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Python Real-World Projects

Python Real-World Projects

By : Steven F. Lott
4.4 (5)
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Python Real-World Projects

Python Real-World Projects

4.4 (5)
By: Steven F. Lott

Overview of this book

In today's competitive job market, a project portfolio often outshines a traditional resume. Python Real-World Projects empowers you to get to grips with crucial Python concepts while building complete modules and applications. With two dozen meticulously designed projects to explore, this book will help you showcase your Python mastery and refine your skills. Tailored for beginners with a foundational understanding of class definitions, module creation, and Python's inherent data structures, this book is your gateway to programming excellence. You’ll learn how to harness the potential of the standard library and key external projects like JupyterLab, Pydantic, pytest, and requests. You’ll also gain experience with enterprise-oriented methodologies, including unit and acceptance testing, and an agile development approach. Additionally, you’ll dive into the software development lifecycle, starting with a minimum viable product and seamlessly expanding it to add innovative features. By the end of this book, you’ll be armed with a myriad of practical Python projects and all set to accelerate your career as a Python programmer.
Table of Contents (20 chapters)
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19
Index

17.4 Next steps toward machine learning

We can draw a rough boundary between statistical modeling and machine learning. This is a hot topic of debate because — viewed from a suitable distance — all statistical modeling can be described as machine learning.

In this book, we’ve drawn a boundary to distinguish methods based on algorithms that are finite, definite, and effective. For example, the process of using the linear least squares technique to find a function that matches data is generally reproducible with an exact closed-form answer that doesn’t require tuning hyperparameters.

Even within our narrow domain of “statistical modeling,” we can encounter data sets for which linear least squares don’t behave well. One notable assumption of the least squares estimates, for example, is that the independent variables are all known exactly. If the x values are subject to observational error, a more sophisticated approach is required.

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