This course covers the core math you need to understand how machine learning works under the hood. No advanced degree required—just practical explanations and hands-on examples.

You’ll learn the key concepts from:

• Linear Algebra (vectors, matrices, operations)
• Calculus (derivatives, gradients, optimization)
• Probability and Statistics (distributions, Bayes’ theorem, expectation)

We’ll connect each topic to real ML use cases so you see why it matters—not just how it works.

By the end, you’ll be able to:

• Understand the math behind ML models
• Build stronger intuition for algorithms
• Read ML papers and docs with more confidence

This course bridges the gap between "just running code" and actually knowing what’s going on.

Course Duration: 38+ hours