Natural Language Processing

Course Overview

This online course on Natural Language Processing (NLP) covers a wide range of tasks from basic to advanced, such as sentiment analysis, summarization, and dialogue state tracking. Upon completion, students will be able to recognize NLP tasks in their day-to-day work, propose approaches, and judge which techniques are likely to work well. The final project is devoted to building a conversational chat-bot that will assist with search on StackOverflow website.

Throughout the lectures, the course will aim to find a balance between traditional and deep learning techniques in NLP and cover them in parallel. Core techniques will not be treated as black boxes, and students will gain an in-depth understanding of what’s happening inside. To succeed in that, familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks is expected. Some materials are based on one-month-old papers and introduce students to the very state-of-the-art in NLP research.

For technical problems, students can contact the course staff at [email protected].

[Applications]
Upon completing this course, students will be able to apply their knowledge of Natural Language Processing to recognize NLP tasks in their day-to-day work, propose approaches, and judge what techniques are likely to work well. They will also be able to build their own conversational chat-bot that will assist with search on StackOverflow website. Students should also be able to find a balance between traditional and deep learning techniques in NLP and apply them in parallel.

[Career Paths]
1. Natural Language Processing Engineer: Natural Language Processing Engineers are responsible for developing and deploying natural language processing (NLP) models and algorithms. They must have a strong understanding of NLP techniques, such as sentiment analysis, summarization, dialogue state tracking, and text classification. They must also be familiar with machine learning setup, deep neural networks, linear algebra, and probability theory. As NLP technology continues to evolve, Natural Language Processing Engineers must stay up-to-date on the latest trends and developments in the field.

2. Natural Language Processing Researcher: Natural Language Processing Researchers are responsible for researching and developing new NLP models and algorithms. They must have a strong understanding of NLP techniques, such as sentiment analysis, summarization, dialogue state tracking, and text classification. They must also be familiar with machine learning setup, deep neural networks, linear algebra, and probability theory. Natural Language Processing Researchers must stay up-to-date on the latest trends and developments in the field and be able to identify and develop new NLP models and algorithms.

3. Natural Language Processing Consultant: Natural Language Processing Consultants are responsible for providing advice and guidance to clients on the best NLP models and algorithms to use for their specific needs. They must have a strong understanding of NLP techniques, such as sentiment analysis, summarization, dialogue state tracking, and text classification. They must also be familiar with machine learning setup, deep neural networks, linear algebra, and probability theory. Natural Language Processing Consultants must stay up-to-date on the latest trends and developments in the field and be able to provide clients with the best advice and guidance.

4. Natural Language Processing Product Manager: Natural Language Processing Product Managers are responsible for managing the development and deployment of NLP products. They must have a strong understanding of NLP techniques, such as sentiment analysis, summarization, dialogue state tracking, and text classification. They must also be familiar with machine learning setup, deep neural networks, linear algebra, and probability theory. Natural Language Processing Product Managers must stay up-to-date on the latest trends and developments in the field and be able to identify and develop new NLP products.

[Education Paths]
Recommended Degree Paths:
1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, natural language processing, and machine learning. This degree path is ideal for those interested in pursuing a career in software engineering, data science, or machine learning.

2. Master of Science in Artificial Intelligence: This degree path provides students with a deep understanding of artificial intelligence and its applications. It covers topics such as machine learning, natural language processing, computer vision, robotics, and deep learning. This degree path is ideal for those interested in pursuing a career in research or development in the field of artificial intelligence.

3. Doctor of Philosophy in Natural Language Processing: This degree path provides students with a comprehensive understanding of natural language processing and its applications. It covers topics such as text mining, machine translation, dialogue systems, and information retrieval. This degree path is ideal for those interested in pursuing a career in research or development in the field of natural language processing.

Developing Trends:
1. Natural Language Generation: Natural language generation (NLG) is a rapidly growing field of research that focuses on the automatic generation of natural language from structured data. This technology is being used in a variety of applications, such as summarization, question answering, and dialogue systems.

2. Deep Learning: Deep learning is a subset of machine learning that uses neural networks to learn from data. It has been used to great success in a variety of tasks, including natural language processing, computer vision, and speech recognition.

3. Reinforcement Learning: Reinforcement learning is a type of machine learning that focuses on learning from rewards and punishments. It has been used to great success in a variety of tasks, including natural language processing, robotics, and game playing.