notes for future reference: 1. Self-assess: do you have the knowledge? the experience? How long will it take you to be ready; a few months? half a year? a year (or more?) 2. Theory test: many "top ML engineer questions" tips are mostly about theoretical questions; prepare for some, but these will be the minority of questions 3. Assess and build on practical knowledge: ML algorithms you've used, ML pipelines you've built and tools/methods (data collection, model building, etc) you've used and are familiar with 4. Practical test: 90% of the questions you'll get. Why/how did you make the decisions you did in your projects? Be very familiar with the ML pipeline, and how the ones you made work 5. Prepare for company-specific questions: What types of problems will they solve using ML? What type of data will they be working with? The answers probably won't be available online, but get an idea of what they might ask about about these topics
Great video and awesome timing. I have my first interview tomorrow. I will still need to go through the process you outlined for better or worse. I feel like I definitely should have done more projects.
I'm glad this was helpful! Don't worry about not having done enough projects, that's part of the process. The best thing you can do is be knowledgeable in the projects you have already done. All the best!
Hello smitha. Thanks for these informations which is very helpful for getting a successful in interview. Am currently learning about ML,DL and data science concepts and not from an IT background. Is it difficult for me to get a career in ML? and what are the skills I need to develop for entering into ML career?
notes for future reference:
1. Self-assess: do you have the knowledge? the experience? How long will it take you to be ready; a few months? half a year? a year (or more?)
2. Theory test: many "top ML engineer questions" tips are mostly about theoretical questions; prepare for some, but these will be the minority of questions
3. Assess and build on practical knowledge: ML algorithms you've used, ML pipelines you've built and tools/methods (data collection, model building, etc) you've used and are familiar with
4. Practical test: 90% of the questions you'll get. Why/how did you make the decisions you did in your projects? Be very familiar with the ML pipeline, and how the ones you made work
5. Prepare for company-specific questions: What types of problems will they solve using ML? What type of data will they be working with? The answers probably won't be available online, but get an idea of what they might ask about about these topics
Great video and awesome timing. I have my first interview tomorrow. I will still need to go through the process you outlined for better or worse. I feel like I definitely should have done more projects.
I'm glad this was helpful! Don't worry about not having done enough projects, that's part of the process. The best thing you can do is be knowledgeable in the projects you have already done. All the best!
Very good way to prepare for interviews!
Nice video Smitha. Your all videos are very interesting and informative
That was so helpful. Thank you Smitha for making content related to ML.
This was very insightful and clearly explained. Thank you for sharing.
A great video as always 👏
Hey Smitha, thank you for this video😊. It is very helpful. Please make a video on questions that are asked in the interview.
Thanks, will do!
Does ML and DL based research works could give an advantage in interviews?
Awesome! Great tips. Thanks
Very good!
Great Video Smitha
thanks for you content!
How to make a strong foundation of probability, Statistics and calculus.. assuming I've done a bit in high school and wants to start all over.
Thanks for sharing
Hello smitha. Thanks for these informations which is very helpful for getting a successful in interview. Am currently learning about ML,DL and data science concepts and not from an IT background. Is it difficult for me to get a career in ML? and what are the skills I need to develop for entering into ML career?
Great 👍❤
How to get an interview ???
That helpful sist
First like and comment
Noice, 👍
Pretty tech woman
first