Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications.

**Summary**

To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. *Math for Programmers* teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest programming fields.

**About the technology**

Skip the mathematical jargon: This one-of-a-kind book uses Python to teach the math you need to build games, simulations, 3D graphics, and machine learning algorithms. Discover how algebra and calculus come alive when you see them in code!

**What’s inside**

Vector geometry for computer graphics

Matrices and linear transformations

Core concepts from calculus

Simulation and optimization

Image and audio processing

Machine learning algorithms for regression and classification

**About the reader**

For programmers with basic skills in algebra.

**About the author**

**Paul Orland** is a programmer, software entrepreneur, and math enthusiast. He is co-founder of Tachyus, a start-up building predictive analytics software for the energy industry. You can find him online at www.paulor.land.

**Table of Contents**

1 Learning math with code

PART I – VECTORS AND GRAPHICS

2 Drawing with 2D vectors

3 Ascending to the 3D world

4 Transforming vectors and graphics

5 Computing transformations with matrices

6 Generalizing to higher dimensions

7 Solving systems of linear equations

PART 2 – CALCULUS AND PHYSICAL SIMULATION

8 Understanding rates of change

9 Simulating moving objects

10 Working with symbolic expressions

11 Simulating force fields

12 Optimizing a physical system

13 Analyzing sound waves with a Fourier series

PART 3 – MACHINE LEARNING APPLICATIONS

14 Fitting functions to data

15 Classifying data with logistic regression

16 Training neural networks