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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-natural-language-processing-using-case-studies/
课程评论：没有评论
**课程名称：Master Natural Language Processing using case studies (精通自然语言处理：案例研究)** **课程概述：** 本课程专为有志成为NLP工程师和数据科学家的学员设计。由 provenienti IIT 的数学和数据科学专家精心打造，课程以简单易懂的方式讲解复杂的理论、算法和代码库，适合初学者。我们将引导您一步步深入NLP的世界，通过每个教程学习新技能，提升对这一充满挑战但回报丰厚的领域（数据科学的一个子领域）的理解，从入门到高级。课程中包含真实世界项目的解决方案，方便学员实践。案例研究提供详细的分步讲解。具备机器学习和深度学习的先修知识将有益，但课程本身也涵盖了所有必需的先修知识。 **涵盖的主要主题：** 1. NLP和正则表达式入门 2. 词汇处理入门 3. 高级词汇处理 4. 基础句法处理 5. 中级句法处理 6. 高级句法处理 7. 概率方法 8. 句法处理与真实世界项目 9. 语义处理入门 10. 高级语义处理（第一部分） 11. 高级语义处理（第二部分） **先修知识（建议）：** Python、机器学习、深度学习。如果缺乏这些知识，课程中也会进行覆盖。
Wants to become a expert NLP engineer and data scientist? Then this is a right course for you.This course has been designed by IIT professionals who have mastered in Mathematics and Data Science. We will be covering complex theory, algorithms and coding libraries in a very simple way which can be easily grasped by any beginner as well.We will walk you step-by-step into the World of NLP. With every tutorial you will develop new skills and improve your understanding towards the challenging yet lucrative sub-field of Data Science from beginner to advance level. We have solved few real world projects as well during this course and have provided complete solutions so that students can easily implement what have been taught. Case studies are explained in detail with step by step instructions. Prior Knowledge of Machine Learning and deep learning is beneficial , if not we have covered all required pre-requisites in the course itself.We have covered following topics in detail in this course:1) Introduction to NLP and Regex2) Introduction to Lexical Processing3) Advanced Lexical Processing4) Basic Syntactic Processing5) Intermediate Syntactic Processing6) Advanced Syntactic Processing7) Probabilistic Approach8) Syntactic Processing With Real World Project9) Introduction to Semantic Processing10) Advance Semantic Processing Part111) Advance Semantic Processing Part212) Prereqs: Python, Machine Learning , Deep Learning
