Learn the essentials of text processing in Natural Language Processing (NLP). Master techniques such as tokenization, stop word and punctuation removal, and text normalization with lowercasing, stemming, and lemmatization to prepare text data for further analysis and insight extraction.

Introduction to natural language processing

Sentence and word tokenization

NLP workflow

Stop words and punctuation handling

Removing stop words

Removing punctuation

Text normalization techniques

Lowercasing

Stemming

Lemmatization

Text Processing Fundamentals

Transform raw text into powerful numerical features. Create Bag-of-Words and TF-IDF representations to capture word importance across documents, then explore word embeddings like Word2Vec and GloVe to uncover deep semantic patterns. Visualize frequency, relevance, and similarity to bring your text data to life.

Bag-of-Words representation

Building vocabulary from customer reviews

Transforming text to numbers with BoW

Frequency analysis of product reviews

Visualizing word frequencies

TF-IDF vectorization

TF-IDF representation of product feedback

Comparing BoW and TF-IDF representations

Embeddings

Exploring word relationships with embeddings

Visualizing and comparing word embeddings

Feature Extraction from Text

Harness the power of pre-trained models to perform advanced text classification tasks. Use Hugging Face pipelines for sentiment analysis, topic classification, and natural language inference. Evaluate semantic similarity and grammatical correctness with state-of-the-art models, all without building anything from scratch.

Hugging Face pipelines for sentiment analysis

Analyzing the sentiment of a review

Batch classifying multiple reviews

Comparing models on labeled review data

Zero-shot classification and QNLI

Zero-shot classification of support tickets

Does the text answer the question?

Question similarity and grammatical correctness

Detecting duplicate questions

Checking grammatical correctness

Text Classification with Hugging Face

Dive into the core of modern NLP applications with token classification and text generation techniques. Learn to extract meaningful entities and grammatical structures using NER and PoS tagging. Master both extractive and abstractive question answering, and explore advanced generation tasks including summarization, translation, and language modeling using Hugging Face pipelines.

Token classification

Identifying named entities in news headlines

Part of Speech tagging for text analysis

Question answering

Answering questions from product descriptions

Generating natural answers with abstractive QA

Sequence generation tasks

Summarizing news articles for quick insights

Translating customer reviews to French

Building a search completion system

Congratulations

Token Classification and Text Generation

product_reviews.csv

Unlock the power of Natural Language Processing (NLP) and take your text analysis skills to the next level! This course equips you with essential tools to process, analyze, and extract insights from text data. Start with the fundamentals of text processing, such as tokenization, lemmatization, and stemming, to extracting meaningful numerical features from text using bag-of-words (BoW), TF-IDF, and embeddings. Finally, harness the power of state-of-the-art transformer models using Hugging Face. Perform sentiment analysis, classify content, analyze question-answer relationships, assess grammatical acceptability, and generate text using the latest models.

<h2>Build a Strong NLP Foundation </h2>Unlock the power of Natural Language Processing (NLP) and take your text analysis skills to the next level! This course equips you with essential tools to process, analyze, and extract insights from text data. Start with the fundamentals of text processing, from tokenization to cleaning and normalizing text by removing stop words, punctuation, and applying lemmatization and stemming to improve text consistency.<br><br><h2>Extract Meaningful Features from Text</h2>Go beyond raw text and transform it into numerical representations! Explore the Bag-of-Words representation, dive into TF-IDF vectorization, and leverage powerful word embeddings like Word2Vec and GloVe to capture semantic relationships between words.<br><br><h2>Classify and Generate Text with AI</h2>Harness the power of state-of-the-art transformer models using Hugging Face pipelines. Learn how to perform sentiment analysis, classify content, analyze question-answer relationships, assess grammatical acceptability, and generate text using various models. Explore Named Entity Recognition (NER), Part-of-Speech (PoS) tagging, text summarization, and translation to expand your NLP toolkit.<br><br><h2>Master key NLP libraries</h2>By the end of this course, you’ll have a strong grasp of NLP fundamentals and hands-on experience with key libraries such as nltk, sklearn, gensim, and Hugging Face’s transformers. Start your journey today and transform the way you interact with text data!

Python Toolbox

Master text analysis with essential NLP techniques from preprocessing to advanced transformer models. 

Natural Language Processing (NLP) in Python

Wetenschapper op het gebied van machine learning in Python

Natuurlijke taalverwerking in Python

Machine Learning Engineer

Question similarity

QQP pipeline

Assessing grammatical correctness

CoLA pipeline

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_41242/subtitles/course_41242_4a3531e4ba5bcfe11ee135ca20231603.vtt

Text Classification with Hugging Face - Question similarity and grammatical correctness

Question similarity and grammatical correctness

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