Maths for machine learning course

Do we need to learn mathematics seperately from somewhere before starting this Machine Learning course, if yes, then please suggest some interesting resources for that.

Hi @Vidushi_29

It is generally recommended to have knowledge of mathematics before starting to learn Machine Learning. Knowing the following subjects can make your learning journey easier:

  1. Linear Algebra
  2. Calculus
  3. Probabilities & Statistics

In coursera, there is a Mathematics for Machine Learning Specialization that you can start with. Otherwise, there are lots channels on YouTube (3Blue1Brown, Khan Academy, StanfordOnline, etc.) or books that you can start with. However, you can also start your courses right away!

Hope this help, feel free to ask if you need further assistance!


The Math for Machine Learning course covers a lot of topics that are not related to Machine Learning. It is all interesting mathematics however. Whether to attend it depends on your interests.

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Hello, @Vidushi_29,

I think basic maths and basic Python are enough for this Machine Learning Specialization (MLS).

Being good at algebra, calculus and probability is a bonus as they will give you more insight of ML concepts, but it actually takes time and discussions to really fully leverage the maths knowledge, so without extensive use and discussion of maths, I do not think just taking a maths course will give you 100% of that bonus.

Maths will be essential if you go deep into the ML world, but it should be considered as a longer term study instead of just a prerequisite for this MLS, not to mention that basic maths should be sufficient here, because this MLS is considered beginner level .



Will it be fine if I just start the course of ML without learning math seperately?

@ rmwkwok
Thanks a lot for your advice. Can you please specifically tell what to study in maths, or should I just kickstart my journey of “Machine Learning Specialization”? That will be a great help.

@ Alireza_Saei
Thanks for your advice. Should I need to learn everything in deep? I am very confused like what should I start and from where.

It depends on how much math you already understand.

Note that the Machine Learning Specialization (MLS) existed for a long time before the “Math for Machine Learning” (M4ML) course was created. Since some students had a lot of questions about the underlying mathematical methods, the M4ML course was created for those who are curious about the details.

The MLS course assumes only that you understand basic algebra (i.e the equation of a straight line), and a little bit of statistics (like means, standard deviations, and probabilities for rolling dice).

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Oh okay. This basic math I know. Rest they will tell in the MLS course itself right?


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Thank you so much for your time.

Hi again,

Not that deep! Just an understanding of each subject mentioned is enough for MLS.

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Fine. Thanks a lot!

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@Vidushi_29, for example, in this MLS, you will see gradient formula derived for you. What will you do? Can you choose to accept the formula instead of proving it yourself? The course will not ask you to prove it, but will you insist it yourself? Sometimes some learners do insist it for their own reasons :wink:

There is nothing wrong to prove it yourself, but if you were not strong in calculus, then could you hold it, finish the MLS first, and decide whether you want to advance your calculus skills to prove it later?

This course will not ask you to do advanced maths. If you follow the flow of this specialization and leave any of your own maths adventure to a later time, then you may start MLS right the way!

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I have practical experience with this. My advise is to learn the concepts of maths for machine learning before diving deep into ML. You can have for some algorithms the mathematical concepts hidden behind the scene with libraries like scikitlearn doing the mathematical work, but when it comes to model explainability, you might really struggle if you have little mathematical and/or statistical background.

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