I'm new to Machine Learning and currently in the midst of my placement process. While I'm not the best at DSA or web development, I do have some solid projects that help with interview selection. The good companies visiting my campus are pushing for at least basic ML questions, including some common algorithms. I've found a lecture (link included) and I want to know if by following it I can effectively answer these interview questions. Just to clarify, I'm not expecting this video alone to prepare me for all ML interviews, but if I learn from it and practice more ML questions (like top 100 ML interview questions), will I be in a decent position to tackle such inquiries? If anyone has better resources or suggestions, I'd love to hear them as I aim to complete this within a month while juggling my curriculum. Thanks in advance!
2 Answers
Why even apply to ML roles if it's not your area of expertise?
Getting into roles outside your current skills could be a good way to explore new opportunities. You never know what doors might open if you try!
Yes, you can definitely cover the basics of ML interview questions with that lecture and some dedicated practice. One thing I found helpful was creating a one-page cheat sheet covering concepts such as bias-variance tradeoff, regularization, train-test split, and common metrics. I would explain each topic aloud to reinforce my understanding, and I also set aside 20 to 30 minutes daily for flashcards and quick review questions. Combined with timed mock interviews using resources like the IQB question bank, that approach really helped me grasp the essentials in a month and feel prepared for campus interviews!
I’m aiming for campus placements. As a fresher, I doubt I can secure a job off-campus easily, and it seems that almost every company coming to my college is looking for candidates with at least a basic understanding of ML.