Data Science and Machine Learning have fueled up interest in mathematics. A lot of people who are ramping up their skills in ML/AI domain have realized the practical applications of mathematical concepts, for the first time in their lives. During my venture into AI/ML space, I realized how difficult, mathematical ideas (such as calculus and vector algebra) were made in school and college than they really were! I am assuming a lot of people share this feeling. This article is an attempt to explain calculus and its applications, in a fundamental way without using the infamous jargons and big dreaded calculus equations.
How much math knowledge do you need for machine learning and deep learning? Some people say not much. Both are correct, depending on what you want to achieve. There are plenty of programming libraries, code snippets, and pretrained models that can get help you integrate machine learning into your applications without having a deep knowledge of the underlying math functions. At some point in your exploration and mastering of artificial intelligence, you'll need to come to terms with the lengthy and complicated equations that adorn AI whitepapers and machine learning textbooks.
This article is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. How much math knowledge do you need for machine learning and deep learning? Some people say not much. Both are correct, depending on what you want to achieve. There are plenty of programming libraries, code snippets, and pretrained models that can get help you integrate machine learning into your applications without having a deep knowledge of the underlying math functions.
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.
Much like its creator, Karl Weierstrass' monster came from nowhere. After four years at university spent drinking and fencing, Weierstrass had left empty handed. He eventually took a teaching course and spent most of the 1850s as a schoolteacher in Braunsberg. He hated life in the small Prussian town, finding it a lonely existence. His only respites were the mathematical problems he worked on between classes. But he had nobody to talk to about mathematics, and no technical library to study in. Even his results failed to escape the confines of Braunsberg.