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Put Python to work in your data and AI projects!

Python for AI and Data Analysis The Practical Guide for Business and Science

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$44.99

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$59.99

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282 pages, , Print edition paperback
ISBN 978-1-4932-2892-8 282 pages,
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2893-5 282 pages, , Print edition paperback
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2894-2
Not a programmer by trade, but still want to use Python and AI in your day-to-day work? No problem! Learn how to use Python to analyze data, automate tasks, and work with models without getting bogged down by unnecessary busy work. You’ll learn about key tools like Visual Studio Code and Jupyter Notebooks and then jump straight into data analysis and visualization with NumPy, pandas, and Matplotlib. Hands-on examples will have you learning to clean, filter, and transform data, perform calculations, and create charts and interactive plots—right out of the gate!
  • A practical guide to Python for AI and data projects
  • Learn to analyze and visualize data with NumPy, pandas, and Matplotlib
  • Use AI and machine learning for predictive modeling, text analysis, and image recognition
About the E-book About the Book E-book in full color. PDF (16 MB) and EPUB (5 MB) files for download, DRM-free with personalized digital watermark. Copy and paste, bookmarks, and print-out permitted. Table of contents, in-text references, and index fully linked. Including online book edition in dedicated reader application. 282 pages, paperback, .65 in. Reference book format 7 x 10 in. Printed black and white on 60# offset paper from sustainable sources. Reader-friendly serif font (TheAntiquaB 9.5 Pt.). One-column layout.

In this book, you'll learn about:

  1. Data Analysis and Visualization

    Get hands on with the Python data stack. Use NumPy and pandas to load, clean, filter, and transform data from CSV, Excel, JSON, and other formats. Then turn your results into compelling charts and interactive plots with Matplotlib.

  2. Machine Learning and AI

    Apply the tools professionals use! Work through classical machine learning methods—linear regression, decision trees, clustering, and more—before moving into natural language processing, sentiment analysis, image classification, and deep learning.

  3. Automation and Real-World Applications

    Take Python beyond analysis. Build lightweight web tools with Flask and Streamlit, scrape web pages, query databases, and automate recurring tasks—from PDF processing and document generation to scheduled data retrieval and email notifications.

Highlights include:

  • Python fundamentals
  • Data analysis
  • Data visualization
  • Machine learning
  • Natural language processing
  • Text and sentiment analysis
  • Image recognition and classification
  • APIs and web scraping
  • Database access
  • Task automation

Prof. Dr. Johannes Schildgen is a professor of databases, specializing in big data, at Ostbayerische Technische Hochschule (OTH) Regensburg (the Regensburg University of Applied Sciences).

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