Learning Data Mining with Python: Second Edition

Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models.

Key Features

• 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.

Book Description

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

• 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

Table of Contents

1. Getting Started with Data Mining
2. Classifying with scikit-learn Estimators
3. Predicting Sports Winners with Decision Trees
4. Recommending Movies Using Affinity Analysis
5. Features and scikit-learn Transformers
6. Social Media Insight using Naive Bayes
7. Follow Recommendations Using Graph Mining
8. Beating CAPTCHAs with Neural Networks
9. Authorship Attribution
10. Clustering News Articles
11. Object Detection in Images using Deep Neural Networks
12. Working with Big Data
13. Next Steps...

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