Artificial Intelligence & Data Science · 1.9

NLP Engineer

Build systems that understand, generate, and manipulate human language at scale — from chatbots to document intelligence.

5–7 months5 phases29 skills

Phase 01 · 6h

Foundations

Python fluency

Python fluencygenerators, decorators, type hints, virtual environments with poetry, asyncio for concurrent API calls

Linear algebra essentials

Linear algebra essentialsvectors, matrices, dot products for similarity measurement, eigenvalues — as they underpin word embeddings and attention

Probability and statistics

Probability and statisticsBayes' theorem, distributions, hypothesis testing — for understanding probabilistic language models

ML refresher

ML refreshersupervised vs unsupervised, train/test splits, evaluation metrics (accuracy, precision, recall, F1), cross-validation

Phase 02 · 12h

Classical NLP

Text preprocessing

Text preprocessingtokenization (word-level, sentence-level with NLTK and spaCy), stemming (PorterStemmer), lemmatization (spaCy's lemmatizer using part-of-speech), stop-word removal, regex for pattern matching and text cleaning

Feature extraction

Feature extractionbag-of-words (CountVectorizer), TF-IDF (TfidfVectorizer — terms weighted by frequency in document relative to corpus), n-grams (bigrams, trigrams for capturing word sequences)

Word embeddings

Word embeddingsWord2Vec (Skip-gram predicts context from target, CBOW predicts target from context — train with gensim), GloVe (trained on co-occurrence statistics — load pretrained vectors), FastText (subword information handles out-of-vocabulary words)

Text classification with traditional ML

Text classification with traditional MLTfidfVectorizer → LogisticRegression pipeline, Naive Bayes, SVM on text — evaluation with classification_report

spaCy for NLP

spaCy for NLPNamed Entity Recognition (NER — PERSON, ORG, GPE, DATE, MONEY), POS tagging, dependency parsing, custom entity training with spaCy's config system

Topic modeling

Topic modelingLatent Dirichlet Allocation (LDA with gensim — discover topics as distributions over words), NMF (Non-negative Matrix Factorization) as alternative, topic coherence scores for evaluation

Sentiment analysis

Sentiment analysisbuilding a complete pipeline from data collection to model deployment

Phase 03 · 10h

Deep Learning for NLP

Neural network fundamentals

Neural network fundamentalsforward/backpropagation, activation functions, loss functions, Adam optimizer — implemented in PyTorch

RNNs, LSTMs, GRUs

RNNs, LSTMs, GRUssequence modeling for text, vanishing gradient problem, LSTM gates (forget, input, output), bidirectional processing, attention mechanism (Bahdanau attention for seq2seq models)

Transformer architecture deep-dive

Transformer architecture deep-diveself-attention (Q, K, V matrices — each token attends to all others), multi-head attention (parallel attention heads capture different relationships), positional encoding (sinusoidal functions add position information), layer normalization and residual connections

Hugging Face ecosystem

Hugging Face ecosystemTransformers library (AutoModel, AutoTokenizer, pipeline for quick inference), Tokenizers (BPE, WordPiece — fast tokenization), Datasets library (load, preprocess, cache), Trainer API (TrainingArguments, Trainer — simplified training loop with automatic mixed precision, gradient accumulation, evaluation)

Fine-tuning pre-trained models

Fine-tuning pre-trained modelsBERT for text classification (BertForSequenceClassification), RoBERTa (more robust pretraining), DeBERTa (disentangled attention — currently best for many NLU tasks) — for classification, NER (token classification), extractive QA (question answering)

Text generation with GPT-family

Text generation with GPT-familyautoregressive generation, temperature and top-p sampling, beam search vs nucleus sampling

Phase 04 · 15h

Large Language Models & Advanced Topics

LLM landscape

LLM landscapeGPT-4o, Claude 3.5 Sonnet, Llama 3, Mistral, Gemini — capabilities, context windows, pricing, and trade-offs for different use cases

Parameter-efficient fine-tuning

Parameter-efficient fine-tuningLoRA (freeze pretrained weights, add small rank-r matrices to attention layers — reduces trainable parameters by 90%+), QLoRA (4-bit quantization + LoRA — fine-tune 7B model on 8GB VRAM), prefix tuning, adapter layers — all via Hugging Face PEFT library

RAG (Retrieval-Augmented Generation)

RAG (Retrieval-Augmented Generation)chunking strategies (recursive character splitting, semantic chunking), embedding models (sentence-transformers all-MiniLM-L6-v2 for local, OpenAI text-embedding-3-small for API), vector databases (Pinecone, Weaviate, Chroma), retrieval pipeline design, evaluation with RAGAS framework (context relevance, faithfulness, answer relevance), advanced techniques (hybrid search combining vectors + BM25, cross-encoder re-ranking with ms-marco-MiniLM)

Evaluation

EvaluationBLEU (n-gram overlap for translation), ROUGE (recall-oriented for summarization), BERTScore (semantic similarity using contextualized embeddings), human evaluation design with rubrics, LLM-as-judge for scalable quality assessment

Multilingual NLP

Multilingual NLPcross-lingual transfer learning, multilingual models (mBERT, XLM-RoBERTa — trained on 100+ languages), language identification, zero-shot cross-lingual classification

Speech-to-text integration

Speech-to-text integrationOpenAI Whisper (transcribe audio to text — pip install openai-whisper, support for 99 languages), assembling speech + NLP pipelines (transcribe → NER → summarize)

Responsible NLP

Responsible NLPdetecting and measuring bias in text models (gender, racial), toxicity filtering (Perspective API, custom classifiers), fairness auditing across demographic groups

Phase 05 · 17h

Production & Capstone

Model serving

Model servingFastAPI endpoint wrapping a Hugging Face model, TorchServe for PyTorch models, ONNX export for optimized cross-platform inference, quantization (GPTQ, AWQ) for deploying large models efficiently

Monitoring

Monitoringdrift detection for language models (input text distribution shifts, prediction confidence changes), logging and A/B testing for NLP features

Capstone Project 1

Capstone Project 1End-to-end document intelligence pipeline — OCR (Tesseract or Amazon Textract) → NER extraction → structured JSON output → database storage, with evaluation metrics on a labeled test set

Capstone Project 2

Capstone Project 2Custom RAG-powered chatbot with RAGAS evaluation suite — ingest company documentation, build retrieval pipeline with Chroma, serve with FastAPI, evaluate on 50 Q&A pairs, iterate on chunking and retrieval strategy

Capstone Project 3

Capstone Project 3Multilingual sentiment analysis system — fine-tune XLM-RoBERTa on multi-language sentiment data, evaluate per-language performance, conduct bias audit across languages, deploy as an API

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