COURSE DESCRIPTION:
The Natural Language Processing (NLP) course offers an in-depth curriculum aimed at providing students with the essential knowledge and skills to effectively process, analyze, and derive insights from textual information. As a branch of artificial intelligence (AI), NLP focuses on empowering machines to comprehend, interpret, and produce human language.
The program includes foundational concepts in linguistics, text preprocessing methods, and advanced NLP techniques such as word embeddings, sequence modeling, and transformers. Participants will engage with widely-used NLP tools and libraries like NLTK, spaCy, and Hugging Face, applying machine learning and deep learning strategies to tackle practical challenges, including sentiment analysis, text classification, machine translation, and text summarization.
Upon completion of the course, students will be prepared to design and execute NLP pipelines, gaining practical experience with datasets and projects pertinent to both academic and industry settings.
CERTIFICATION
Upon finishing the Natural Language Processing (NLP) course, participants will be awarded a Certificate in Natural Language Processing.
To obtain this certification, students must fulfill several requirements: they need to complete all weekly assignments and quizzes, successfully pass both the midterm and final exams, and submit a capstone project that entails developing an NLP application or conducting an in-depth analysis of a text dataset. This certification will validate the student’s proficiency in applying NLP techniques to practical challenges, equipping them for careers as NLP Engineers, Data Scientists, AI Specialists, or Researchers.
LEARNING OUTCOME
By the conclusion of the Natural Language Processing (NLP) course, participants will acquire a comprehensive understanding of several key areas. They will grasp foundational concepts and challenges in NLP, including the structure of human language, which encompasses syntax, semantics, and pragmatics, while also becoming acquainted with essential NLP tasks such as tokenization and stemming. Students will learn to preprocess text data effectively, employing techniques like stopword removal and case normalization, and will explore both traditional and modern data representation methods, including Bag of Words and word embeddings.
Additionally, they will delve into language modeling, focusing on n-gram models and advanced sequence modeling techniques like RNNs and Transformers. The course will cover sentiment analysis and text classification, enabling students to build models and apply various machine learning algorithms for categorizing text. They will also learn to identify entities through Named Entity Recognition (NER) and extract structured information from unstructured data. The curriculum includes an introduction to machine translation and text summarization, as well as the principles of conversational AI and chatbot development.
Advanced topics will involve exploring cutting-edge NLP techniques using models such as BERT and GPT, along with methods for fine-tuning these models for specific tasks. Finally, students will gain insights into the practical applications of NLP in various industries, including search engines and voice assistants, equipping them with the knowledge to apply their skills in real-world contexts.
Course Features
- Lectures 33
- Quiz 0
- Duration 54 hours
- Skill level All levels
- Language English
- Students 28
- Assessments Yes
