Using GPT, AUTO-GPT, and Pandas for Natural Language Processing
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Layout
This training course combines lectures with practical exercises that help the delegates to put what they have learned on the training course into practice. The exercises specifically build on what has been recently taught and are built up as the training course progresses.
Who it is for
This course is for those who need to learn more about these aspects of AI
Training Course Prerequisites
- A basic appreciation of AI technology
- A good grasp of the Python programming language
Chapters
Chapter 1 Foundations of Natural Language Processing (NLP)
- The core concepts, techniques and challenges in NLP
- Text preprocessing, feature extraction and text representation
Chapter 2 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
Chapter 3 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
Chapter 4 Sentiment analysis
- How to leverage language models for sentiment analysis
- Methods to perform sentiment analysis using pre-trained models and training custom models
Chapter 5 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
Chapter 6 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
Chapter 7 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
Chapter 8 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
Chapter 9 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
Chapter 10 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