DeepLearning.AI - Calculus for Machine Learning and Data Science 

Course Introduction by Andrew Ng

Course Introduction by Luis Serrano

Machine Learning Motivation

Motivation to Derivatives - Part I

Derivatives and Tangents

Slopes, maxima and minima

Derivatives and their notation

Some common derivatives - Lines

Some common Derivatives - Quadratics

Some common derivatives - Higher degree polynomials

Some common derivatives - Other power functions

The inverse function and its derivative

Derivative of trigonometric functions

Meaning of the Exponential (e)

The derivative of e^x

The derivative of log(x)

Existence of the derivative

Properties of the derivative: Multiplication by scalars

Properties of the derivative: The sum rule

Properties of the derivative: The product rule

Properties of the derivative: The chain rule

Introduction to Optimization

Optimization of squared loss - The one powerline problem

Optimization of squared loss - The two powerline problem

Optimization of squared loss - The three powerline problem

Optimization of log-loss - Part 1

Optimization of log-loss - Part 2

Week 1 - Conclusion

(Optional) Downloading your Notebook and Refreshing your Workspace

(Optional) Assignment Troubleshooting Tips

(Optional) Partial Grading for Assignments

Derivatives

Derivatives and Optimization

Introduction to Tangent planes

Partial derivatives - Part 1

Partial derivatives - Part 2

Gradients

Gradients and maxima/minima

Optimization with gradients: An example

Optimization using gradients - Analytical method

Optimization using Gradient Descent in one variable - Part 1

Optimization using Gradient Descent in one variable - Part 2

Optimization using Gradient Descent in two variables - Part 1

Optimization using Gradient Descent in two variables - Part 2

Optimization using Gradient Descent - Least squares

Optimization using Gradient Descent - Least squares with multiple observations

Week 2 - Conclusion

Partial Derivatives and Gradient

Partial Derivatives and Gradient Descent

Regression with a perceptron

Regression with a perceptron - Loss function

Regression with a perceptron - Gradient Descent

Classification with Perceptron

Classification with Perceptron - The sigmoid function

Classification with Perceptron - Gradient Descent

Classification with Perceptron - Calculating the derivatives

Classification with a Neural Network

Classification with a Neural Network - Minimizing log-loss

Gradient Descent and Backpropagation

Newton's Method

Newton's Method: An example

The second derivative

The Hessian

Hessians and concavity

Newton's Method for two variables

Week 3 - Conclusion

Acknowledgments

(Optional) Opportunity to Mentor Other Learners

Optimization in Neural Networks

Optimization in Neural Networks and Newton's Method