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.
The eBook for Data Mining for Business Intelligence (2nd edition) is now available for purchase from the Wiley website. (It is no longer available as a Kindle. Data Mining for Business Analytics. Concepts, Techniques, and Applications. 2nd Edition (2010) 1st Edition (2006) We're at a University Near.
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.
Reviews. 'Featuring complimentary access to XLMiner, the Microsoft Office Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of DM techniques are presented with hands-on, business-oriented applications.' (DMN News Wire, 7 March 2011).
'The book would be useful for a one or twosemester datamining course or a business intelligence course.' (The AmericanStatistician, 1 November 2011). 'The book would be useful for a one- or two-semester data mining course or a business intelligence course.' (The American Statistician, 1 November 2011).