3. What key topics and practical skills will I learn?

Q: 3. What key topics and practical skills will I learn?

Course 1 – Linear Algebra (~34 hours): Covers vectors, matrices, dot products, determinants, eigenvalues/eigenvectors, and PCA with intuitive Python labs. Course 2 – Calculus (~26 hours): Teaches derivatives, gradient descent, optimization, and visual explanations of layered neural network gradients. Course 3 – Probability & Statistics (~33 hours): Includes probability distributions, hypothesis testing, confidence intervals, MLE/MAP estimation, and statistical reasoning. All courses emphasize visualization, intuitive understanding , and hands-on Python lab exercises using Jupyter notebooks.

Course 1 – Linear Algebra (~34 hours): Covers vectors, matrices, dot products, determinants, eigenvalues/eigenvectors, and PCA with intuitive Python labs.
Course 2 – Calculus (~26 hours): Teaches derivatives, gradient descent, optimization, and visual explanations of layered neural network gradients.
Course 3 – Probability & Statistics (~33 hours): Includes probability distributions, hypothesis testing, confidence intervals, MLE/MAP estimation, and statistical reasoning.
All courses emphasize visualization, intuitive understanding, and hands-on Python lab exercises using Jupyter notebooks.