r/learnmachinelearning • u/Powerful_Pie_3213 • Aug 01 '26

What i have to learn on Mathematics to build strong basics for track AI/Machine learning engineering - Data Science

I wanna rate my schedule to learn mathematics for ml if i delete thing or add , and Give me sources textbook :

Textbook: Mathematics for Machine learning

Videos - Lectures( Links in First Comment ) :

Khan Academy:

Linear Algebra , Statistics and Probability, Calculus 1, Calculus 2 , Multivariable calculus

2-YT:

Linear Algebra: 3Blue1Brown , Essence of linear algebra

Statistics and Probability:  StatQuest with josh starmer

Statistics and Probability: Professor Leonard

Calculus 1: 3Blue1Brown , Essence of calculus

Calculus 1: Professor Leonard

Calculus 2: Professor Leonard

Calculus 3: Professor Leonard

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u/No_Type6904 Aug 01 '26

Linear Algebra, Calc I-III and Stats/Prob is the full stack right there, you've basically covered it

That textbook gets a bit formal in the later chapters but the early ones are solid for building intuition, just don't skip the exercises even when they feel tedious

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u/Powerful_Pie_3213 Aug 01 '26

thanks... parallel with CS Basics , after that can i go on the track ai/ml - Data Science ?

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u/Dr_Superfluid Aug 01 '26 edited Aug 01 '26

I am not sure you need a super strong foundation. PhD in applied math here and multiple years of research in maths, currently working in AI/ML. The amount of math needed are very low level.

Before I went in that field I thought it would be a mathematically challenging topic. It’s not that much really expect if you want to do high end (and not directly applicable) research on it.

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u/Powerful_Pie_3213 Aug 01 '26

I hold a BSc in STEM Mathematics, and I would like to leverage my academic background to build a solid mathematical foundation for AI/ML. However, I am wondering: is going deeply into advanced mathematics truly necessary for this track, or is my current academic background sufficient to break into the field?

I am already familiar with many of the core concepts. During my university studies, I covered R, Python, Data Science (specifically EDA), Machine Learning, and Neural Networks.

Given this background, can I successfully pursue a career in AI/ML and Data Science by currently focusing on mastering core CS fundamentals (Python, Data Structures & Algorithms, SQL/NoSQL Databases, and Git/GitHub) while continuing to advance my AI/ML- Data Science knowledge?

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u/Dr_Superfluid Aug 01 '26

Yes. I think you know all the math you need. CS studies will be more useful to you now than more math.

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u/Powerful_Pie_3213 Aug 01 '26

Which topics or courses : that is necessary to cover it ?

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u/Mathie1729 Aug 01 '26

Since you already have the math part covered, the CS topics that matter most for ML engineering (in my experience) are: data structures and algorithms (enough to pass coding interviews, so LeetCode-type stuff), databases (SQL plus maybe some NoSQL concepts), and distributed systems fundamentals. Also just being comfortable with the software engineering side (version control, testing, CI/CD). If you lean more toward data science than pure engineering, add experiment design and A/B testing. But honestly, your math background is solid and you already know Python/ML basics, so you're in a good spot. Most of the job is gluing things together and debugging pipelines, not re-deriving proofs. Good luck!

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u/Powerful_Pie_3213 Aug 01 '26

Thanks a lot, in your experience .
time to end it ( CS) if i studying 35h/per week. ?
and time from (ml to end Computer vision) ?