This book assumes that you know close to nothing about Machine Learning. Its goal is
to give you the concepts, tools, and intuition you need to implement programs capable of learning from data.
We will cover a large number of techniques, from the simplest and most commonly
used (such as Linear Regression) to some of the Deep Learning techniques that regularly win competitions.
The book favors a hands-on approach, growing an intuitive understanding of Machine
Learning through concrete working examples and just a little bit of theory. While you
can read this book without picking up your laptop, I highly recommend you
experiment with the code examples available online as Jupyter notebooks at
https://github.com/ageron/handson-ml2. Prerequisites
This book assumes that you have some Python programming experience and that you
are familiar with Python’s main scientific libraries—in particular, NumPy, pandas, and
Matplotlib.