@ParamSiddh: If I had to learn Math for Machine Learning from scratch, this is the roadmap I would follow:

If I had to learn Math for Machine Learning from scratch, this is the roadmap I would follow:

1. Linear Algebra

These are non-negotiables:

• Vectors • Matrices • Equations • Factorizations • Matrices and graphs • Linear transformations • Eigenvalues and eigenvectors

Now you’ve learned how to represent and transform data.

2. Calculus

Don’t skip any of these:

• Series • Functions • Sequences • Integration • Optimization • Differentiation • Limits and continuity

Now you understand the math behind algorithms like gradient descent and get a better feeling of what optimization is.

3. Multivariable Calculus

Here’s how you start:

• Multivariable functions • Derivatives and gradients • Optimization in multiple variables

In real life, neural networks involve functions with thousands of parameters, and you need to know how they change together.

4. Probability Theory

Learn this:

• Distributions • Expected values • Random variables

Now you know how to model uncertainty, learn from data, and make predictions.