Mathematics for Machine Learning
Multivariate Calculus

Looking for feedback on my Action Plan for Learning Machine Learning

Hello everyone, I am currently building an app and I want to integrate a Recommender system inside the app, so I decided to learn Machine learning. I'm going to be pursuing the Machine Learning course offered by Andrew NG on course era, but before I start it, I wanted to make sure I had the foundation to properly understand the concepts of Machine Learning. So this is the plan I made:

1) Linear Algebra
2) MIT Calculus 1A: Differentiation
3) MIT Calculus 1B: Integration
4) MIT Calculus 1C: Coordinate Systems & Infinite Series
5) Imperial College London: Mathematics for Machine Learning: Multivariate Calculus
5) MIT Fundamentals of Statistics
6) Finally, Stanford Machine Learning by Andrew Ng

I'm a college dropout and I want to get into learning machine learning so that I could start using concepts such as Recommender System in the app I am building, and hopefully eventually work towards getting a job in Machine Learning.

I have oriented on this question quite a bit and in general math is not used that much. It is when you’re more at the algorithm/deep learning side of things. It is however useful to have a grasp on some algebra, calculus and linear regression in particular. So I’m going to do these courses:

- [ ] Algebra https://www.coursera.org/learn/datasciencemathskills 
- [ ] Lineair https://www.coursera.org/learn/linear-algebra-machine-learning or https://github.com/fastai/numerical-linear-algebra/blob/master/README.md 
- [ ] Calculus https://www.edx.org/course/pre-university-calculus or https://www.coursera.org/learn/multivariate-calculus-machine-learning of easy way out —> http://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

Statistics is more useful to read up on.

For a MS in Applied Stats, Calculus (single- and multi-variable) and Linear Algebra are all you need. I can recommend some online course.

Linear Algebra: https://www.edx.org/course/linear-algebra-foundations-to-frontiers-0

Calculus: https://www.coursera.org/learn/multivariate-calculus-machine-learning

For courses that force you to pay to get your work graded, you can audit them and them supplement them by doing outside exercises.

Advice for a mathematical moron

I am interested in learning Machine Learning as a hobby and, maybe, in the distant future as a career. The problem is, I have a graduate degree in a totally unrelated field and am a dunce when it comes to math.

I read the Super Harsh Guide and quickly realized that Elements is well out of my depth, so I began reading the (apparently) easier Introduction to Statistical Learning; the material covered within is still somewhat beyond me. Are there any suggestions as to where to start for someone who knows very little math beyond basic introductory algebra? I know it's a big ask and I'm aware that I'll likely never work at Google Brain, however I'm really interested in the topic and would like to become more educated for my own personal satisfaction.

I have been looking at the Intro to Probability and Data course for introductory statistics and the Mathematics for Machine Learning: Linear algebra and Calculus courses for general math. Do these seem sufficient for getting into the Intro book? Contrarily, is this overkill/should I just read the Introduction to Stat Learning book and glean as much as I can without any prep? Will I even be able to understand these courses with only a basic algebra background?

I know this is a text dump; thanks for reading and please know that any insight is much appreciated.

My problem is that I did Math at a Graduate level but many years ago and I almost forgot everything. I need probably a refresher, my brain will probably pick it up.

I am thinking of the Math course for ML of the Imperial college of London https://www.coursera.org/learn/multivariate-calculus-machine-learning and I hope it's not an overkill. I will probably do it anyways because it would look good on a CV or Linkedin.

I would suggest, to first complete beginner-intermediate course on ML, then step into math. Because, when u start walking through math concepts and make links to practical ml algorithms, everything registers strongly. It also keeps you motivated, because when u learn a math concept u know how it relates to algorithms.

in math concepts, the order of importance, I would suggest is

1) Statistics - with hypothesis testing

2) Probability (book-Machine Learning A probabilistic perspective(chapters 2,5,6,))

3) Linear Algebra ( https://github.com/fastai/numerical-linear-algebra/blob/master/README.md )

4) calculus ( 3Blue1Brown(youtube) , https://www.coursera.org/learn/multivariate-calculus-machine-learning?skipBrowseRedirect=true )

This is what I did when I started getting into maths for machine learning.

1) Mathematics for Machine Learning: Linear Algebra coupled with 3b1b: Essence of linear algebra

Then

2) 3b1b: Essence of Calculus, Mathematics for Machine Learning: Multivariate Calculus and Khan Academy: Multivariate Calculus by 3b1b

After that, I did Andrew ng's machine learning and with all the understanding of mathematics in my head, this course turned out to be very easy.

Then after,

3) I learned Numpy, Pandas and did Mathematics for Machine Learning: PCA

I'm doing deep learning specialization and all these maths are helping me understand faster.

I suggest you, do the maths first and then the algorithms. You'll thank yourself later!

Good luck!

If you don't need accredited coursework, you can audit these for free. They're pretty good and cover most of the math you'll encounter in ML courses:

https://www.coursera.org/learn/linear-algebra-machine-learning

https://www.coursera.org/learn/multivariate-calculus-machine-learning

Free resources:

- videos in https://www2.isye.gatech.edu/\~sman/courses/6739/

-

https://www.coursera.org/learn/linear-algebra-machine-learning?specialization=mathematics-machine-learning

https://www.coursera.org/learn/multivariate-calculus-machine-learning?specialization=mathematics-machine-learning

https://www.3blue1brown.com/topics/linear-algebra

- The first 150 pages of Ian Goodfellow's Deep Learning book