Using GPT, AUTO-GPT, and Pandas for Natural Language Processing (3 days)
This practical 3-day course equips you with the skills to leverage GPT, AUTO-GPT, Langchain, and Pandas for natural language processing tasks. You'll learn to perform text classification, sentiment analysis, and named entity recognition through hands-on examples. Whether you're a data scientist, software engineer, or technology enthusiast, you'll gain the expertise to unlock valuable insights from textual data using these cutting-edge NLP tools.
Prerequisites
- A basic appreciation of AI technology
- A good grasp of the Python programming language
Contents
Foundations of Natural Language Processing (NLP)
- The core concepts, techniques and challenges in NLP
- Text preprocessing, feature extraction and text representation
Basics of language models
- The role of language models in NLP
- GPT and AUTO-GPT, their architecture and how to use them for NLP tasks
Data Pre-Processing techniques and classification
- How to clean and preprocess text data with Pandas and other libraries
- How to tokenize, stem, lemmatize and handle special characters
- Training and fine-tuning of language models for text classification tasks: sentiment analysis, topic classification and spam detection
- Text classification, labeled datasets, model training and model performance evaluation
Sentiment analysis
- How to leverage language models for sentiment analysis
- Methods to perform sentiment analysis using pre-trained models and training custom models
Named Entity Recognition (NER)
- What is NER
- How to extract named entities from text data using language models
- Fine-tune models for better NER performance
Foundations of NLP and Language Models
- Understand the basics of Natural Language Processing (NLP)
- Explore the fundamentals of language models
- Learn about GPT and AUTO-GPT architecture
- Discuss the applications of language models in NLP
Text Preprocessing and Feature Extraction
- Perform data preprocessing using Pandas and other relevant libraries
- Learn techniques for tokenization, stemming, and lemmatization
- Handle special characters and noise in text data
- Extract relevant features from text for NLP tasks
Text Classification and Sentiment Analysis
- Dive into text classification using language models
- Train and fine-tune models for sentiment analysis
- Perform sentiment analysis on textual data
- Evaluate and interpret the results of sentiment analysis
Named Entity Recognition and Text Generation
- Understand named entity recognition (NER) and its importance
- Fine-tune models for named entity recognition tasks
- Extract named entities from text data
- Explore text generation techniques using language models
Real-world NLP Applications and Ethical Considerations
- Apply NLP techniques to real-world datasets
- Develop end-to-end NLP workflows using GPT, Langchain, and Pandas
- Discuss ethical considerations in NLP, including bias and privacy concerns
- Learn about responsible data usage and best practices in NLP projects
