Complete Mathematics for Data Science & Business Analytics

You will learn the Complete Mathematical Foundations of Machine Learning in this course. The Mathematical for Machine Learning course is a rigorous and essential program designed to provide you with a deep understanding of the mathematical principles that underpin modern machine learning algorithms. Tailored for students, researchers, and professionals, this course aims to bridge the gap between theory and application by focusing on the core mathematical concepts critical for building and analyzing machine learning models.

The course begins with an in-depth exploration of linear algebra, covering vectors, matrices, eigenvalues, eigenvectors, and matrix decomposition techniques such as Singular Value Decomposition (SVD). These concepts form the basis of numerous machine learning methods, including dimensionality reduction, principal component analysis (PCA), and deep learning. Next, the course delves into probability theory and statistics, equipping learners with the tools to model uncertainty, understand distributions, and perform inference.

Topics such as Bayes' theorem, conditional probability, expectation, variance, and statistical estimators are discussed with a focus on their relevance to probabilistic models and algorithms like Naïve Bayes, Gaussian Mixture Models, and Hidden Markov Models. The course also emphasizes calculus, particularly differential and integral calculus, which is fundamental for optimization in machine learning. Learners study gradients, partial derivatives, and chain rule applications, gaining the skills to understand and implement optimization algorithms like gradient descent and backpropagation in neural networks.

By the end of the course, You will possess a strong mathematical foundation to confidently explore, analyze, and develop advanced machine learning models. This deep knowledge is crucial for interpreting algorithm behavior, diagnosing model performance issues, and innovating new techniques. Whether you aim to become machine learning engineers, AI researchers, or data scientists, this course provides the critical theoretical toolkit required to succeed in the field.