Text Preprocessing to Prepare for Machine Learning in Python — Natural Language Processing

Text Preprocessing to Prepare for Machine Learning in Python — Natural Language Processing.

Overview

Author(s): Rashida Nasrin Sucky

Originally published on Towards AI.

Some Commonly Used Text Preprocessing Techniques in Python With Examples
Photo by Kiril Dobrev on Unsplash

In this age of social media and online business era, text data are coming from everywhere. However, dealing with text data is tricky. Because raw text may come in with all types of impurities, unnecessary noises, spelling mistakes, and more. So, it is necessary to go through proper preprocessing before diving into any modeling with text data.

In this article, we will work on some common text preprocessing techniques to prepare text data for machine learning.

Numbers in text can be deceiving for machine learning models. Because anyway, the text needs to be converted as numbers. Each… Read the full blog for free on Medium.

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Published via Towards AI

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AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.