Excellent. Isn't Laurence just great! Fantastically deep knowledge, easy learning style, very practical presentation. And funny! A pure joy, highly relevant and extremely useful of course. Thank you!
Great course for anyone interested in NLP! This course focuses on practical learning instead of overburdening students with theory. Would recommend this to every NLP beginner/enthusiast out there!!
创建者 Mohd S K
•5 Stars for practical knowledge on sequence based models
创建者 Shubham P
•An comprehensive course to kick start your career in nlp
创建者 Balaji S
•Best course I've had on NLP compared to other platforms
创建者 Nguyen T V
•Thank you very much. This course is very useful for me!
创建者 Cosimo R
•Great course held by a terrific teacher! I recommend it
创建者 JP
•Laurence's voice in most videos are too low in volume.
创建者 Nayef R
•It is very helpful course for beginners in NLP subject
创建者 shahin
•Very good fundamental codes and concepts in practice .
创建者 ANINDITA S
•a very good course to start with tensor flow with NLP
创建者 SAUVIK B
•Very good course. Instructor explains very carefully.
创建者 Anuj S
•One of the best course in this series. Learned a lot.
创建者 Haoran C
•Please transfer the exercises from CoLab to Coursera.
创建者 Anh T T
•Thank you for your course. It is very helpful for me
创建者 Danny P
•Good course. Pretty basic intro to NLP using TF 2.0.
创建者 Widi W
•All are great as always! Keep the good work! Thanks
创建者 Avinash S
•Good optional course embedding with sequence models
创建者 patrick E
•Well presented and easy to understand key concepts
创建者 Argenis G
•Profesores excelentes y mucha información valiosa!
创建者 Hamza M
•My first course in NLP and it was quite detailed.
创建者 Teshan L
•Great course. Good content and great explanations
创建者 John G
•Great course with practical applications. Thanks!
创建者 Akshaykumar S
•Great course for tensorflow and seq2seq models !
创建者 Kenechukwu A
•Very explanatory course. As clear as clean water
创建者 Rommy S
•Thank you to the team for putting this together!
创建者 Hieu N
•Great implementation of Tensorflow on RNN model.