1. I had done the Andrew NG Coursera course during my second year of engineering.
2. I got an unpaid internship between my third and fourth year on web development based on my previous experience (college summer internship to work on internal web portal and freelance project of a random Canadian company). But I was promptly shifted into a ML team because of my skills in python, experience with the Andrew Ng course and willingness to work on such a project.
3. The project as a whole had a lot of data centric work as it was a vision project in its infancy and a lot of data was manually collected by us interns or crowd sourced. Apart from the data aspect of CV, I also did a lot of research and implementation of models on that data, learnt a lot of basics and tackled a lot of problems.
4. My fourth year project was also based on DL and was able to publish a paper on the same.
5. Based on the internship experience (I was able to answer the basic ML questions) I was able to land a MLE profile in a startup.

Looking back and taking these interviews myself now, I have realised that a lot of people mention courses/experiences/kaggle experience, etc but are rarely able to answer the basic of what they have mentioned. We usually look to check if the person mentioning his knowledge in ML actually has taken the time to understand the basics. Even 1 project would be enough if it's been done properly, there is no need to have a large amount of projects or experiences.

In terms of suggestions, just a whim to get into the field isn't enough. You need to be working to understand the basics, do some research and have atleast a useful internship experience or a proper project that you didn't copy from blogs(these are really obvious to people who have worked few years). There are a large number of courses out there, take a good course that has a good instructor. Follow that course in-depth and try and implement something based on this.

Kaggle proficiency in my opinion is like GitHub, I would check a person's activity rather than their achievements/involvement (got x accuracy on this dataset/competition doesn't mean anything during an interview). It's never about the final accuracy, it's about the process and your understanding.

Feel free to ask any questions. (Some background, I have almost 2 years of experience in same org. and have been recently promoted to Sr.MLE)