Author Description

What is machine learning (ML)? What is data science (DS)? Ask any two data scientists either question and you will get two different answers. These subjects encompass a wide variety of data analysis techniques. Some of these techniques were invented and in common practice long before the invention of computers. Many new ones are being invented every day. Broadly speaking, these techniques encompass classification and regression, but both ML and DS have a wider reach. This is a book for hackers, programmers, engineers, scientists, and anyone else who wants to get down and dirty with machine learning but doesn't necessarily have the mathematical sophistication to learn a lot o advanced theory. Think of it as a lab manual in machine learning. The target audience is advanced community college students and lower division math, computer, and engineering students. Online supplement includes jupyter notebooks that contain all code examples discussed in the book.