In this article, I have shared resources for advanced mathematics courses, which help machine learning. The topics discussed in this article are Convex and Non-Convex Optimization, Information Theory, Probabilistic Graphical Models, etc.

Mathematics forms the basis of most of the machine learning algorithms. Therefore, it is imperative to have a good grasp of mathematics to understand machine learning. While most of the data scientists are aware of basic mathematical concepts such as Linear Algebra, Statistics, etc. but many of them are not aware of some deep mathematical concepts that can help them have a clearer understanding of how an algorithm works or allow them to understand the latest research in machine learning.

In this article, I have shared resources for advanced mathematics courses, which help machine learning. The topics discussed in this article are **Convex and Non-Convex Optimization, Information Theory, Probabilistic Graphical Models, etc.**

The list of resources is given so that it assumes the reader’s familiarity with basic concepts such as **Linear Algebra, Probability Theory, Multivariable Calculus, and Multivariate Statistics**. It is vital to understand these essential topics to understand the material presented in the advanced courses present in this article.

I have also written another article on these topics which can act as a predecessor for this article. Feel free to check it out!

mathematics data-science education research machine-learning

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.

This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.