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Overview
This course bridges the gap between foundational mathematics and machine learning, making complex topics accessible to beginners and intermediate learners. Youâll explore key concepts in statisticsâsuch as probability distributions, correlation, and hypothesis testingâas well as essential linear algebra topics like vectors, matrices, and eigenvalues. The focus is on how these mathematical tools are applied in machine learning algorithms like linear regression, PCA, and neural networks. Through theory, visualizations, and practical exercises, the course builds a strong mathematical intuition that is...
Syllabus
- Introduction
- Core Math & Stats Concepts
- Getting Started With Math behind regression
- Linear Algebra in Machine Learning
- Difference between Machine Learning and Deep Learning in Mathematical Terms
- Math behind algorithms
- Quiz
- Bonus Lecture
Instructor
SeaportAi .
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