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Learn Python by Building Data Science Applications

You're reading from   Learn Python by Building Data Science Applications A fun, project-based guide to learning Python 3 while building real-world apps

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789535365
Length 482 pages
Edition 1st Edition
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Authors (2):
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Philipp Kats Philipp Kats
Author Profile Icon Philipp Kats
Philipp Kats
David Katz David Katz
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David Katz
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Table of Contents (26) Chapters Close

Preface 1. Section 1: Getting Started with Python
2. Preparing the Workspace FREE CHAPTER 3. First Steps in Coding - Variables and Data Types 4. Functions 5. Data Structures 6. Loops and Other Compound Statements 7. First Script – Geocoding with Web APIs 8. Scraping Data from the Web with Beautiful Soup 4 9. Simulation with Classes and Inheritance 10. Shell, Git, Conda, and More – at Your Command 11. Section 2: Hands-On with Data
12. Python for Data Applications 13. Data Cleaning and Manipulation 14. Data Exploration and Visualization 15. Training a Machine Learning Model 16. Improving Your Model – Pipelines and Experiments 17. Section 3: Moving to Production
18. Packaging and Testing with Poetry and PyTest 19. Data Pipelines with Luigi 20. Let's Build a Dashboard 21. Serving Models with a RESTful API 22. Serverless API Using Chalice 23. Best Practices and Python Performance 24. Assessments 25. Other Books You May Enjoy

Trying SciPy and scikit-learn

The SciPy package essentially kicked off the entire era of scientific Python. Created in 2001 by researchers Travis Oliphant, Pearu Peterson, and Eric Jones, it was formed as a collection of basic and universal scientific techniques. Over time, the package grew and now offers generic tooling and popular techniques for scientific analysis. Its submodules cover linear algebra, integration, optimization, interpolation, statistics, and many more.

With the rise of machine learning, the corresponding submodule of SciPy grew more and more complex. At some point, it became so big, the decision was made to reintroduce it as a separate, independent package—scikit-learn. As the mark of its origins, the package kept its name, defined earlier as SciPy kit—learn. Due to its simple and unified interface and a large variety of models, scikit-learn quickly...

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