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Monday, 13 February 2017

Learning Data Mining with Python

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 these insights, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis.

A highly recommended read would be Learning Data Mining with Python, Second Edition

What this book is about?

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number of these libraries, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. 
You will also get hands-on experience with a text mining exercise, and see its importance in usage of modern internet. 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. Further on, 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 a good knowledge and understanding of the algorithms and implementations.

Who will find this book useful?

If you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected.

What will you learn from this book?

  • 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

Why this book?

This book will be your comprehensive guide to learning the various data mining techniques and implementing them in Python. A variety of real-world datasets is used to explain various techniques in data mining, in a very crisp and easy-to-understand manner.

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 and on twitter @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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