Learning Data Mining with Python - Second Edition

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  • 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

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.

  • 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.
Page Count 358
Course Length 10 hours 44 minutes
ISBN 9781787126787
Date Of Publication 26 Apr 2017


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