Natural Language Processing (NLP) with spaCy

• Tokenization: Splits text into tokens (words, punctuation).
• Part-of-Speech (POS) Tagging: Labels tokens with grammatical roles (e.g., noun, verb).
• Named Entity Recognition (NER): Identifies and classifies entities like names, dates, and locations.
• Dependency Parsing: Analyzes grammatical structure and word relationships within sentences.
• Lemmatization: Converts words to their base forms (e.g., "running" to "run").

Before learning Natural Language Processing (NLP) with spaCy, you should have:

1. Python Programming: Proficiency in Python, including understanding data structures and libraries.
2. Basic NLP Concepts: Knowledge of fundamental NLP concepts like tokenization, POS tagging, and lemmatization.
3. Text Data Handling: Skills in managing and preprocessing text data.
4. Machine Learning Basics: Understanding of basic machine learning principles for tasks like text classification.

By learning Natural Language Processing (NLP) with spaCy, you gain:

1. Advanced Text Processing: Expertise in tokenization, lemmatization, and dependency parsing.
2. Entity Recognition: Skills to identify and classify named entities in text.
3. Text Classification: Ability to build and apply models for categorizing text.
4. Semantic Similarity: Knowledge of using word embeddings to measure text similarity.