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Natural Language Processing Short Course

Learn natural language processing (NLP) to build systems that understand, interpret, and generate human language. NLP powers chatbots, translation services, sentiment analysis, and the large language models behind ChatGPT. This course covers tokenization, bag of words, TF-IDF, word embeddings, sentiment analysis, named entity recognition, and the transformer architecture.. Master natural language processing through bite-sized, swipeable learning tiles — designed for busy people who want to learn on the go.

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About This Natural Language Processing Course

This Natural Language Processing short course on SkillTiles breaks down everything you need to know into 10 structured milestones, each packed with bite-sized learning tiles you can swipe through in under 15 seconds each.

Whether you're a complete beginner or looking to refresh your knowledge, this online natural language processing course uses AI-powered content to adapt to your learning pace. Each tile delivers a focused concept, example, or quiz — making it perfect for commutes, coffee breaks, or quick study sessions.

Covering 70 key topics across 10 milestones, this course takes you from foundations to practical application. Earn badges as you complete each milestone and track your progress on your learning dashboard.

Course Curriculum

1

NLP Foundations

Tokenization, embeddings, and sentiment analysis

tokenization methodsbag of words vs TF-IDFword embeddings (Word2Vec)sentiment analysisnamed entity recognitiontext classificationtransformers overview
2

Text Preprocessing

Clean and prepare text data

tokenization methodsstemming vs lemmatizationstopword removaltext normalizationregular expressions for texthandling special charactersmultilingual preprocessing
3

Text Representation

Turn text into numbers

bag of wordsTF-IDF weightingword embeddings (Word2Vec)GloVe embeddingsFastTextsentence embeddingscontextual embeddings overview
4

Text Classification

Categorize documents and messages

sentiment analysisspam detectiontopic classificationmulti-label classificationNaive Bayes for textfine-tuning for classificationevaluation metrics for text
5

Information Extraction

Extract structured data from text

named entity recognition (NER)part-of-speech taggingrelationship extractionkeyword extractionsummarization basicsquestion answering basicsdependency parsing
6

Transformers for NLP

Modern NLP with transformer models

BERT architecture and usageGPT models overviewHugging Face Transformers libraryfine-tuning pretrained modelstokenizer selectionmodel selection (small vs large)inference optimization
7

Text Generation

Generate human-like text

language model basicsprompt engineeringtemperature and samplingbeam search decodingRAG (Retrieval-Augmented Generation)chatbot architecturecontent generation applications
8

Semantic Similarity

Measure meaning between texts

cosine similaritysemantic searchsentence transformersdocument similarityclustering text documentsduplicate detectionrecommendation from text
9

NLP Applications

Build real-world NLP systems

chatbot developmentspeech-to-text integrationtranslation systems overviewdocument processing pipelinessocial media analysisreview analysis systemsNLP in healthcare
10

Advanced NLP

Cutting-edge NLP techniques

multi-task learningzero-shot and few-shot NLPknowledge graphs and NLPmultilingual NLPNLP fairness and biasefficient transformersNLP research trends

What You'll Learn in This Natural Language Processing Course

tokenization methods
bag of words vs TF-IDF
word embeddings (Word2Vec)
sentiment analysis
named entity recognition
text classification
transformers overview
tokenization methods
stemming vs lemmatization
stopword removal
text normalization
regular expressions for text
handling special characters
multilingual preprocessing

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