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
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.
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Posted on 5/14/2017 7:02:40 PM