NLP Technician Course

Lesson 1 • What NLP Is and Why It Matters

Defines NLP, its relationship to linguistics and machine learning, and real-world impact. Establishes the conceptual baseline for all subsequent technical content.

Lesson 2 • Core Linguistic Concepts for NLP

Covers phonology, morphology, syntax, semantics, and pragmatics as they apply to NLP pipelines. Provides the linguistic vocabulary technicians need to interpret model behavior.

Lesson 3 • Tools and Ecosystem Overview

Surveys dominant NLP libraries, frameworks, and cloud APIs used in production. Students gain orientation to the tooling landscape before hands-on work begins.

Lesson 4 • The NLP Pipeline Overview

Introduces the standard sequence of NLP processing stages from raw text to structured output. Students map each stage to a concrete task and understand data flow.

Lesson 1 • Tokenization Strategies

Explains word, sentence, and subword tokenization methods and their trade-offs. Tokenization choice directly affects vocabulary size and model performance.

Lesson 2 • Text Acquisition and Encoding

Covers sourcing text from files, APIs, and databases, plus handling character encodings. Ensures data enters the pipeline without corruption or loss.

Lesson 3 • Stop Word and Vocabulary Management

Addresses stop word removal, vocabulary pruning, and out-of-vocabulary handling. Balances information retention against computational efficiency.

Lesson 4 • Cleaning and Noise Removal

Teaches removal of HTML tags, special characters, boilerplate, and irrelevant markup. Directly improves downstream model accuracy by reducing noise.

Lesson 1 • Sentence and Document Embeddings

Extends word-level representations to full sentences and documents using averaging and dedicated models. Enables semantic similarity and retrieval tasks.

Lesson 2 • Feature Selection and Dimensionality Reduction

Applies chi-square, mutual information, and PCA to reduce feature space without losing signal. Improves model training speed and generalization.

Lesson 3 • Bag-of-Words and Count Vectors

Builds document-term matrices using raw counts and binary indicators. Establishes the simplest baseline representation before introducing richer methods.

Lesson 4 • TF-IDF Weighting

Applies term frequency-inverse document frequency to weight informative terms. Students compute TF-IDF manually and with libraries, then interpret results.

Lesson 1 • Named Entity Recognition

Labels entity spans using rule-based, CRF, and neural sequence taggers. Students annotate data, train models, and evaluate span-level performance.

Lesson 2 • Relation and Event Extraction

Identifies relationships between entities and extracts structured event records from text. Builds on NER output to produce knowledge-graph-ready data.

Lesson 3 • Sentiment Analysis

Applies classification to polarity detection and aspect-based sentiment. Students handle negation, sarcasm indicators, and domain shift.

Lesson 4 • Text Classification Fundamentals

Trains Naive Bayes, logistic regression, and SVM classifiers on text features. Connects representation choices from Chapter 3 to classification outcomes.

Lesson 1 • Sequence-to-Sequence Models

Covers encoder-decoder architecture for tasks like translation and summarization. Students trace data flow through encoder, context vector, and decoder.

Lesson 2 • Recurrent Neural Networks for Text

Introduces RNN architecture, forward pass mechanics, and vanishing gradient issues. Students implement a simple RNN text classifier from scratch.

Lesson 3 • Attention Mechanisms

Introduces additive and dot-product attention as solutions to the context bottleneck. Prepares students for transformer architecture in the next chapter.

Lesson 4 • N-gram Language Models

Builds probabilistic models of word sequences using n-gram counts and smoothing. Provides the statistical foundation before neural sequence models are introduced.

Lesson 1 • Parameter-Efficient Fine-Tuning

Introduces LoRA, prefix tuning, and adapter methods that reduce compute and memory costs. Students apply a PEFT method to a pretrained model.

Lesson 2 • Fine-Tuning Pretrained Models

Covers task-specific head attachment, learning rate scheduling, and overfitting prevention during fine-tuning. Students fine-tune a pretrained model on a classification dataset.

Lesson 3 • Transformer Architecture Deep Dive

Dissects self-attention, positional encoding, feed-forward layers, and layer normalization. Students trace a token through the full encoder stack.

Lesson 4 • Pretrained Language Model Families

Surveys encoder-only, decoder-only, and encoder-decoder model families and their pretraining objectives. Students match model family to task type.

Lesson 1 • Latency Optimization Techniques

Applies quantization, distillation, and ONNX export to reduce inference latency. Students benchmark before and after each optimization.

Lesson 2 • Containerization and Orchestration

Packages NLP services in containers and introduces orchestration for scaling. Students write a container definition and deploy a multi-replica service.

Lesson 3 • Model Serialization and Packaging

Covers saving model weights, tokenizer configs, and preprocessing artifacts as a deployable bundle. Ensures reproducibility across environments.

Lesson 4 • Production Monitoring and Drift Detection

Implements logging, prediction monitoring, and data drift alerts for live NLP services. Students configure a monitoring dashboard and set alert thresholds.

Lesson 1 • Responsible AI in NLP Systems

Addresses bias detection, fairness metrics, privacy risks, and transparency requirements in NLP. Students audit a model for bias and document mitigation steps.

Lesson 2 • Multilingual and Cross-Lingual NLP

Applies multilingual pretrained models to cross-lingual transfer and translation tasks. Students evaluate zero-shot cross-lingual performance.

Lesson 3 • Text Summarization Systems

Implements extractive and abstractive summarization pipelines and evaluates output quality. Students tune length and faithfulness trade-offs.

Lesson 4 • NLP System Design Patterns

Introduces modular pipeline, microservice, and hybrid rule-neural design patterns for NLP. Students select and justify a design pattern for a given scenario.

Lesson 5 • Conversational AI and Dialogue Systems

Covers intent detection, slot filling, dialogue state tracking, and response generation. Students build a task-oriented dialogue prototype.