Foundational Courses
Natural Language Processing (NLP) Specialist
To equip learners with foundational to advanced NLP skills
5
44 enrolled students
Objective
To equip learners with foundational to advanced NLP skills, from text preprocessing to implementing and fine-tuning transformer-based models like BERT and GPT.
Basic To Advance
You will progress through this course from basics to advanced level.
Duration
3 Months
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Modules
Module 1: Data Preprocessing and Feature Engineering for Text
Objective:
Introduce learners to text preprocessing techniques essential for NLP tasks. Cover text tokenization, stop words, stemming, lemmatization, and vectorization methods.
Topics:
- Text tokenization, stop words, stemming, lemmatization
- Vectorization techniques (TF-IDF, Word2Vec, GloVe)
- Practical: Preprocess text data for a sentiment analysis task
Hands-on Exercise:
Preprocess a Text Dataset for Sentiment Analysis using a dataset like the IMDb movie reviews or a custom text dataset, perform text preprocessing, including tokenization, removing stop words, stemming, and lemmatization.
Then, apply TF-IDF and Word2Vec to vectorize the text data. Finally, split the data into training and testing sets, and prepare it for a sentiment analysis model.
Module 2: NLTK/spacy/gensim
Objective:
Familiarize learners with popular NLP libraries such as NLTK, spaCy, and Gensim. These libraries are essential for preprocessing, feature extraction, and working with word embeddings.
Topics Covered:
- Basic usage of NLTK, spaCy, and Gensim
- Text processing with NLTK: tokenization, POS tagging, and parsing
- Word embeddings using Gensim (Word2Vec, GloVe)
Hands-on Exercise:
Text Analysis and Word Embeddings with spaCy and Gensim
- Use spaCy to perform tokenization, part-of-speech tagging, and named entity recognition (NER) on a text corpus.
- Use Gensim to train Word2Vec on a corpus and explore the relationship between words (e.g., “king” “man” + “woman” = “queen”).
- Visualize word embeddings using t-SNE or PCA to observe how similar words cluster in the vector space
Module 3:Introduction to Sequence Models (RNN, LSTM, GRU)
Objective:
Provide foundational knowledge of sequence models, focusing on RNN, LSTM, and GRU models, and explain their advantages in handling sequential data.
Topics Covered:
- Basics of sequence modeling and RNN limitations
- Deep dive into LSTMs and GRUs
- Practical: Build an LSTM for text generation or time-series prediction
Hands-on Exercise:
Build and Train an LSTM Model for Text Generation. Use an LSTM model to generate text, based on a given text corpus (e.g., a collection of Shakespeare’s writings or another literary dataset). Preprocess the text, convert it into sequences of tokens, and train the LSTM to predict the next word in the sequence. Test the model by generating new sentences or paragraphs. Alternatively, you can use LSTMs for a simple time-series prediction problem, like predicting stock prices or sales data.
Module 4: Transformers and Self-Attention
Objective:
Introduce the transformer architecture and the self-attention mechanism, including positional encoding and multi-head attention, which are core concepts in advanced NLP.
Topics:
- Self-attention and multi-head attention
- Positional encoding in transformers
- Practical: Implement a transformer model for text classification
Hands-on Exercise:
Build and Train a Simple Transformer for Text Classification
Implement a basic transformer model from scratch (or using a pre-built framework like Hugging Face’s transformers) for a text classification task, such as classifying movie reviews as positive or negative. Use a dataset like IMDb and train the model to classify the sentiment of the reviews. Visualize attention weights to understand how the transformer attends to different words in the text.
Module 5: BERT, GPT, and Transformer-based NLP Models
Objective:
Cover popular transformer-based models (BERT, GPT) and guide learners in fine-tuning these models for custom NLP tasks.
Topics:
- Overview of BERT, GPT, and transformer-based architectures
- Fine-tuning for NLP tasks
- Practical: Fine-tune BERT for sentiment analysis or named entity recognition
Hands-on Exercise:
Fine-Tune BERT for Sentiment Analysis
Use the Hugging Face transformers library to fine-tune a pre-trained BERT model for a sentiment analysis task. Start with a dataset like IMDb or Amazon reviews, load the pre-trained BERT model, and fine-tune it using transfer learning. Evaluate the model’s performance, and experiment with hyperparameter tuning to improve results.
Alternatively, fine-tune BERT for Named Entity Recognition (NER) using a dataset like the CoNLL-03 NER dataset, and then apply it to a custom domain (e.g., identifying medical terms or product names).
Module 6: Capstone Project
Objective:
Apply all concepts learned in a real-world project by building a question-answering or text classification model tailored to a specific domain (e.g., customer service, healthcare).
Project:
Build a specialized model (e.g., question-answering or text classification) using BERT, focusing on a unique domain.
Hands-on Exercise:
Capstone Project – Build a Domain-Specific NLP Model
- Choose a domain (e.g., customer service, healthcare, legal) and collect or find a relevant dataset (e.g., customer support tickets, medical texts, legal documents).
- Based on the domain, either build a text classification model or a question-answering model using BERT or another transformer-based architecture.
- For a text classification model, fine-tune the model on your dataset to predict labels (e.g., customer satisfaction, medical diagnoses).
- For a question-answering model, fine-tune BERT or GPT on a question-answering dataset (e.g., SQuAD, WikiQA) and customize it to answer domain-specific queries.
- Evaluate your model using standard metrics (e.g., F1 score, accuracy) and document your findings in a detailed report
Frequently Asked Questions
1. What is the Natural Language Processing (NLP) Specialist course?
This course covers the principles and applications of NLP, a field of AI that focuses on the interaction between computers and human language. You will learn to build systems that understand, interpret, and generate human language in text and spoken forms.
2. What are the prerequisites?
A solid foundation in Python programming and some familiarity with basic machine learning concepts is recommended. If you’re new to AI, our course includes introductory modules to help you get up to speed.
3. What types of projects are included?
You’ll work on hands-on projects such as building a sentiment analysis tool, creating text summarizers, developing a language translation model, and training chatbots. These projects will reinforce your understanding and prepare you for real-world NLP challenges.
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