This repository contains my personal notes, code examples, and learning materials from the Natural Language Processing Specialization offered by DeepLearning.AI on Coursera. The course is instructed by Łukasz Kaiser.
Link to course
Link take notes:
This specialization delves into the fascinating world of Natural Language Processing (NLP), equipping learners with the skills to build cutting-edge NLP systems. The course covers a wide range of topics, from traditional techniques like classification and probabilistic models to the latest advancements in sequence models and attention mechanisms.
The specialization is divided into four distinct courses:
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Natural Language Processing with Classification and Vector Spaces: This course introduces the fundamental concepts of NLP, including text classification, vector space models, and word embeddings.
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Natural Language Processing with Probabilistic Models: This course explores probabilistic models for NLP, such as Naive Bayes, hidden Markov models, and word2vec.
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Natural Language Processing with Sequence Models: This course covers sequence models like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, which are essential for tasks like machine translation and sentiment analysis.
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Natural Language Processing with Attention Models: This course introduces attention mechanisms, a key innovation in NLP that has led to breakthroughs in machine translation, text summarization, and question-answering systems.
- Course Notes: Summaries of key concepts and takeaways from each module.
- Code Examples: Python code implementations of NLP techniques and algorithms.
- Assignments (Optional): Solutions to course assignments (if included).
This repository is licensed under the MIT License. Feel free to use and modify the materials as needed.
I am grateful to DeepLearning.AI and Łukasz Kaiser for creating this excellent course. I would also like to thank the Coursera community for their support and collaboration.
