Data science

How to Create an Awesome Machine Learning Portfolio That Will Get You a Job?

Follow the steps and create your machine learning portfolio that will easily get you hired.
Portfolios are a great way to exhibit the accomplishments you would list on a resume or talk about in an interview. People always believe what you can show and not what you tell. Similarly, when you are applying for machine learning jobs without a portfolio your value lessens down. During a job search, the machine learning portfolio will display your work to potential employers.

In your portfolio, you should essentially mention the various projects that show the technical adeptness of your machine learning skills. Even experienced machine learning professionals create and update their machine learning portfolio to keep up and stay relevant to their machine learning skills.

 

The Format of Your Portfolio
For the machine learning portfolio, you can use GitHub or a personal website or blog. A personal blog or GitHub profile is a strong indicator that you are a potent machine learning engineer. To exhibit the machine learning projects that you have worked on it is important to have an active GitHub account. Besides this, having a personal blog channel can be beneficial too. You can advertise your machine learning skills by writing blogs with project presentations and also writing about your experience working with machine learning tools.

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.

Along with the presentation and blog the most important thing that you should always remember is that when you are presenting the projects and experiences you must explain them, this will draw the interviewer’s attention. You should briefly explain all your projects rather than just writing down the projects. The projects in the portfolio should narrate the story of your work and experience.

 

Quality of the Content
The content is the most important thing for portfolios. The quality of the content matters more than the quantity. You cannot just pick up random projects and work on them and add them to your portfolio. You must keep the focus on your domain expertise and accordingly work on the machine learning projects that are relevant. You cannot be an expert in all fields so choose your field very carefully and then choose your projects and work on them to add to the portfolio. It will not matter if you have worked on a few projects to the point, it is worthy and based on your domain.

 

Types of Projects to Include
You must be having confusion about what type of projects you should pick? So, on that note always try to select innovative projects to create your portfolio. Innovation always excites people and therefore it will surely excite the interviewer making him want to know more about the project. You should not work on machine learning projects that are common like spam detection or intrusion detection. 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 be excited to know more about like how you did the face recognition, how much data was required, and more. In short, pick a project that has an interesting application and also requires effort to collect data.

Data preparation, data pre-processing, data visualization, and storytelling are the main categories on which you should emphasize. Make sure that the machine learning portfolio has at least one project in each of these categories showcasing 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.

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