Hi! I am transitioning my career to AI and have completed few beginners courses on Coursera by Andrew Ng, AI for Everyone(Completed), AI Python for Beginners(Ongoing), Machine Learning (Ongoing). I am about to complete Supervised Machine Learning: Regression and Classification course and one question came up in my mind that “Do we have to remember all of these algorithms, formulas on our fingertips?” I mean it’s not like we have to write/re-write these algorithms, functions manually for every project/task. Will it be enough just to remember what each algorithm does and where/how to use them? Or we actually do have to memorize the formulas too to became a Expert in Machine Learning?
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I would suggest this, and with practice they become more engulfed into ones memory!
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Thanks, it’s a relief to know that we don’t actually have to memorize each and every formula to became master or get a job.
No, You dont need to memories all formulas because in real projects, you mostly use libraries like scikit-learn or TensorFlow. The only thing you should remember is what each algorithms does, when to use and basic idea about how it works. Formula are useful only if you want to deeply understand or research.
I hope this will help.
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Thanks a lot @mamta25 It’s reassuring to hear from another person. It means a lot.
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In real machine learning work, you don’t need to memorize every formula—tools like scikit-learn and TensorFlow do the calculations for you. What’s important is understanding the basics: when to use a model, what assumptions it makes, and how to adjust it. You’ll learn the key formulas over time just by using them; no one expects you to remember them all. Instead, focus on the main ideas, handling data, and evaluating models—those are what really matter in projects.
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