This module introduces key natural language processing techniques for analyzing grammatical structure in text. Learners will explore how to use dependency parsing and noun chunk extraction to identify subjects, objects, and phrases, enhancing their ability to extract meaningful data from language.

This module introduces a range of techniques for representing text in natural language processing, from basic bag-of-words and n-gram models to advanced embeddings like TF-IDF, word2vec, BERT, and OpenAI models. Learners will gain hands-on experience with vectorization methods and explore how semantic meaning is captured for downstream NLP tasks, including retrieval-augmented generation.

This module introduces a variety of text classification techniques, ranging from rule-based keyword methods to advanced machine learning and deep learning models. Learners will gain hands-on experience with tools such as K-Means clustering, SVMs, spaCy, and OpenAI models to classify texts by topic or sentiment. By the end, you'll be able to compare and implement multiple approaches for real-world NLP tasks.

This module introduces practical techniques for extracting valuable information from text, including handling misspellings with Levenshtein distance, extracting keywords, and performing named entity recognition (NER) using spaCy and BERT. Learners will gain hands-on experience in processing and analyzing textual data for various real-world applications.

This module introduces a range of advanced topic modeling techniques, including LDA, SBERT, BERTopic, and contextualized topic models, to help you uncover hidden themes in text data. You will learn how to apply these models for clustering, classification, and visualization of textual information. Practical recipes and real-world examples will guide you in leveraging embeddings and community detection for insightful text analysis.

This module introduces key techniques for visualizing various aspects of text data, including parts of speech, topic prevalence, and model performance. Learners will gain hands-on experience with tools such as word clouds and confusion matrices to better interpret and communicate NLP results.

This module introduces the fundamentals of transformer models in natural language processing, guiding learners through data preparation, tokenization, and the application of pre-trained models for tasks such as classification, zero-shot learning, and text generation. By working with real datasets and hands-on recipes, learners will gain practical experience in leveraging transformers for diverse NLP applications.

This module introduces key techniques in natural language understanding, including question answering, text summarization, and sentence entailment using Transformer-based models. Learners will also explore methods to enhance the explainability of NLP classifiers, gaining practical skills to interpret and evaluate model outputs.

This module introduces learners to practical techniques for working with large language models (LLMs), including running models locally, enhancing them with external data, and building interactive chatbots. Learners will gain hands-on experience with instruction prompting, data augmentation, and code generation using transformer-based models.