chapter one

1 What is machine learning?

 

It is common sense, except done by a computer

This chapter covers:

  • What is machine learning?
  • Is machine learning hard? (Spoiler: No)
  • What do we learn in this book?
  • What is artificial intelligence and how does it differ from machine learning?
  • How do humans think, and how can we inject those ideas into a machine?
  • Some basic machine learning examples in real life.

I am super happy to join you in your learning journey!

Welcome to this book! I’m super happy to be joining you in this journey through understanding machine learning.  At a high level, machine learning is a process in which the computer solves problems and makes decisions in much the same way as humans.

In this book, I want to bring one message to you, and it is: Machine learning is easy! You do not need to have a heavy math and programming background to understand it. You do need some basic mathematics, but the main ingredients are common sense, a good visual intuition, and a desire to learn and to apply these methods to anything that you are passionate about and where you want to make an improvement in the world. I’ve had an absolute blast writing this book, as I love understanding these topics more and more, and I hope you have a blast reading it and diving deep into machine learning!

Machine learning is everywhere

1.1       Why this book?

1.1.1   We play the music of machine learning; the formulas and code come later

1.2       Do I need a heavy math and coding background to understand machine learning?

1.2.1   Formulas and code are fun when seen as a language

1.3       Ok, so what exactly is machine learning?

1.3.1   What is artificial intelligence?

1.3.2   What is machine learning?

1.3.3   And now that we’re at it, what is deep learning?

1.4       How to get machines to make decisions with data? The remember-formulate-predict framework

1.4.1   How do humans think?

1.4.2   Some machine learning lingo - models and algorithms

1.4.3   Some examples of models that humans use

1.4.4   Some examples of models that machines use

1.5       What is this book about?

1.5.1   Types of chapters

1.5.2   Recommended learning paths

1.5.3   Appendices

1.5.4   Requirements and learning goals

1.6       Other resources

1.7       We’ll be writing code

1.8       Summary