This course is designed to help you master modern Natural Language Processing (NLP) techniques using spaCy, one of the fastest and most practical NLP libraries in Python. You will learn how to process, analyze, and understand human language data through advanced machine learning models such as text classification, named entity recognition, and sentiment analysis. The training covers key concepts including tokenization, vectorization, model training, and pipeline customization while working with real-world datasets. By the end of the course, you will be able to build and deploy efficient NLP applications used in chatbots, recommendation systems, information extraction, and more — making you industry-ready for the growing field of AI-driven language technologies.
Module 1: Introduction to NLP & spaCy What is NLP? Applications & Use Cases Overview of spaCy ecosystem Installation & environment setup spaCy pipelines and processing workflow Comparing spaCy with NLTK, Transformers, HuggingFace.
Module 2: Text Processing & Linguistic Features Tokenization, Lemmatization & Stemming Stop words, POS tagging, Morphology Dependency parsing spaCy Doc, Span, Token objects — usage & attributes Linguistic visualization with displaCy.
Module 3: Data Preprocessing for Machine Learning Text cleaning and normalization Sentence segmentation Custom tokenization rules in spaCy Building ML-ready features Vocabulary building & vector representation basics.
Module 4: Word Embeddings & Vector Representations spaCy pre-trained word vectors Word2Vec, GloVe, FastText concepts Vector similarity & semantic similarity Text classification features from embeddings. Custom vector models.
Module 5: Text Classification with spaCy Rule-based classification vs ML-based classification Building a text classification pipeline Multi-class & multi-label classification Training, evaluating & improving classification performance Exporting and deploying models.
Module 6: Named Entity Recognition (NER) Understanding NER systems spaCy NER model architecture Custom NER training workflow Labeling data for NER Improving NER performance with active learning.
Module 7: Information Extraction & Pattern Matching Phrase matching & token matching Dependency-based extraction Knowledge graph construction basics Extracting entities, relations & actions from text Custom rule-based extractors with spaCy matchers.
Module 8: Advanced NLP Pipelines Custom pipeline components Using spaCy for large text datasets spaCy project templates Behind-the-scenes: spaCy architecture Combining ML + rule-based NLP for hybrid solutions.
Module 9: spaCy + Machine Learning Integration Integrating spaCy with Scikit-learn Feature engineering for ML models Training ML models for NLP tasks spaCy + transformer pipelines (spaCy Transformers) Transfer learning for NLP.
Module 10: Deployment, Optimization & Real-world Projects Model packaging & deployment Using APIs for NLP applications Performance tuning and scaling Versioning and managing models End-to-end NLP project using spaCy (classification or NER).
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Email: coursedivine@gmail.com
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