NLP: Classification & Vector Spaces
Master NLP fundamentals through hands-on implementation of sentiment analysis, word embeddings, and machine translation.
Master NLP fundamentals through hands-on implementation of sentiment analysis, word embeddings, and machine translation.
This comprehensive course covers essential Natural Language Processing techniques using classification and vector spaces. Students learn to perform sentiment analysis using logistic regression and naive Bayes, work with vector space models to discover word relationships, and implement machine translation using word embeddings and locality-sensitive hashing. The course combines theoretical foundations with practical Python implementation through hands-on programming assignments.
4.6
(4,433 ratings)
1,88,838 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 4 more
Powered by
What you'll learn
Implement sentiment analysis using logistic regression and naive Bayes
Create vector space models to analyze word relationships
Apply PCA for dimensionality reduction and visualization
Develop English-French translation using word embeddings
Implement document search using locality-sensitive hashing
Skills you'll gain
This course includes:
3.73 Hours PreRecorded video
8 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This course provides a comprehensive introduction to Natural Language Processing using classification and vector spaces. Students progress through four key modules covering sentiment analysis, naive Bayes classification, vector space models, and machine translation. The curriculum combines theoretical concepts with practical implementation, featuring extensive programming assignments and real-world applications. Each module builds foundational knowledge for advanced NLP techniques.
Sentiment Analysis with Logistic Regression
Module 1 · 9 Hours to complete
Sentiment Analysis with Naïve Bayes
Module 2 · 7 Hours to complete
Vector Space Models
Module 3 · 8 Hours to complete
Machine Translation and Document Search
Module 4 · 8 Hours to complete
Fee Structure
Instructors
Pioneering AI Educator and Best-Selling Author
Laurence Moroney is an award-winning artificial intelligence researcher and best-selling author dedicated to making AI and machine learning accessible to everyone. As an instructor at DeepLearning.AI, he has taught millions through MOOCs and YouTube, while also serving as a keynote speaker at various events. Moroney is a fellow of the AI Fund and advises several AI startups, leveraging his expertise to foster innovation in the field. Based in Seattle, Washington, he is also an active member of the Science Fiction Writers of America, having authored multiple sci-fi novels and comic books. When not immersed in technology, he enjoys indulging in coffee and exploring creative writing.
Stanford AI Educator Pioneers Global Learning Through Course Innovation and EdTech Leadership
Younes Bensouda Mourri is a distinguished AI educator and entrepreneur who has significantly impacted global tech education. Born and raised in Morocco, he earned his B.S. in Applied Mathematics and Computer Science and M.S. in Statistics from Stanford University, where he now teaches Artificial Intelligence both on campus and online. As the founder of LiveTech.AI, he develops AI tools to transform academic institutions, while his courses have reached over 1.3 million learners worldwide, with 23% securing AI-related jobs after completion. His contributions include co-creating Stanford's Applied Machine Learning, Deep Learning, and Teaching AI courses, as well as developing the highly successful Natural Language Processing Specialization for DeepLearning.AI. Starting as a teaching assistant in Andrew Ng's Machine Learning course, he rose to become an Adjunct Lecturer at Stanford by age 22, demonstrating his commitment to democratizing AI education. Through his work with major companies like ASML, CISCO, and Boston Consulting Group, he continues to advance AI education while focusing on developing innovative NLP tools for personalized feedback and chain-of-thought reasoning
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.