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Updated in [June 30th, 2023]

This course, Natural Language Processing with Probabilistic Models, is the second of four courses in the Natural Language Processing Specialization. It is designed to help learners gain an understanding of how to create a simple auto-correct algorithm using minimum edit distance and dynamic programming, apply the Viterbi Algorithm for part-of-speech (POS) tagging, write a better auto-complete algorithm using an N-gram language model, and write their own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. Learners will also gain the skills to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build a chatbot.

This course is taught by two experts in NLP, machine learning, and deep learning: Younes Bensouda Mourri, an Instructor of AI at Stanford University, and Łukasz Kaiser, a Staff Research Scientist at Google Brain. Learners who complete this course will be well-prepared to take the remaining courses in the Specialization.

[Applications]
Upon completion of this course, students can apply the knowledge and skills acquired to develop natural language processing applications such as auto-correct algorithms, part-of-speech tagging, N-gram language models, Word2Vec models, question-answering systems, sentiment analysis tools, language translation tools, text summarization tools, and chatbots.

[Career Paths]
One job position path that is recommended to learners of this course is Natural Language Processing (NLP) Engineer. NLP Engineers are responsible for developing and deploying natural language processing models and algorithms to solve real-world problems. They must have a strong understanding of machine learning, deep learning, and natural language processing techniques, as well as the ability to develop and deploy models in production. NLP Engineers must also be able to work with large datasets and have experience with programming languages such as Python, Java, and C++.

The development trend of NLP Engineers is rapidly growing due to the increasing demand for natural language processing applications in various industries. Companies are increasingly looking for NLP Engineers to develop and deploy models that can process large amounts of data and provide accurate results. As the demand for NLP Engineers grows, so does the need for more advanced techniques and technologies to improve the accuracy and speed of natural language processing models. Additionally, NLP Engineers must stay up-to-date with the latest advancements in the field in order to remain competitive.

[Education Paths]
The recommended educational path for learners of this course is to pursue a degree in Natural Language Processing (NLP). This degree typically involves courses in linguistics, computer science, and artificial intelligence. Students will learn about the fundamentals of NLP, including algorithms, data structures, and programming languages. They will also learn about the various applications of NLP, such as machine translation, text summarization, and question-answering. Additionally, they will gain an understanding of the development of NLP technologies, such as deep learning and neural networks.

The development trend of NLP is rapidly evolving, with new technologies and applications being developed every day. As such, students pursuing a degree in NLP should be prepared to stay up-to-date with the latest advancements in the field. Additionally, they should be prepared to apply their knowledge to real-world problems, such as natural language understanding, text classification, and sentiment analysis.