## Recommended learning resources

In this book, we've only scratched the surface of the immense body of knowledge behind the term machine learning. If you want to learn more, we highly recommend the following resources.

The main criteria for choosing the courses and books were clarity of presentation and a CS-oriented approach. Other criteria for the books were free online availability and open source code samples. All courses mentioned in this list are free (as of May 2017) and of introductory level.

### Mathematical background

The handwritten comic-style lectures on Calculus by Robert Ghrist from the University of Pennsylvania can be found on YouTube or Coursera. This teaches single-variable calculus: Taylor series, Newton method. This should be your choice if you don't know how to take a derivative of a sigmoid function or which functions are differentiable. For more information refer to: https://www.math.upenn.edu/~ghrist/.

*Coding The Matrix: Linear Algebra Through Computer Science Applications...*