Learning Data Mining with Python: Harness the power of Python to analyze data and create insightful predictive models
About this ebook
- Apply data mining concepts to realworld 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 realworld 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 realtime data from the Internet
If you are a programmer who wants to get started with data mining, then this book is for you.
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About the author
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
