- Have crystal clear Mathematical foundations, as in why this formula/method the way it is, rather than being able to solve college/HS test problems. Really solid footing in Differential Calculus and Linear Algebra is necessary.
- Know the Statistical language that you learn from a basic college-level Stat 101 course. Be able to translate normal sentences into those using Statistical notation, and be able to read easily. Also, know basic Statistics.
- You already know programming, I assume. Learn Python if you don't know already. It's really easy.
- There are a number of paths you can go from there. Here's what I did.
-- IBM Data Science Professional Certificate (not deep at all, but lays out the landscape well; did it in a week)
-- Machine Learning for Absolute Beginners by Oliver Theobald which you can finish in an evening.
-- Machine Learning Specialization by Andrew Ng on Coursera.
-- Deep Learning Specialization by Andrew Ng on Coursera.
-- fast.ai course.
- Learn PyTorch really well. I suggest Sebastian Raschka's book.
Now from here, you can chart your own path. You can choose NLProc, Vision, RL, or something else.
I went towards Vision. And I do Edge AI as hobby.
I was in the last year of college as a Physics undergrad, when I was hired to do Vision modelling/research for a non-flashy company in 2021. Finishing my CS Master's next month and starting to look for PhD. I worked in the same company for the ~2.5 years.
EDIT: If you want a job in big tech, grind Leetcode, and learn about system design, study Machine Learning systems, and be able to design them. Chip Huyen has a good book as I hear. 6-7 rounds of interview is common in Meta/Google. DL hackathon awards, open source contributions are significantly helpful.
- Know the Statistical language that you learn from a basic college-level Stat 101 course. Be able to translate normal sentences into those using Statistical notation, and be able to read easily. Also, know basic Statistics.
- You already know programming, I assume. Learn Python if you don't know already. It's really easy.
- There are a number of paths you can go from there. Here's what I did.
-- IBM Data Science Professional Certificate (not deep at all, but lays out the landscape well; did it in a week)
-- Machine Learning for Absolute Beginners by Oliver Theobald which you can finish in an evening.
-- Machine Learning Specialization by Andrew Ng on Coursera.
-- Deep Learning Specialization by Andrew Ng on Coursera.
-- fast.ai course.
- Learn PyTorch really well. I suggest Sebastian Raschka's book.
Now from here, you can chart your own path. You can choose NLProc, Vision, RL, or something else.
I went towards Vision. And I do Edge AI as hobby.
I was in the last year of college as a Physics undergrad, when I was hired to do Vision modelling/research for a non-flashy company in 2021. Finishing my CS Master's next month and starting to look for PhD. I worked in the same company for the ~2.5 years.
EDIT: If you want a job in big tech, grind Leetcode, and learn about system design, study Machine Learning systems, and be able to design them. Chip Huyen has a good book as I hear. 6-7 rounds of interview is common in Meta/Google. DL hackathon awards, open source contributions are significantly helpful.