future data scientist? Investigate with our free step-by-step guide to getting started in the industry. When you’re ready to build a CV that will make hiring managers melt, join our 4-week Data Science Prep Course or our Data Science Bootcamp—you’ll get a job in data science or we’ll refund your tuition.

Sakshi Gupta

About Sakshi Gupta

Sakshi is a Senior Associate Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer and has experience working in the Indian and US markets.