I have always emphasized on the importance of mathematics in machine learning. Here is a compilation of resources (books, videos, and papers) to get you going. This is not an exhaustive list but I have carefully curated it based on my experience and observations. This is a repost of my Twitter thread that you can find here. I will keep updating the list here as I come across more useful resources.

In this article, I have shared a 3-month plan to learn mathematics for machine learning. As we know, almost all machine learning algorithms make use of concepts of Linear Algebra, Calculus, Probability & Statistics, etc. Some advanced algorithms and techniques also make use of subjects such as Measure Theory(a superset of probability theory), convex and non-convex optimization, and much more. To understand the machine learning algorithms and conduct research in machine learning and its related fields, the knowledge of mathematics becomes a requirement. The plan that I have shared in this article can be used to prepare for data science interviews, to strengthen mathematical concepts, or to start researching in machine learning. The plan will not only help in understanding the intuition behind machine learning but can also be used in many other advanced fields such as statistical signal processing, computational electrodynamics, etc.

Share this post In this post, we are going in deep with list of mathematics to be learned before going to start ahead in AI or Machine Learning Table of Contents What is Impact of Mathematics on Machine Learning? What is the Approximate Distribution ratio of Topics in Mathematics Detailed List of Mathematical topics Good Sources to learn Mathematics for Machine Learning 1. What is the Impact of Mathematics on Machine Learning or Artificial Intelligence(AI) Mathematics has an incredible impact on developing machine learning algorithms for real-time problem-solving. In the Machine Learning algorithm, learning insights from data is completely numerical one. The first algorithm ( i.e., Linear regression) to the last algorithm all are associated with Mathematics and Optimization.

So you want to learn the Mathematics for Machine Learning? Well, for Machine Learning or Deep Learning and AI, a thorough mathematical understanding is not an option. I know the options out there; prerequisites and the skills you need to become successful in Machine Learning and AI. If you want to learn Machine Learning, these classes will help you to master the mathematical foundation required for writing programs and algorithms for Machine Learning, Deep Learning and AI. My goal in this piece is to help you find the resources to gain good intuition and get you the hands-on experience you need with coding neural nets, stochastic gradient descent, and principal component analysis.