Follow the steps and create your machine learning portfolio that will easily get you hired.
Portfolios are a great way to showcase accomplishments that you would list on a resume or talk about in a job interview. People always believe in what you can show and not what you say. Likewise, if you are applying for machine learning jobs without a portfolio, your worth lessens down. During a job search, the machine learning portfolio shows potential employers your work. In your portfolio, you should essentially mention the different projects that demonstrate the technical strength of your machine learning skills. Even seasoned machine learning experts create and update their machine learning portfolio to stay up-to-date and relevant with 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 capable machine learning engineer. It’s important to showcase the machine learning projects you’ve worked on, have an active GitHub account. In addition, a personal blog channel can also be beneficial. You can promote your machine learning skills by blogging with project presentations and also writing about your experience with machine learning tools. If using GitHub or any other code repository like your portfolio, make sure it always supports a readme file for each project that includes the purpose and results of the project along with graphics, images, videos and reference links if any. Also, make it easy for others to run the project again by providing clear instructions on how to download the project and reproduce the results.
Besides the presentation and the blog, the most important thing to always remember is that when presenting the projects and experiences, you need to explain them, this will grab the interviewer’s attention, you need to briefly explain all of your projects rather than simply the projects to write. The projects in the portfolio should tell the story of your work and experience.
Quality of the Content
Content is the most important thing for portfolios. The quality of the content is more important than the quantity. You can’t just pick random projects, work on them, and add them to your portfolio. You should keep the focus on your domain expertise and accordingly work on the relevant machine learning projects. You can’t be an expert in all areas, so choose your area of ​​expertise very carefully and then select and edit your projects to add to the portfolio. It doesn’t matter if you’ve got a few projects to the point, it’s worth it and builds on your skills.
Types of Projects to Include
Don’t you need to be sure which type of project to choose? Hence, always try to choose innovative projects to build your portfolio; Innovation always excites people and therefore will surely excite the interviewer by getting them to learn more about which machine learning projects should not be considered as screening for spam are common or intrusion detection. For example, if you’re a graduate engineering student who knows CNN and deep learning, you can create an automated attendance system where the interviewer would be happy to learn more about how you did facial recognition, how much data was required, and much more. In short, choose a project that has an interesting application and that also requires an effort to collect data. Data preparation, data preprocessing, data visualization, and storytelling are the main categories that you should highlight. Make sure the ML portfolio has at least one project in each of these categories. Showcase all of your machine learning skills to the potential employer along with at least one end-to-end machine learning project implementation, from conceptual understanding to real-world model assessment.
