This online course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection.
提供方
高级机器学习 專項課程俄罗斯国家研究型高等经济大学課程信息
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100% 在線
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第 6 門課程（共 7 門）
高級
完成時間大約為32 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
您將獲得的技能
- Chatterbot
- Tensorflow
- Deep Learning
- Natural Language Processing
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 6 門課程（共 7 門）
高級
完成時間大約為32 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
提供方
俄罗斯国家研究型高等经济大学
HSE University is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more.
授課大綱 - 您將從這門課程中學到什麼
完成時間為 5 小時
Intro and text classification
In this module we will have two parts: first, a broad overview of NLP area and our course goals, and second, a text classification task. It is probably the most popular task that you would deal with in real life. It could be news flows classification, sentiment analysis, spam filtering, etc. You will learn how to go from raw texts to predicted classes both with traditional methods (e.g. linear classifiers) and deep learning techniques (e.g. Convolutional Neural Nets).
完成時間為 5 小時
12 個視頻 （總計 115 分鐘）, 5 個閱讀材料, 3 個測驗
完成時間為 5 小時
Language modeling and sequence tagging
In this module we will treat texts as sequences of words. You will learn how to predict next words given some previous words. This task is called language modeling and it is used for suggests in search, machine translation, chat-bots, etc. Also you will learn how to predict a sequence of tags for a sequence of words. It could be used to determine part-of-speech tags, named entities or any other tags, e.g. ORIG and DEST in "flights from Moscow to Zurich" query. We will cover methods based on probabilistic graphical models and deep learning.
完成時間為 5 小時
8 個視頻 （總計 84 分鐘）, 2 個閱讀材料, 3 個測驗
完成時間為 5 小時
Vector Space Models of Semantics
This module is devoted to a higher abstraction for texts: we will learn vectors that represent meanings. First, we will discuss traditional models of distributional semantics. They are based on a very intuitive idea: "you shall know the word by the company it keeps". Second, we will cover modern tools for word and sentence embeddings, such as word2vec, FastText, StarSpace, etc. Finally, we will discuss how to embed the whole documents with topic models and how these models can be used for search and data exploration.
完成時間為 5 小時
8 個視頻 （總計 83 分鐘）
完成時間為 5 小時
Sequence to sequence tasks
Nearly any task in NLP can be formulates as a sequence to sequence task: machine translation, summarization, question answering, and many more. In this module we will learn a general encoder-decoder-attention architecture that can be used to solve them. We will cover machine translation in more details and you will see how attention technique resembles word alignment task in traditional pipeline.
完成時間為 5 小時
9 個視頻 （總計 98 分鐘）
審閱
來自自然语言处理的熱門評論
由 AS 提供2020年5月18日
The course had an advanced content and was taught at a good pace. Though there are some concepts which were not elaborated and needed to be understood from different sources.
由 AJ 提供2020年4月23日
This is basic to advanced level course. you should have significant amount of knowledge in the field, if not you have to do additional research .Over all good course.
由 DW 提供2020年8月2日
It's a comprehensive course on NLP. The instructors clearly explain both the traditional/classical approaches and modern approaches such as neural networks in NLP.
由 AS 提供2019年12月17日
One of the best courses I took from coursera. Good mathematical knowledge, resources provided are related to current research. Assignments are more than expected.
關於 高级机器学习 專項課程
This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.
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