Python Natural Language Processing Cookbook

Packt via Coursera

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Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

10 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course offers a hands-on approach to mastering Natural Language Processing (NLP) in Python, focusing on real-world applications and advanced techniques. You'll learn how to extract insights from text, build NLP pipelines, and leverage modern transformer models like GPT-4 for complex tasks. Through step-by-step recipes, you will gain practical skills in text preprocessing, grammar analysis, semantic representation, classification, topic modeling, and visualization. The course also guides you through implementing generative AI solutions and applying natural language understanding for diverse domains. These exercises help translate theory into actionable outcomes for data-driven projects. What sets this course apart is its combination of foundational NLP knowledge with cutting-edge developments in large language models and explainable AI (XAI). Each module pairs theory with hands-on coding exercises using Python and open-source libraries, ensuring learners can immediately apply concepts in practical settings. This course is designed for Python programmers, data scientists, and machine learning engineers who want to strengthen NLP expertise. No prior NLP experience is required, but familiarity with Python programming is recommended.

Syllabus

Learning NLP Basics

This module introduces essential text preprocessing techniques for natural language processing using NLTK and spaCy. Learners will practice tokenization, lemmatization, and stopword removal, and compare the capabilities of different NLP libraries. The module also covers handling multilingual text and preparing data for further analysis.

Playing with Grammar

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.

Representing Text – Capturing Semantics

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.

Classifying Texts

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.

Getting Started with Information Extraction

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.

Topic Modeling

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.

Visualizing Text Data

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.

Transformers and Their Applications

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.

Natural Language Understanding

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.

Generative AI and Large Language Models

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.