Book Image

Python Data Science Essentials - Second Edition

By : Luca Massaron, Alberto Boschetti
Book Image

Python Data Science Essentials - Second Edition

By: Luca Massaron, Alberto Boschetti

Overview of this book

Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.
Table of Contents (13 chapters)
Python Data Science Essentials - Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Preparing tools and datasets


As introduced in the previous chapters, apart from the times when using special packages such as XGBoost for extreme gradient boosting and Keras for deep learning, the Python package for machine learning having the lion's share is Scikit-learn.

The motivations for using this open source package, developed at Inria, the French Institute for Research in Computer Science and Automation (https://www.inria.fr/en/), are multiple. It is worthwhile at this point to mention the most important reasons for using Scikit-learn for the success of your data science project:

  • A consistent API (fit, predict, transform, and partial_fit) across models that naturally helps to correctly implement data science procedures working on data organized in NumPy arrays

  • A complete selection of well-tested and scalable classical models for machine learning, offering many out-of-core implementations for learning from data that won't fit in your RAM memory

  • A steady development with many new additions...