This course will guide you through the basics of Natural Language Processing (NLP) using Python. You will learn how to preprocess text, extract features, and build models to analyze and understand human language.
Course Outline
- Introduction to NLP: An overview of the field, its applications, and the tools we will use.
- Text Preprocessing: Learn how to clean and prepare text data for analysis.
- Tokenization: Splitting text into words, sentences, or other meaningful elements.
- Part-of-Speech Tagging: Identifying the grammatical parts of speech in text.
- Named Entity Recognition (NER): Detecting and classifying named entities in text.
- Sentiment Analysis: Analyzing the sentiment of text data.
- Text Classification: Building models to classify text into predefined categories.
- Word Embeddings: Understanding and using word embeddings for NLP tasks.
Prerequisites
- Basic knowledge of Python programming.
- Familiarity with basic machine learning concepts.
Resources
- Python for Data Science - A comprehensive course on Python for data analysis.
- Machine Learning Basics - Learn the fundamentals of machine learning.
Learning Outcomes
- Understand the basics of NLP and its applications.
- Master the use of Python libraries for NLP tasks.
- Build and evaluate NLP models for various tasks.
NLP Diagram
In this course, you will learn how to preprocess text, extract features, and build models to analyze and understand human language. Whether you're a beginner or an experienced data scientist, this course will provide you with the knowledge and skills to tackle NLP challenges.
If you have any questions or need further assistance, please don't hesitate to reach out to our support team.