Learning Data Mining with Python

More Information
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
  • Set up reproducible experiments and generate robust results
  • Recommend movies, online celebrities, and news articles based on personal preferences
  • Compute on big data, including real-time data from the Internet
About

The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis.

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems.

There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK.

Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.

Features
  • Learn data mining in practical terms, using a wide variety of libraries and techniques
  • Learn how to find, manipulate, and analyze data using Python
  • Step-by-step instructions on creating real-world applications of data mining techniques
Page Count 344
Course Length 10 hours 19 minutes
ISBN 9781784396053
Date Of Publication 28 Jul 2015

Authors

Robert Layton

Robert Layton is a data scientist investigating data-driven applications to businesses across a number of sectors. He received a PhD investigating cybercrime analytics from the Internet Commerce Security Laboratory at Federation University Australia, before moving into industry, starting his own data analytics company dataPipeline. Next, he created Eureaktive, which works with tech-based startups on developing their proof-of-concepts and early-stage prototypes. Robert also runs the LearningTensorFlow website, which is one of the world's premier tutorial websites for Google's TensorFlow library.

Robert is an active member of the Python community, having used Python for more than 8 years. He has presented at PyConAU for the last four years and works with Python Charmers to provide Python-based training for businesses and professionals from a wide range of organisations.

Robert can be best reached via Twitter @robertlayton