Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By : Cyrille Rossant
Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By: Cyrille Rossant

Overview of this book

Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.
Table of Contents (19 chapters)
IPython Interactive Computing and Visualization CookbookSecond Edition
Contributors
Preface
Index

Getting started with scikit-learn


In this recipe, we introduce the basics of the machine learning scikit-learn package (http://scikit-learn.org). This package is the main tool we will use throughout this chapter. Its clean API makes it easy to define, train, and test models.

We will show here a basic example of linear regression in the context of curve fitting. This toy example will allow us to illustrate key concepts such as linear models, overfitting, underfitting, regularization, and cross-validation.

Getting ready

You can find all instructions to install scikit-learn in the main documentation. For more information, refer to http://scikit-learn.org/stable/install.html. Anaconda comes with scikit-learn by default, but, if needed, you can install it manually by typing conda install scikit-learn in a Terminal.

How to do it...

We will generate a one-dimensional dataset with a simple model (including some noise), and we will try to fit a function to this data. With this function, we can predict...