Natural Language Processing (NLP) Professional Course

• Guided, career-focused training for building real language AI systems.

• Covers text processing, linguistic features, classical ML, embeddings, deep NLP, transformers, RAG, LLM applications, deployment and MLOps.

• Learn through labs, assignments, project work, evaluation practice and responsible-AI reviews.

• Designed for aspiring NLP, ML and AI engineers, LLM developers, search engineers, data scientists and chatbot developers.

• NLP tasks: classification, extraction, search, summarization, QA, chatbots and RAG.

• Text data basics: corpora, documents, Unicode, noise, metadata, datasets, labels, splits, leakage and privacy.

• Preprocessing: normalization, tokenization, sentence splitting, stop words, stemming, lemmatization and language detection.

• Python setup: Jupyter, VS Code, regex, Pandas, NumPy, scikit-learn, NLTK, spaCy and Hugging Face.

• Evaluation: accuracy, precision, recall, F1, confusion matrices, ROUGE/BLEU awareness and human review.

• Linguistic pipelines: tokenization, sentence segmentation, POS tagging, morphology and lemmatization.

• Dependency parsing, noun chunks, parse trees, relation patterns and syntactic extraction.

• Named entity recognition for people, organizations, locations, products, dates, money and custom labels.

• Rule-based matching with regex, token patterns, phrase matching, gazetteers and dictionaries.

• Build spaCy/NLTK labs for entity extraction, parsing, rules and structured reports.

• Text features: bag-of-words, word and character n-grams, count vectors and TF-IDF.

• Classical models: Naive Bayes, Logistic Regression, Linear SVM, Random Forest and baseline selection.

• Applications: sentiment analysis, spam detection, topic classification, intent detection and support-ticket tagging.

• Evaluation: precision, recall, F1, ROC/AUC awareness, threshold tuning, imbalance handling and error analysis.

• Unsupervised analysis: LSA, LDA, NMF, clustering and document similarity.

• Dense vectors, cosine similarity, nearest neighbours, distance metrics and semantic similarity.

• Word2Vec, GloVe, fastText and sentence embeddings for documents, matching and clustering.

• Keyword, semantic and hybrid search with reranking, query rewriting and relevance checks.

• Vector search awareness: FAISS, Chroma, Pinecone, Qdrant, Weaviate and pgvector.

• Build a semantic-search engine with chunking, metadata, filters, retrieval metrics and feedback clustering.

• Neural NLP foundations: tensors, token IDs, padding, masks, embeddings, batches, loss functions and checkpoints.

• Text CNNs, RNNs, LSTMs, GRUs, bidirectional networks and sequence classification.

• Attention, context vectors, encoder-decoder models, sequence-to-sequence tasks and transformer motivation.

• PyTorch/Keras training workflow: dataloaders, callbacks, early stopping, TensorBoard, GPU awareness and reproducibility.

• Diagnose rare words, negation, sarcasm, label noise, class imbalance and domain mismatch.

• Transformer architecture: self-attention, multi-head attention, positional encoding, encoders, decoders, residuals and masking.

• Model families: BERT, RoBERTa, DistilBERT, T5, GPT-style and multilingual models.

• Hugging Face workflows: pipelines, AutoTokenizer, AutoModel, datasets, Trainer, model hub and inference.

• Fine-tune for classification, token classification, NER, QA, summarization, translation and generation.

• Use LoRA/PEFT awareness, quantization, task metrics, responsible-use checks and model-card notes.

• Extract entities, relations, events, key phrases, dates, amounts, tables, sections and structured JSON.

• NER customization: label design, annotation guidelines, IOB tags, train/dev/test splits and entity-level metrics.

• Build extractive and generative QA with context windows, answer spans, no-answer cases and F1 evaluation.

• Summarization: extractive vs abstractive, length control, coverage, factual consistency and review rubrics.

• Document workflows for PDFs, OCR-aware text, invoices, contracts, resumes, policies and knowledge bases.

• LLM basics: instruction following, tokens, context windows, temperature, prompts, hallucinations and refusal behaviour.

• Prompt engineering: task framing, examples, output constraints, rubrics, versioning and tests.

• RAG: chunking, embeddings, vector search, retrieval, reranking, citations, grounding and answer faithfulness.

• Structured JSON output, validation, retries, confidence flags, tool/function calling and safe workflow automation.

• Build a RAG chatbot and tool-using assistant with PII, prompt-injection, access-control and evaluation safeguards.

• Evaluation strategy: baselines, gold labels, validation/test sets, metrics, human review, error taxonomies and release criteria.

• Test preprocessing, schemas, tokenizers, models, prompts, adversarial cases, regression checks and data validation.

• Deploy with FastAPI, Flask, Streamlit, Gradio, batch jobs, real-time APIs, model servers and containers.

• Monitor drift, confidence, latency, cost, failure rates, feedback, toxic outputs and retrieval misses.

• Use dataset/model/prompt/vector-index versioning, governance, PII redaction, audit trails, model cards and runbooks.

• Plan an end-to-end NLP, ML or LLM solution with users, data, metrics, model path, deployment target and demo scope.

• Portfolio projects: preprocessing toolkit, sentiment classifier, NER extractor, semantic search, transformer fine-tune, QA bot, RAG app and deployed demo.

• Document problem statement, data, preprocessing, model choices, metrics, error analysis, limits, safety and screenshots.

• Practice interviews on tokenization, TF-IDF, embeddings, POS/NER, transformers, fine-tuning, RAG, deployment and responsible AI.

• Deliver clean notebooks, modular scripts, configs, requirements, logging, checkpoints, README, architecture diagrams and demo evidence.