Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Recruiters are checking your online presence before contacting you about an interview.

There is a hype train going on about ML (Machine Learning), and many beginners are getting to be the victims of this hype train as they are getting in for the wrong reasons. Your professor will explain how getting a Ph.D. is necessary if you want to get better or your peers are telling you how to get a better GPU and IDE (Integrated Development Environment). When you started to learn from the online courses, you realized you needed a bigger dataset and proficiency in Python. After learning the required skills when you applied for a job, you realized that you need more than a few courses or certifications to make it. In the end, after getting the job, you realized that it is demanding work, and sometimes these jobs don't pay well at the initial stages. This article will help you get through these disappointments and prepare you to face these problems.

Recruiters nowadays are checking your online presence before contacting you about an interview. They will look for your LinkedIn profile, GitHub, and Kaggle to figure out what value you will bring to their company. The hiring manager will also look for the latest blogs or projects you have worked on in the past to prepare interview questions so that they can test your intelligence (catherinescareercorner). Other than that, working on real-world projects will give you the required experience for the job, and with a few projects in your portfolio, you will make a good impression on the recruiter (data-flair). We will be learning new ways to crack your interviews and how creating a strong portfolio has helped me aced multiple interviews.

Your portfolio project could make or break your chances of landing a job. With a growing number of people aspiring for a career in machine learning, it's important that you're able to distinguish yourself from the other candidates. In this light, many use their portfolio project to set themselves apart and grab the attention of hiring managers. You can work on competitions from Kaggle for your project. But, realizing the full experience of an ML engineer comes from working through the entire machine learning workflow, which is best done when working on your own unique project -- Kaggle skips the early stages of the machine learning workflow.

You just released a machine learning project. It can be a new product at your start-up, a proof of concept for a client demo, or a personal project that enriches your portfolio. You are not looking for a production-grade site; you want to get the job done. So that a few users can test your product. This post is a follow-up and an update over this previous post, where I introduced raplyrics.eu,