Mathematical Foundations of Machine Learning

Machine Learning is a rapidly growing field that relies heavily on mathematical concepts to analyze and make predictions from complex data. In this comprehensive course, you'll delve into the mathematical foundations of Machine Learning, covering linear algebra, calculus, probability theory, and statistics. You'll understand the importance of vector spaces, matrices, and linear transformations in linear algebra, and learn how to apply derivatives and integrals to optimize Machine Learning models through calculus. Probability theory will help you explore probability distributions, Bayes' theorem, and conditional probability in the context of Machine Learning. In statistics, you'll master concepts such as hypothesis testing, confidence intervals, and regression analysis to analyze and interpret Machine Learning results. Additionally, the course covers optimization techniques like gradient descent and stochastic gradient descent, information theory concepts such as entropy and mutual information, and linear models including linear regression and logistic regression. You will gain hands-on experience applying these mathematical concepts to real-world Machine Learning problems using popular programming languages like Python and R, as well as BI tools like Tableau and Power BI. Benefit from expert guidance to simplify complex mathematical concepts and provide personalized support.