Book Image

Python Machine Learning Blueprints - Second Edition

By : Combs, Chhajed, Roman
Book Image

Python Machine Learning Blueprints - Second Edition

By: Combs, Chhajed, Roman

Overview of this book

Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects. The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks. By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.
Table of Contents (13 chapters)

Sourcing shared counts and content

Before we can begin exploring which features make content shareable, we need to get our hands on a fair amount of content, as well as data on how often it's shared. Unfortunately, securing this type of data has gotten more difficult in the last few years. In fact, when the first edition of this book came out in 2016, this data was easily obtainable. But today, there appears to be no free sources of this type of data, though if you are willing to pay, you can still find it.

Fortunately for us, I have a dataset that was collected from a now defunct website, ruzzit.com. This site, when it was active, tracked the most shared content over time, which is exactly what we require for this project:

We'll begin by loading our imports into our notebook, as we always do, and then load in the data. This particular data is in the form of a JSON...