Learning Data Mining with Python, 2nd Edition

Key Features

• Use a wide variety of Python libraries for practical data mining purposes.
• Learn how to find, manipulate, analyze, and visualize data using Python.
• Step-by-step instructions on data mining techniques with Python that have real-world applications.

Book Description

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK.

You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now.

With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations.

What you will learn

• Apply data mining concepts to real-world problems
• Predict the outcome of sports matches based on past results
• Determine the author of a document based on their writing style
• Use APIs to download datasets from social media and other online services
• Find and extract good features from difficult datasets
• Create models that solve real-world problems
• Design and develop data mining applications using a variety of datasets
• Perform object detection in images using Deep Neural Networks
• Find meaningful insights from your data through intuitive visualizations
• Compute on big data, including real-time data from the internet

About the Author

Robert Layton is a data scientist working mainly on text mining problems for industries including the finance, information security, and transport sectors. He runs dataPipeline to build algorithms for practical use, and Eurekative, helping bringing start-ups to life in regional Australia. He has presented at the last four PyCon AU conferences, at multiple international research conferences, and has been training in some capacity for five years. He has a PhD in cybercrime analytics from the Internet Commerce Security Laboratory at Federation University Australia, where he was the Inaugural Young Alumni of the Year in 2014 and is currently and Honorary Research Fellow.

You can find him on LinkedIn at https://www.linkedin.com/in/drrobertlayton and on Twitter at @robertlayton.

Robert writes regularly on data mining and cybercrime, in a private, consultancy, and a research capacity. Robert is an Official Member of the Ballarat Hackerspace, where he helps grow the future-tech sector in regional Victoria.

Table of Contents

Chapter 1. Getting Started with Data Mining
Chapter 2. Using Python and the Jupyter Notebook
Chapter 3. A simple affinity analysis example
Chapter 4. Product recommendations
Chapter 5. A simple classification example
Chapter 6. What is classification?
Chapter 7. Summary
Chapter 8. Summary
Chapter 9. Authorship Attribution
Chapter 10. Clustering News Articles
Chapter 11. Object Detection in Images using Deep Neural Networks
Chapter 12. Working with Big Data
Chapter 13. Next Steps…