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Some courses on Statistic, Linear Algebra and Calculus for Machine Learning

Where can I find quality course on the three subjects? I need some recommendations on some MOOC which help me start on Machine Learning (Preferably free, since I'm a student). I did learn those subjects in the university, most of the time, we learned about concepts and calculations, not about how to apply them in real world problems or Machine Learning.

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Dung’s Answer

Here is a useful link to free online classes you can take: https://www.forbes.com/sites/bernardmarr/2018/04/16/the-6-best-free-online-artificial-intelligence-courses-for-2018/61bab97b59d7

There are also many classes on udemy, udacity and coursera. You can also look up Andrew Ng's machine learning and artificial intelligence classes.


Good luck!
Thank you comment icon Thank for your suggestions! I absolutely love Dr. Andrew courses. However, I was looking for some courses that teach Calculus, Linear Algebra and Statistic in a more practical approach than those I learned at the University. I find it hard to learn advance machine learning/ deep learning courses without a solid background on those subjects. Dang Tuan Hoang
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Dung’s Answer

I think you can take the machine learning class on udacity first to understand what machine learning is. Once you have a better understanding of it you'll know what math courses to take. I assume being a student in Vietnam, you should have a solid foundation of Mathematics. They didn't go deeply into Statistics when I was in high school in Ho Chi Minh city, so maybe you can start with Statistics on Coursera. But understanding what the concept and different types of machine learning should be your first step. After that, you might want to start learning to code. I suggest python as it's the most popular programming language and you can use it to do almost everything. It also doesn't hurt that it's free.

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karthik’s Answer

In this answer, I will give a 5-month plan covering all important mathematical topics with suitable online learning resources arranged in chronological order. These resources helped me to intuitively understand the mathematics behind not only ML algorithms but many other advanced engineering fields like statistical signal processing, computational electrodynamics, etc. The mathematical papers in top ML conferences/journals might be overwhelming for those who approach ML only from a programming point of view (i.e. just using smart toolboxes, libraries and functions) and it can take weeks or months to understand all the derivations and prerequisite of each paper. As famously said: “Mathematics is the language of the universe” and hence it is the language of physics and since all engineering fields are directly or indirectly derived from physics, hence math is the language of engineering too. If you aspire to be an ML developer or researcher, there is no way to escape learning math. So here is the list of topics which are crucial for learning ML:
Linear Algebra (optional advanced topics include: Multi-linear and Tensor algebra)
Probability Theory (optional advanced topics: Measure Theory, Stochastic Processes, Information Theory)
Multivariable Calculus (optional advanced topics: Stochastic Calculus, Differential Equation)
Multivariate Statistics (optional advanced topics: Random Matrix Theory)
Convex Optimization (optional advanced topics: Stochastic and Non-Convex Optimization)
Five topics in bold font in the above list are essential to developing a mathematical understanding of ML algorithms. In fact, these topics are sufficient to understand complex topics in any field of engineering. The remaining topics which are marked as “optional” are simply an extension and are required for doing advance research.
Each of these topics can take months or years to master.
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