Unit 1: Foundations of Natural Language Processing (NLP)

• 1.1 The core concepts, techniques and challenges in NLP
• 1.2 Text preprocessing, feature extraction and text representation

Unit 2: Basics of language models

• 2.1 The role of language models in NLP
• 2.2 GPT and AUTO-GPT, their architecure and how to use them for NLP tasks

Unit 3: Data Pre-Processing techniques and classification

• 3.1 How to clean and preprocess test data with Pandas and other libraries
• 3.2 How to tokenize, stem, lemmatize and handling of special characters
• 3.3 Training and fine tuning of language models for text classification tasks: sentiment analysis, topic classification and spam detection
• 3.4 Text classification, labeled datasets, model train and model performance evaluation

Unit 4: Sentiment analysis

• 4.1 How to leverage language models for sentiment analysis
• 4.2 Methods to perform sentiment analysis using pre-trained models and training custom models

Unit 5: Named Entity Recognition (NER)

• 5.1 What is NER
• 5.2 Hot to extract named entities from text data using language models
• 5.3 Fine-tune models for better NER performance

More: 

Unit 1: Foundations of NLP and Language Models

• 1.1 Understand the basics of Natural Language Processing (NLP)
• 1.2 Explore the fundamentals of language models
• 1.3 Learn about GPT and AUTO-GPT architecture
• 1.4 Discuss the applications of language models in NLP

Unit 2: Text Preprocessing and Feature Extraction

• 2.1 Perform data preprocessing using Pandas and other relevant libraries
• 2.2 Learn techniques for tokenization, stemming, and lemmatization
• 2.3 Handle special characters and noise in text data
• 2.4 Extract relevant features from text for NLP tasks
• Unit 3: Text Classification and Sentiment Analysis
• 3.1 Dive into text classification using language models
• 3.2 Train and fine-tune models for sentiment analysis
• 3.3 Perform sentiment analysis on textual data
• 3.4 Evaluate and interpret the results of sentiment analysis

Unit 4: Named Entity Recognition and Text Generation

• 4.1 Understand named entity recognition (NER) and its importance
• 4.2 Fine-tune models for named entity recognition tasks
• 4.3 Extract named entities from text data
• 4.4 Explore text generation techniques using language models

Unit 5: Real-world NLP Applications and Ethical Considerations

• 5.1 Apply NLP techniques to real-world datasets
• 5.2 Develop end-to-end NLP workflows using GPT, Langchain, and Pandas
• 5.3 Discuss ethical considerations in NLP, including bias and privacy concerns
• 5.4 Learn about responsible data usage and best practices in NLP projects