I get asked this question a lot and will like to answer this in a post here. :)
There are a lot of "rumors" swirling around that machine learning is very tough mathematics or requires a lot of mathematics. This created some fear and perhaps even panic as well, when folks want to consider Data Science or Artificial Intelligence (AI) as their career.
Let me try to allay that fear as much as possible. :)
Cannot Run Away!
Sorry to those that do not like mathematics, but want to make Data Science or Artificial Intelligence as your career. My answer to you is, you cannot run away! Or maybe to give a more detail explanation, your analysis or interpretation of insights can be terribly misguided if you do not know the maths behind the statistics and models that you have generated. It will be like holding a bomb in your hand but you do not know when it will explode in your face.
However, hear me out here, the mathematics that is needed to for you to kickstart your career, is something that you have covered before during your high school and university education. Why you have terrifying nightmares about it, which I am speculating, is because in school we are made to memorise all theses maths rules without any idea how they are applied, except for us to gain marks in our exams! (Who still know how to find the roots of quadratic equations? :P)
Job Scope
How much mathematics you need really depends on the job scope you are going for.
1) Normal Business Needs
If you are a data scientist working to train models that is going to be embedded into business process, you really do not need hard-core mathematics. You can implement summary statistics, and machine learning models with ready packages from Python or other enterprise software you are using.
You need enough mathematics to understand how the different machine learning models does their training. That will be linear algebra, calculus and statistics. Those that you cover in a degree program will be a suitable level as a start.
If you are keen on such a job scope, which usually cover over 80% of the job position are, what is discussed should be great news for you. I strongly encourage you to pick up again. I find what is available in the Khan Academy to be a great FREE resource.
2) Business Innovation/Academic Research
If this is the are that you are interested in, you actually want to be a Research Scientist. Of course, this means that you will need to understand other branches of mathematics then for instance, geometry, mathematical analysis, number theory, etc. The more you know, the more value you can create as you work on getting an innovative solution. Besides knowing the different branches, having a good ability to integrate them together to solve the challenge at hand will be another good competency to have for sure. :)
Conclusion
Whichever job scope you are going for, I will strongly encourage you to start your maths journey with Linear Algebra, Calculus, and Statistics. With these topics as foundations, they can give you the flexibility in which career option you are going for later, a Business Data Scientist or a Research Scientist.
I have written a more detailed post on the mathematics you should study for a data career, give it a read here.
I wish you all the best in your data career! If you find this to be useful, share it please! :)
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