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  • Book Overview & Buying Applied Computational Thinking with Python
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Applied Computational Thinking with Python

Applied Computational Thinking with Python - Second Edition

By : Sofía De Jesús, Martinez
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Applied Computational Thinking with Python

Applied Computational Thinking with Python

By: Sofía De Jesús, Martinez

Overview of this book

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence. This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions. By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.
Table of Contents (25 chapters)
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1
Part 1: An Introduction to Computational Thinking
10
Part 2: Applying Python and Computational Thinking
16
Part 3: Data Processing, Analysis, and Applications Using Computational Thinking and Python

Problem 5 – using Python for language detection

In this section, we’ll dive into the Multinomial Naive Bayes classifier, a handy tool often used in NLP in machine learning, especially for tasks such as language detection.

Language detectors, such as the ones found in tools such as Google Translate, play a crucial role in bridging language gaps and enabling global communication. These systems rely on Naive Bayes classifiers, which are known for their efficiency in handling large text datasets and their accuracy in dealing with the complexities of languages. Their simplicity and speed make them a top choice for real-time language detection and translation:

Figure 18.11: Google Translate

Figure 18.11: Google Translate

The simplicity and speed of Naive Bayes algorithms make them ideal for real-time language detection and translation in applications such as Google Translate.

This classifier is based on Bayes’ theorem and operates under the assumption that features...

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Applied Computational Thinking with Python
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