Natural Language Processing Live Lessons

Code and slides to accompany the online lecture series (https://bit.ly/NLP_LL) by Data For Science.

Natural Language Processing LiveLessons covers the fundamentals and some of the more advanced aspects of Natural Language Processing in a simple and intuitive way, empowering you to add NLP to your toolkit. Using the powerful NLTK package, it gradually moves from the basics of text representation, cleaning, topic detection, regular expressions, and sentiment analysis before moving on to the PyTorch deep learning framework to explore advanced topics such as text classification and sequence-to-sequence models. The transformer architectures underlying Large Language Models (LLMs) like ChatGPT, Claude and BERT are explored in depth along with some practical applications. After successfully completing these lessons you'll be equipped with a fundamental and practical understanding of the full breath of Natural Language Processing tools and algorithms.

Schedule

1. Text Representation

• One-hot Encoding
• Bag of Words
• Stopwords
• TF/IDF
• N-grams
• Word Embeddings

2. Text Cleaning

• Stemming
• Lemmatization
• Regular Expressions

3. Named Entity Recognition

• Part Of Speech Tagging
• Chunking
• Chinking
• Named Entity Recognition

4. Topic Modeling

• Explicit Semantic Analysis
• Document Clustering
• Latent Semantic Analysis
• Latent Dirichlet Allocation
• Non-Negative Matrix Factorization

5. Sentiment Analysis

• Quantifying Words and Feelings
• Negations and Modifiers
• Corpus Based Approaches

6. Text Classification

• Feed-Forward Networks
• Convolutional Neural Networks
• Applications

7. Sequence Modeling

• Recurrent Neural Networks (RNN)
• Gated Recurrent Unit (GRU)
• Long-Short Term Memory (LSTM)
• Auto-Encoder Models

8. Applications

• word2vec Embeddings
• GloVe
• Transfer Learning
• Language Detection

9. NLP With Large Language Models

• Large Language Models
• Transformers
• BERT
• HuggingFace
• NLP Tasks