Leveraging AUTO-GPT, GPT, and Pandas for Unleashing Natural Language Processing Brilliance
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Understand core NLP concepts and techniques
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Preprocess and extract features from textual data using Pandas
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Utilize GPT and AUTO-GPT models for various NLP tasks
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Implement sentiment analysis and text classification models
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Perform Named Entity Recognition and fine-tune models for improved performance
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Apply ethical considerations in the development and deployment of NLP solutions.
Overview
Off the shelf (OTS)
This course is tailored for data scientists, machine learning engineers, and software developers seeking to deepen their expertise in Natural Language Processing (NLP) through practical applications of advanced tools like GPT, AUTO-GPT, Langchain, and Pandas. Participants will engage in hands-on exercises focusing on tasks such as text classification, sentiment analysis, and named entity recognition, equipping them with the skills to implement and fine-tune NLP models effectively.
A foundational understanding of Python programming and basic NLP concepts is recommended. Familiarity with data analysis techniques and libraries such as Pandas will be beneficial but is not mandatory.
The Auto-GPT & Pandas for NLP Training Course offers an in-depth exploration of modern NLP techniques and tools. Participants will start with the fundamentals of NLP, progressing to advanced topics like GPT and AUTO-GPT architectures. The curriculum includes practical sessions on data preprocessing using Pandas, feature extraction, and the application of language models for tasks such as sentiment analysis, topic classification, and named entity recognition. The course also covers ethical considerations and best practices in deploying NLP solutions.
Key Topics Covered:
• Foundations of Natural Language Processing (NLP)
• Text preprocessing and feature extraction with Pandas
• Architecture and applications of GPT and AUTO-GPT models
• Sentiment analysis and text classification techniques
• Named Entity Recognition (NER) and fine-tuning models
• Ethical considerations and best practices in NLP
The course is delivered over three days through a combination of lectures and hands-on labs, providing participants with practical experience in applying NLP techniques to real-world datasets.
Delivery method
Virtual
Course duration
21 hours
Competency level
Working

Delivery method
-
Virtual
Course duration
21 hours
Competency level
-
Working
