Natural Language Processing
The course teaches core NLP concepts—from preprocessing and tokenization to embeddings, classification, sequence labeling, and text generation—focusing on intuitive algorithmic understanding and when to apply each technique.
Who Should Take This
Data scientists, software engineers, and research analysts who have basic programming experience and want to deepen their grasp of language-driven solutions find this course valuable. It equips them to design, evaluate, and deploy NLP pipelines, bridging theory and practice for real‑world applications.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
64 learning goals
1
Text Preprocessing and Tokenization
5 topics
Describe text preprocessing steps including tokenization, lowercasing, stopword removal, stemming, and lemmatization and explain when each step is appropriate
Describe subword tokenization algorithms including Byte-Pair Encoding, WordPiece, and SentencePiece and explain how they handle out-of-vocabulary words and morphological variation
Apply text normalization and cleaning techniques including Unicode handling, regex-based extraction, HTML stripping, and language detection for multilingual text corpora
Analyze the impact of tokenization strategy choice on downstream model performance including vocabulary size, sequence length, and representation of rare or domain-specific terms
Apply vocabulary building strategies including frequency thresholds, special tokens, padding, and truncation for preparing text data for neural network consumption
2
Word Representations and Embeddings
7 topics
Describe bag-of-words and TF-IDF representations including term frequency, inverse document frequency, and the sparsity limitations of count-based text representations
Describe Word2Vec including skip-gram and CBOW architectures, negative sampling, and how distributed representations capture semantic relationships through vector arithmetic
Describe GloVe and FastText embeddings including co-occurrence matrix factorization, subword information, and how these methods complement Word2Vec for different use cases
Describe contextual embeddings from ELMo, BERT, and GPT including how token representations vary by surrounding context unlike static word embeddings
Apply word embedding visualization techniques including t-SNE and UMAP projections to explore semantic clusters, analogies, and potential biases in learned representations
Analyze the progression from sparse count-based to dense pretrained contextual embeddings and evaluate when static versus contextual representations are sufficient for a given task
Apply embedding evaluation including intrinsic evaluation via analogy tests and similarity benchmarks and extrinsic evaluation through downstream task performance comparison
3
Text Classification
6 topics
Describe text classification tasks including sentiment analysis, spam detection, topic categorization, and intent recognition and explain the standard pipeline from text to prediction
Apply traditional machine learning classifiers including naive Bayes, logistic regression, and SVM with TF-IDF features for document classification tasks
Apply deep learning classifiers including CNN-based text classification, LSTM-based classifiers, and fine-tuned transformer models for sentiment and topic classification
Apply multi-label and multi-class classification strategies including one-vs-rest, threshold tuning, and hierarchical classification for complex taxonomy assignments
Analyze text classification evaluation including precision, recall, F1-score, macro versus micro averaging, confusion matrices, and appropriate metrics for imbalanced text datasets
Apply aspect-based sentiment analysis including aspect extraction, opinion target identification, and polarity classification for fine-grained opinion mining from reviews and feedback
4
Sequence Labeling and Information Extraction
6 topics
Describe named entity recognition including entity types, BIO and BIOES tagging schemes, and the distinction between flat and nested entity recognition tasks
Describe part-of-speech tagging and syntactic parsing including dependency parsing, constituency parsing, and their role in understanding grammatical structure
Apply sequence labeling models including BiLSTM-CRF architectures and transformer-based token classifiers for named entity recognition and slot filling tasks
Apply relation extraction and information extraction pipelines to identify relationships between entities including distant supervision and joint entity-relation models
Analyze the trade-offs between pipeline and joint approaches for information extraction evaluating error propagation, training complexity, and performance on overlapping entities
Apply event extraction and temporal reasoning including event detection, temporal relation classification, and timeline construction from unstructured text documents
5
Sequence-to-Sequence and Text Generation
6 topics
Describe sequence-to-sequence models including encoder-decoder architecture, teacher forcing, and beam search decoding for machine translation and summarization
Describe the attention mechanism in seq2seq models including Bahdanau and Luong attention and explain how attention weights improve handling of long input sequences
Apply text generation strategies including greedy decoding, beam search, top-k sampling, nucleus sampling, and temperature scaling to control output diversity and quality
Apply abstractive and extractive summarization techniques including pointer-generator networks, sentence scoring, and fine-tuned transformer models for document summarization
Analyze text generation evaluation metrics including BLEU, ROUGE, METEOR, BERTScore, and human evaluation and explain the limitations of automated metrics for open-ended generation
Apply controllable text generation techniques including attribute conditioning, constrained decoding, and style transfer to produce text with specific properties like formality or sentiment
6
Language Models
7 topics
Describe statistical language models including n-gram models, perplexity as an evaluation metric, and smoothing techniques for unseen word combinations
Describe neural language models including LSTM-based and transformer-based architectures and explain how autoregressive and masked language modeling objectives differ
Describe BERT architecture including bidirectional masked language modeling, next sentence prediction, and how fine-tuning adapts pretrained representations to downstream tasks
Describe GPT-family architectures including autoregressive pretraining, scaling laws, in-context learning, and the emergent capabilities observed in large language models
Apply the distinction between encoder-only models like BERT, decoder-only models like GPT, and encoder-decoder models like T5 to select appropriate architectures for specific NLP tasks
Analyze the trade-offs of scaling language models including compute requirements, data needs, diminishing returns, and the relationship between model size and task performance
Apply instruction tuning concepts including how instruction-formatted training data transforms base language models into helpful assistants that follow diverse natural language instructions
7
Semantic Similarity and Retrieval
5 topics
Describe semantic similarity and textual entailment tasks including paraphrase detection, natural language inference, and sentence similarity scoring
Apply sentence embedding methods including mean pooling, CLS token extraction, and Sentence-BERT to compute dense vector representations for semantic search and retrieval
Apply cross-encoder and bi-encoder architectures for ranking and retrieval tasks including the trade-offs between accuracy and computational efficiency at scale
Analyze semantic similarity evaluation including cosine similarity, Spearman correlation on benchmark datasets, and the limitations of embedding-based similarity for nuanced language
Apply dense passage retrieval including bi-encoder training with hard negatives, efficient ANN indexing, and how dense retrieval complements sparse keyword matching for document search
8
Question Answering
5 topics
Describe question answering task formulations including extractive QA, abstractive QA, open-domain QA, and multi-hop reasoning over multiple evidence passages
Apply extractive question answering using span prediction models including how BERT-based models identify answer start and end positions within a context passage
Apply retrieval-augmented question answering including the retrieve-then-read pipeline, dense passage retrieval, and how external knowledge improves factual accuracy
Analyze the challenges of open-domain QA including knowledge conflicts, hallucination in generated answers, temporal knowledge drift, and evaluation of factual correctness
Apply conversational question answering including dialogue context management, coreference resolution across turns, and how multi-turn QA differs from single-turn extraction tasks
9
Multilingual and Cross-Lingual NLP
5 topics
Describe multilingual NLP challenges including script diversity, morphological complexity, low-resource languages, and the typological variation across language families
Describe multilingual pretrained models including mBERT, XLM-RoBERTa, and how shared vocabulary and joint pretraining enable zero-shot cross-lingual transfer
Apply machine translation concepts including parallel corpora, backtranslation for data augmentation, and how transformer-based models achieve state-of-the-art translation quality
Analyze cross-lingual transfer effectiveness including which linguistic features transfer across languages and why performance varies significantly between high- and low-resource languages
Apply low-resource NLP techniques including data augmentation, active learning, annotation projection, and few-shot learning for building NLP systems in languages with limited training data
10
Ethics and Bias in NLP
5 topics
Describe sources of bias in NLP systems including training data bias, annotation bias, representation bias, and how societal biases propagate through word embeddings and language models
Apply bias detection techniques including embedding association tests, counterfactual evaluation, and disaggregated performance analysis across demographic groups
Apply bias mitigation strategies including data balancing, debiasing embeddings, constrained decoding, and post-hoc calibration to reduce harmful outputs from NLP models
Analyze the tension between model capability and safety in NLP including toxicity filtering, content moderation challenges, and the limitations of current debiasing approaches
Analyze the challenges of evaluating and certifying NLP systems for fairness including intersectional bias, evolving social norms, and the limitations of benchmark-based fairness assessment
11
NLP Applications
7 topics
Apply dialogue systems concepts including task-oriented dialogue, open-domain chatbots, dialogue state tracking, and response generation strategies
Apply text-to-speech and speech-to-text concepts including ASR pipelines, acoustic models, language models, and the integration of speech and NLP in voice interfaces
Analyze real-world NLP system design including latency constraints, error cascading in pipeline architectures, and the trade-offs between modular and end-to-end approaches
Apply knowledge graph construction from text including entity extraction, relation detection, and link prediction for building structured knowledge bases from unstructured document collections
Apply document understanding tasks including layout analysis, table extraction, and form parsing and explain how multimodal models combine textual and visual features for document AI
Describe low-latency NLP deployment including model distillation, vocabulary pruning, and ONNX optimization for running NLP models in resource-constrained and real-time environments
Analyze the evolution from task-specific NLP models to general-purpose language models and evaluate the implications for NLP application development and engineering practices
Hands-On Labs
15 labs
~425 min total
Console Simulator
Code Sandbox
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
Scope
Included Topics
- Text preprocessing and tokenization (BPE, WordPiece, SentencePiece), word embeddings (Word2Vec, GloVe, FastText, contextual embeddings), text classification (sentiment, topic, intent), sequence labeling (NER, POS tagging), seq2seq models and text generation, language models (BERT, GPT, T5), semantic similarity and retrieval, question answering, multilingual NLP, bias and ethics in language technology
Not Covered
- Speech signal processing and audio feature extraction
- Specific NLP library APIs (spaCy, Hugging Face Transformers implementation details)
- Domain-specific NLP applications (legal, medical, financial) beyond illustrative examples
- Formal linguistics and generative grammar theory
- LLM application development patterns (covered in separate domain)
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