Go to Course: https://www.coursera.org/learn/probabilistic-models-in-nlp
### Course Review: Natural Language Processing with Probabilistic Models on Coursera If you're fascinated by the interplay between language and technology and wish to delve deep into the world of Natural Language Processing (NLP), the course "Natural Language Processing with Probabilistic Models" offered on Coursera is an exceptional choice. Part of the Natural Language Processing Specialization, this course is designed to equip you with essential tools and techniques used in the field of NLP. #### Overview of the Course The syllabus is meticulously crafted to guide you through the fundamental concepts of NLP using probabilistic models, augmenting your theoretical knowledge with practical applications. Throughout this course, you will engage in the following key activities: 1. **Build a Simple Auto-Correct Algorithm**: You will learn about the minimum edit distance and how dynamic programming can be utilized to create an efficient spell-checker. This foundational skill is crucial for understanding how language processing tools can enhance communication. 2. **Apply the Viterbi Algorithm for Part-of-Speech Tagging**: Discover the power of Markov models in computational linguistics by creating part-of-speech tags for a text corpus derived from the Wall Street Journal. This practical exercise will deepen your understanding of sentence structure and the role of each word within a sentence. 3. **Develop a Better Auto-Complete Algorithm**: Through an exploration of N-gram language models, you will learn to calculate sequence probabilities and build your own autocomplete language model using real data from Twitter. This hands-on experience will unlock the secrets of predictive text and suggestive typing. 4. **Create Your Own Word2Vec Model**: In a world where semantic meaning is paramount, you will learn how to harness the power of neural networks to compute word embeddings. By building your own Continuous Bag-of-Words (CBOW) model from Shakespeare's text, you'll gain insights into how words relate to each other in context, equipping you for advanced NLP tasks. #### Detailed Syllabus Overview - **Autocorrect**: The module kicks off with a theoretical introduction to autocorrect features, leading to practical exercises on building your own spellchecker. The concepts of minimum edit distance and dynamic programming are covered comprehensively. - **Part of Speech Tagging and Hidden Markov Models**: This section delves into the basic workings of Markov chains and Hidden Markov Models, offering insights into part-of-speech tagging with a practical approach using a recognizable text corpus. - **Autocomplete and Language Models**: Here, you will engage with N-gram models, learning to calculate sequence probabilities and construct your own language model, which will be immensely beneficial for those interested in enhancing user interfaces and text input systems. - **Word Embeddings with Neural Networks**: This module wraps up the course with an exploration of word embeddings, making it clear how these representations can effectively capture the nuances of semantic relationships. By building a CBOW model from Shakespeare's texts, you will gain practical experience that reinforces your learning. #### Recommendation I wholeheartedly recommend the "Natural Language Processing with Probabilistic Models" course on Coursera to anyone looking to strengthen their understanding and skills in NLP. Whether you are a beginner or someone with some prior knowledge, the structured format and practical approaches make the learning process enriching and accessible. The course instructors provide a balance of theoretical foundations and practical applications, ensuring that you can apply what you learn in real-world scenarios. With the rise of AI and language-based technologies, acquiring knowledge in NLP is not just beneficial but crucial for future prospects in various fields, including data science, software development, and artificial intelligence. ### Conclusion Overall, this course is a valuable asset for anyone serious about progressing in the field of Natural Language Processing. The hands-on projects are particularly beneficial for reinforcing learning and enabling participants to showcase their skills to potential employers. Enroll today and embark on a journey through the sophisticated world of language technologies!
Autocorrect
Learn about autocorrect, minimum edit distance, and dynamic programming, then build your own spellchecker to correct misspelled words!
Part of Speech Tagging and Hidden Markov ModelsLearn about Markov chains and Hidden Markov models, then use them to create part-of-speech tags for a Wall Street Journal text corpus!
Autocomplete and Language ModelsLearn about how N-gram language models work by calculating sequence probabilities, then build your own autocomplete language model using a text corpus from Twitter!
Word embeddings with neural networksLearn about how word embeddings carry the semantic meaning of words, which makes them much more powerful for NLP tasks, then build your own Continuous bag-of-words model to create word embeddings from Shakespeare text.
In Course 2 of the Natural Language Processing Specialization, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. By the end of
The course is exceptional in its own way by bringing people to the understanding of probabilistic models. Crisp & Clear. But one need to explore & practise more to gain expertise.
I'm really thankful to the professors for sharing there knowledge and experience and creating this excellent course. I have learnt a a lot. Thank You !!!
Very good course! helped me clearly learn about Autocorrect, edit distance, Markov chains, n grams, perplexity, backoff, interpolation, word embeddings, CBOW. This was very helpful!
This course is very good introduction to NLP Probabilistic models such as Hidden Markov model, N-Gram Language model, and Word2Vec with Python programming assignments.
Thoroughly relished this course. Each and every concept is explained in depth as well as there is a companion notebook to explain as well as practically implement the concepts.