CNLP study guide
CNLP · Work through your exam, one topic at a time.
The CNLP is a professional certification for practitioners who build, evaluate, and operate natural language processing systems in production. It suits machine learning engineers moving into NLP, data scientists who own text pipelines, and technical leads who need to reason about model behavior, cost, and risk rather than only training accuracy. The CNLP study guide has 8 sections and about 2,479 words.
01Natural Language Processing Fundamentals
Natural Language Processing Fundamentals tests whether you can describe how language is represented computationally and why ambiguity, rather than syntax alone, drives most design choices. Morphology : how words are built from roots and affixes; it determines whether stemming or lemmatization is appropriate.
365 words, about 2 min02Text Preprocessing and Feature Engineering
Text Preprocessing and Feature Engineering tests whether you can turn raw strings into reproducible, leakage-free model inputs that behave identically in training and serving. Unicode NFKC normalization, case folding, and accent handling must be applied identically at train and inference time.
337 words, about 2 min03Classical and Statistical NLP Methods
Classical and Statistical NLP Methods tests probabilistic sequence models, the assumptions behind them, and the failure modes those assumptions create. Naive Bayes : conditional independence assumption; use add-one (Laplace) smoothing or zero counts annihilate the product.
308 words, about 2 min04Machine Learning and Deep Learning for NLP
Machine Learning and Deep Learning for NLP tests how learned representations replaced sparse features, and which training decisions make or break a model. word2vec: skip-gram predicts context from a target word, CBOW predicts the target from context; negative sampling replaces the full softmax.
300 words, about 2 min05Large Language Models and Transformers
Large Language Models and Transformers tests the architecture, pretraining objectives, adaptation methods, and decoding controls that govern modern systems. Self-attention: Attention(Q,K,V) = softmax(QKᵀ/√d k)V .
281 words, about 1 min06Model Evaluation, Ethics, and Responsible AI
Model Evaluation, Ethics, and Responsible AI tests whether you can measure a system honestly and reason precisely about whom it can harm. Confusion matrix to precision, recall, F1.
271 words, about 1 min07Deployment, MLOps, and Production NLP Systems
Deployment, MLOps, and Production NLP Systems tests the gap between a notebook model and a service that stays correct under load, drift, and cost pressure. Batch, online (REST/gRPC), and streaming serving carry different latency contracts; quote p50, p95, and p99, never a mean alone.
311 words, about 2 min08Applied NLP Use Cases and Business Integration
Applied NLP Use Cases and Business Integration tests whether you can map a business problem onto a task, a metric, a deployment pattern, and an explicit cost model. Classify the task first: text classification, information extraction, retrieval-augmented generation, summarization, generation, or conversational assistance.
306 words, about 2 min