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.

About This Book

• 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.

Who This Book Is For

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 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

In Detail

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.

Style and approach

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 data mining techniques in a very crisp and easy to understand manner.

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Spis treści

Learning Data Mining with Python. Use Python to manipulate data and build predictive models - Second Edition eBook -- spis treści

• 1. Getting Started with data mining
• 2. Classification using scikit-learn estimators
• 3. Predicting Sports Winners with Decision Trees
• 4. Book Recommendations using Affinity Analysis
• 5. Features and scikit-learn transformers
• 6. Social media spam detection 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. Appendix: Next Steps